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Internet of Things: Geographical Routing based on healthcare centers vicinity for mobile smart tourism destination
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Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Internet of Things: Geographical Routing based on healthcare centers vicinity for mobile smart tourism destination Wesam Almobaideena,⁎, Rand Krayshana, Mamoon Allanb, Maha Saadeha a b
King Abdullah II School of Information Technology, The University of Jordan, Amman 11942, Jordan Faculty of Archaeology and Tourism, The University of Jordan, Amman 11942, Jordan
A R T I C L E I N F O
A B S T R A C T
Keywords: Internet of Things Geographical Routing Mobile smart tourism destination Healthcare tourism Tourists
Tourists with chronic diseases, being afraid of unexpected emergency situation, might be less motivated to travel to remote destinations due to their health concerns, particularly while on the move. Smartphone, sensor devices, cellular communication, and the Internet of Things (IoT) are recent technologies that provide new mobile location based services. Such services could help mobile tourists, i.e. tourist who is currently moving between various sites, to get innovative set of services which make their trip easier and safer. Designing efficient solutions that help tourists with chronic diseases to find an appropriate geographical route from a source to a tourism destination through a transportation network is crucial to healthcare systems. This paper proposes a new approach in generating geographical routes that are best served with close medical centers. Our approach, called Geographical Routing for Mobile Tourist (GRMT), selects a route that is best served with medical centers, and goes through the path that is as shortest as possible in regards with the distance. We have modeled and simulated the proposed approach using simulation program. Simulated experimental results show that GRMT allows tourists to select geographical routes that are served with the highest number of close medical centers although the selected route is slightly longer in distance.
1. Introduction Recently, pertinent tourism literature pays much attention to quality tourism experience which is considered as the core of tourism industry (Tussyadiah, 2014). Thus, implementation of Information and Communication Technologies (ICTs) and its applications in tourism industry could enhance different tourism experiences (Chung et al., 2015). Likewise, it is acknowledged that new information technologies have fundamentally changed the nature and scope of tourism industry (Ho and Lee, 2007). More specifically, new technologies, quick changes in the different business environments, industry structures, and tourists' needs and requirements continue to challenge tourism destination in radical ways (Gretzel et al., 2000). While tourism industry is fragmented and information-intensive; the information-structure produced by the ICTs has the ability to integrate all aspects and segments of such industry (Buhalis, 2003). This is supported by the fact that information quality plays a vital role in tourists' decision-making processes (Wen, 2009). Difficulties and complications in the modern transportation networks and human's inefficient route finding methods result in a large amount of excessive travel. The problem of finding the best path is an
important task in many networks and transportation related analysis (Zhan and Noon, 1998). It is well accepted that people with special needs represent a significant tourism niche market which is growing rapidly (Allan, 2015). Thus, it is estimated that special needs market share is worth more than 117 billion USD annually (Bizjak et al., 2011). One of the main segments of the people with special needs market is the aging people. According to the UN statistics, the number of people in the world aged 60 years or above will grow by 56% between 2015 and 2030, from 901 million to 1.4 billion (The United Nations, 2015). Another important segment of with special needs market is people with disabilities, as there are more than 650 million people with disabilities worldwide (Disabled World, TM, 2013). However, travel constraints are considered as the main challenges facing tourists with special needs (Page and Hall, 2003). By reviewing the literature, it is noted that the most often cited constraints to travel are a lack of time, financial concerns, physical and emotional costs, health status (objective and self-reported), perceived disability, age, security considerations, lack of information, family approval, and family responsibilities (Chen and Wu, 2009). These
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Corresponding author. E-mail addresses: [email protected] (W. Almobaideen), [email protected] (R. Krayshan), [email protected] (M. Allan), [email protected] (M. Saadeh). http://dx.doi.org/10.1016/j.techfore.2017.04.016 Received 30 March 2016; Received in revised form 18 April 2017; Accepted 20 April 2017 0040-1625/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Almobaideen, W., Technological Forecasting & Social Change (2017), http://dx.doi.org/10.1016/j.techfore.2017.04.016
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particular tourists based on preference messages sent from his/her smartphone. The third attribute is immediacy; GRMT provides on righttime services based on tourist relevant information, targeted HCCs specialty and on being online for timely information delivery. The fourth attribute is location-based; GRMT for tourists' mHealth assumes the provision of context-specific information services using GPS. The fifth attribute is interactivity; GRMT allows a tourist to specify his/her preferences, to determine his destination, and have relevant information from the nearest HCC when an emergency even occurs. The last attribute is mobility; GRMT allows a tourist to have mHealth services while on the move. The rest of this paper is organized as follows. Section 2 provides a background on previous related work. Section 3 briefly discusses our proposed approach GRMT. The experimental evaluation results along with brief discussion are presented in Section 4, and finally Section 5 concludes the paper.
constraints have been classified into three factors ordered according to their importance as perceived risks, time constraints, and personal reasons. “Worry about healthcare at destination” is listed as the second important item of the first and most important constraint factor, i.e. perceived risks (Chen and Wu, 2009). Overall, ICTs could help to tackle different types of travel constraints and facilitate tourism experiences specifically for tourists who have health concerns during their tourism experiences, especially while moving between various sites in a territory which they are not familiar with its transportation networks and the locations of healthcare centers. Smart tourism involves multiple components that are supported by ICT, for example, smart destinations which refers to the integration of ICTs into physical infrastructure. Barcelona, for instance offers travelers interactive bus shelters that provide touristic information and bus arrival times as well as USB ports for charging mobile devices (Gretzel et al., 2015). Always Best Connected (ABC) at any point in time is one of the mobile tourists' most important needs (Gustafsson and Jonsson, 2003). IoT and the ABC concept allow tourists to have connectivity which enables them to have access to applications using the devices and network technologies that best fit their needs. This is considered critical especially for tourists with health concerns due to their chronic diseases that could cause an emergency condition to which a quick response operation has to be done (Yang et al., 2013). In this paper, we propose a new approach, called GRMT, to select an appropriate route for tourists with special health conditions to move from their source location to the tourism destination through transportation networks. This approach finds the shortest path that is bestserved with healthcare centers. The proposed approach can be adopted and implemented as an extension of the services provided by many map applications such as Google Maps, Yahoo Maps, Flash Earth, and EarthBrowse. In other words, GRMT is not intended as a replacement of such wellestablished and widely used map applications. Instead, it is an added feature for such popular applications, especially those provided over mobile devices in order to target such a large population of mobile travelers and tourists who are looking for eHealthCare services which GRMT provides. Assuming that the basic mobile services provided by such applications are stable, the competition between them remains in providing distinguished services that target wide population and here where solutions provided by GRMT come into consideration. The contribution of this study is summarized as follows:
2. Literature review and related work Several developments have revolutionized the nature and scope of the tourism industry on the global context including the usage of computer reservation systems in the 1970s; evolving the internet in 1990s and the continuing development of the ICTs (Sheldon, 1997). It is obvious that tourism industry is one of the main users of the internet (Gretzel et al., 2000). The internet is a significant marketing tool to better communicate with different consumers in tourism sector due to its interactive ability (Li and Wang, 2011). Many ICT related research efforts and applications developments have focused on tourism, as it is one of the fastest and largest growing sectors in the world. They have mainly concentrated on providing tourists with services to make their trips easier, safer and more comfortable. In medical tourism, most of the efforts have laid eyes on developing systems that serve patients whom travel to receive medical treatments, while few of them have studied the needs of patient-tourists that are prone to have medical emergencies during their trips (Chen and Wu, 2009; Fröber et al., 2006). Increasing the number of people with special health conditions in our society has raised the awareness toward providing free barriers services and facilities. In Stiller et al. (2006), a handheld/server based assistance system is proposed to undertake different tourism activities for tourists with physical limitations. They have included a scalable and easy to learn user interface design, a routing algorithm that does not only search for the shortest way, but also the way with fewer obstacles. So users ensure going through the most comfortable way to reach their destinations. They have also taken into consideration the possibility to add medical observations as well as an emergency system. This possibility is what GRMT proposes and evaluates in terms of routing a traveler through the path that keeps him/her as close as possible to medical health care centers. Basili et al. (2014) have introduced ‘Tourists Assistance System (TAS)’ that takes the safety of patient travelers and their health concerns into consideration. The system consists of four main components; Internet to allow tourists to be connected anytime and anywhere, a handheld device, control center, and server and database. The handheld device represents a personal digital assistant (PDA), which is connected through the Internet to the control center where the medical information is monitored and contains a human operator who can send out help in case of emergency. GRMT is similarly a TAS that monitors the health status of a tourist and guides him/her to the nearest medical center. Wireless Sensor Network (WSN) is the most used technology in patient monitoring and collecting physiological information and vital signs specifically in the form of a WBSN. In Umamaheswari and Priya (2013), a Vehicular Ad-hoc NETwork (VANET) and WBSN based healthcare monitoring system is proposed. The system's main objective is to monitor the patients' health continuously and while in the move by
1- A new approach, called GRMT, of finding a geographical route that is best served with healthcare centers is proposed. This approach serves tourists for being assured to be rapidly routed to a near healthcare center once an emergency condition arises. 2- An architecture of IoT that connects a mobile tourist's Wearable/ Wireless Body Sensor Network (WBSN), a smartphone, a cellular network, healthcare centers, and other components of the proposed GRMT approach is introduced. 3- An analytical model that describes the way of differentiating between various available routes is presented. In this model, healthcare centers are assumed to vary in terms of offered services. Specialized healthcare centers provide medical services related to the tourists medical conditions. On the other hand, general healthcare centers offer general medical treatment that could help in classification and initial treatment of an emergency case. In Akter et al. (2013) several unique attributes of mHealth have been specified. While designing GRMT, we have taken into consideration satisfying these attributes as much as possible. The first attribute is accessibility; GRMT as a mHealth for mobile tourists provides ubiquitous and unison accessibility for HCCs at any-time and anywhere. The second attribute is personalized solutions; GRMT provides mHealth for individualized solutions taking into account the specific needs of 2
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no single study exists which has proposed and evaluated the approach proposed by GRMT. A solution such as presented in this paper would have potential social impact for tourists who need special healthcare and would like to undertake different tourism experiences. This is because it provides a service which allows them to overcome perceived leisure constraints. Specifically, a traveler is assured that when any health problem emerges while moving between tourism sites in a foreign destination, potential tourist would be routed through the fastest path toward the nearest healthcare center that will have the required information about their health status. Consequently, specialized medical treatment would be provided to such patients as soon as possible and by professional staff. This in turn would encourage such tourism market segment to have free barriers and accessible tourism experience. However, it is worth mentioning that GRMT approach supports the current and main notion of the United Nations World Tourism Organization (UNWTO) entitled as “Tourism for All” which seeks promoting universal accessibility.
using a VANET which is an ad-hoc network that is integrated with a vehicle system to connect it with the internet and other nearby vehicles. WBSN is a set of health related sensors that are placed on a special suit so they become wearable and collect the patients' Personal Health Information (PHI). Based on PHI, the medical staff can recognize the condition of the patient. In case of any emergency situations, the information will be forwarded to an Ambulance. Thus the patient can get complete medical care and monitoring of their health condition from any place. GRMT uses the concept of PHI that can be collected by a WSN attached to the tourist body or environment. A prototype for placing heterogeneous sensor nodes on human body for health monitoring is presented in Javaid et al. (2013). MobileHealthcare is the new technology or providing healthcare services using the two new technologies; WSN and IoT. Many applications and studies have used the concept of IoT in health related systems for patient's surveillance and monitoring. Istepanian et al. (2011) have introduced a new concept of integrating the functionalities of mobile-healthcare and IoT in m-IoT. However, its concept is related to the new (4G Health), a future innovative health services based on the IPv6 connectivity. They tested the system using thermal sensor nodes, for measuring body temperature as an example for healthcare applications. Cloud computing and IoT concept together constitute a good combination for sensor data processing and management in an efficient way. Doukas and Maglogiannis (2012) present a platform for patientrelated sensor data management, in which IoT allows the sensor devices to communicate directly with the cloud applications. Whereas a routing protocol called Movement Aided Energy Balance (MAEB), a prototype of the IoT healthcare systems has been introduced by Su et al. (2013). More specifically, it is an ambulatory system that uses WBAN and a broader telemedicine system, where WBAN collects patient's physiological information and vital signs and transmits them to the Internet through the Access Gateway (GA), which in turn transmits them to the telemedicine system. Coordinators, using neighbor's discovery procedure, get information about its neighbor's movement and energy, and then using MAEB forwarding the most suitable coordinator to forward data will be selected. The study compares the current proposed approach with a geographical routing approach that is proposed by Almobaideen et al. (2015). We will refer to this approach as Geographical Routing based on User preferences (GRUP). A combined criterion was used to provide a service that suggests geographical routes based on user preferences in terms of public transportation and services. Public transport media preferences included bus, train, metro, and walking … etc. on the other hand, GRUP is also based on service related preferences, which include the best wireless network connection quality such as General Packet Radio Service (GPRS), 3G, and 4G … etc. along the available paths. The aim was to provide continuous and best quality of internet connection for tourists or travelers in general, while they are moving to their destination. GRUP has been modeled, simulated via C++ program, and then evaluated to prove that its combined criteria outperform others in selecting geographical routes when considering the preferred public transport media and the higher quality network connections. A summary of the related previous studies is presented in Table 1. Reflection to explore the healthcare systems; medical tourism systems, patients monitoring systems and the concept of IoT in health-related technologies, this study has found the necessity for developing a new approach for patient-tourists with chronic diseases that finds the best route that fits their health status and medical conditions. In this paper we have focused on patient-tourists, who suffer from chronic diseases and cases that require constant monitoring. Collecting patient's health information in one location and make them accessible for services providers and patients is considered a major challenge. Despite the fact that many studies have investigated the problem of monitoring patient's physiological and vital signs in healthcare systems,
3. Geographical Routing for Mobile Tourists (GRMT) In this section we present the main components of GRMT starting with the transportation network model in Section 3.1. The general architecture is illustrated in Section 3.2. The main phases of the GRMT protocol are described in Section 3.3. Section 3.4 sheds light on the simulation environment assumed to evaluate and compare the protocols under consideration. 3.1. Network model GRMT finds a geographical route in a public transportation network, e.g. in Fig. 1, which is represented by a graph (Aldous and Wilson, 2000) as follows: 1 The transportation network, of the city where the tourist travels, is represented as a graph G with a set of nodes N, edges E, and healthcare centers HCC. 2 Each n ϵ N is a transportation station (intersection). 3 Each e ϵ E is an edge that represents a road segment which connects two transportation stations. 4 Each SHCC ϵ HCC is a specialized healthcare center relative to the mobile tourist's health conditions, and each GHCC ϵ HCC is a healthcare center that provides general medical services. 5 Each road segment represents a transport line, for example Bus, and has its own characteristics such as duration time, and its departure and termination stations. 6 Each w ϵ W represents a road segment weight that is calculated based on the length of the route and the vicinity of healthcare centers, as explained in Section 3.3. 7 Each tourism site has a station close to it. 3.2. General architecture of GRMT The architecture of GRMT is illustrated in Fig. 2. The tourist smartphone device is assumed to be GPS enabled and allows the tourist to send a preference message and get back a map that shows the resulted geographical route leading towards the intended destination. The following details describe the content of the preference message which is sent from the user smartphone and contains the following fields: 1 Time: The time when the message is created. 2 Location: The tourist's current location read by a GPS device and represents the source location. 3 Destination: The user intended tourism destination. 4 Personal Health Information (PHI): A specification of a tourist's health 3
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Table 1 Summary of the related previous work. Reference
Study Description
Purpose
Results
Stiller et al. (2006)
A handheld system that routes tourists with physical limitations through a way with fewer obstacles.
Fröber et al. (2006)
A design process for a Tourist Assistance System (TAS) that provides tourists with special health conditions with some services. VANET and WBSN are used together for collecting PHI and monitoring patients' physiological and vital signs. A prototype of health monitoring using WBSN, using two types of communication; multi and single hop. Integration of the IoT concept with mobilehealthcare in m-IoT. A platform based on Cloud Computing for management of mobile and wearable healthcare sensors data. A prototype of the IoT healthcare systems. A routing protocol called Movement Aided Energy Balance (MAEB). Geographical routing protocol (GRUP). Selects the route based on user preferences.
Allow people with disabilities and health problems to participate in holidays by taking their needs and conditions into account. Monitor patient-tourists signs and take an action in case of any emergency.
The system was developed to consider special health conditions. It was tested and has shown a high level of acceptance with some further hints to improve the usability. Developed and tested a strategy for determining the requirements and deploying design criteria for an assistance system. Based on modern healthcare systems, an application of the two technologies WBSN and VANET was developed and tested. The results have shown that the proposed routing algorithm has less energy consumption and more reliable as compared to multi-hop communication. Tests and results have shown the successful functionality of the system in real time environments Allowed direct communication of the sensor devices with the Cloud application. Highly scalable in the context of data stored, users and sensors supported. In comparison with three other approaches, MAEB has shown better performance in terms of energy consumption, packet delivery ratio, and throughput. GRUP allows mobile users to select geographical routes according to their preferred transportation media. And in comparison with Dijkstra's shortest path and other route finding approaches in term of the transmission rate of available network connection, GRUP better satisfies mobile users' needs.
Umamaheswari and Priya (2013) Javaid et al. (2013)
Istepanian et al. (2011) Doukas and Maglogiannis (2012) Su et al. (2013)
Almobaideen et al. (2015)
Monitor the patients' health continuously and to collect the patients' Personal Health Information (PHI) Patient vital signs monitoring.
Patient surveillance and monitoring. Allow the sensor devices to communicate directly with the cloud applications and managing patient-related data on the cloud. Ambulatory system that collects patient's physiological information and vital signs. Provide a path based on two user preferences; transportation media and network connectivity.
information that exists in various databases. It is connected to the geographical maps and transportation DB, tourism information DB, and the healthcare DB. These databases are described as follows:
conditions such as their diseases and needed medicines, sensitivity to other certain medicines, and any other related information. Each tourist, as a potential IoT object, is assumed to be equipped with small wireless sensors that should be placed in various parts of the body in a form of a WBSN (Yoo et al., 2009; Jassim and Almobaideen, 2013). These sensors collect various information that could represent health performance measures such as blood pressure, heart beats, or any other medical measures. This information is then sent to the tourist's smartphone device which possibly aggregates this information and sends it through a cellular communication network to the Tourists Serving Unit (TSU). The TSU is a main component which exits on the cloud-centric IoT and conducts the required computations and analyses based on the
1 The geographical and transportation database: Contains all the information of transportation network topology including the stations, road segments, driving speed over each segment, and other related records. 2 The Healthcare database: Contains information about all healthcare centers including their geographical location, whether they are specialized or general healthcare centers, and medical treatment they are specialized in. 3 The tourism location database: Stores information of the name, location, opening hours of different attractions or tourism destina-
Fig. 1. An example of the transportation network topology.
4
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Fig. 2. Geographical Routing for Mobile Tourist Architecture.
illustrated as follows:
tion.
Phase I: data aggregation The tourist's smartphone receives Personal Healthcare Information (PHI) message from the sink node of a Wireless Body Sensor Network (WBSN) (Jassim and Almobaideen, 2013). It then aggregates this information and encapsulates it into a Tourist Preference Message (TPM) accompanied with the other needed information such as the current time, the location of the targeted destination, and the current tourist's location. The TPM is then sent to the TSU through a cellular connection. Phase II: data processing and analysis After receiving the TPM, the received information will be retrieved and analyzed to check whether there is a path that has been previously generated from the current location to the target destination. If there is no path, road segments weights are calculated and the route with the highest weight is selected. Phase III: route generation In this phase and based on the aggregated information, a route that is best-served with medical centers is generated. A route consists of multiple road segments that connect the stations a route goes through. We refer to the set of N road segments that constitute a path as RS1, RS2 …RSN. This phase consists of multiple steps.
The TSU consists of the following modules: 1 The Message Reception Module: This Module receives the user preference message, de-capsulates its contents, and forwards it to the preprocessing module, should it be the first query for a particular source and destination. In case of being a message for a previously known source and destination it is forwarded to the cache module. 2 The Preprocessing Module: This module analyzes the information extracted from the tourist's preference message and consults the tourism locations database and the geographical database for transportation routes that are available between a tourist current location and the intended tourism destination sites. 3 The Route generation: Among the paths that could been found, the route generation module selects the best one after consulting the healthcare database for near HCCs, the location of such close HCCs, whether they are GHCCs or SHCCs, and does a SHCC specialty relates to the health problems of the tourist according to what has been mentioned in the preference message. The resulted route is then sent to the cache module and the route offering module. 4 The Cache Module: Keeps a cache of recent resulted routes for a certain source and destination and specified preferences. In case of receiving the same message again, the cache module can provide a path immediately without recalculating and generating the route again in order to increase the system performance. 5 The Route Offering Module: This module formats the resulted route in a way suitable to be displayed on the user's smartphone.
The first step starts with checking the vicinity of HCCs to each road segment that is part of a route to the destination. Vicinity in our model has two levels, specifically very close and close. We have set the very close vicinity distance to be 300 meters. We search also within a greater distance represents the close level of vicinity which we have set to 600 meters, see Table 1. It also shows that we have assigned different weights to various types of healthcare centers based on their level of vicinity and whether they are specialized or general.
3.3. GRMT phases and analytical description GRMT works in three main phases; data aggregation, data processing, and route generation. The details of each one of these steps are 5
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introduced in (Almobaideen et al., 2015) as mentioned in the related work section. The same simulator that has been used to simulate GRUP was amended with extra modules that implement the steps of GRMT. The simulator contains a module that builds a random transportation network topology, see Subsection 3.1. This topology consists of transportation stations that are assumed to be scattered around the terrain area that represents a city or a town. A road segment connects two transportation stations and has its own characteristics such as the duration time, network coverage quality, and its departure and terminal stations. In the amended simulator, HCCs are then randomly distributed over the specified geographical area covered by the transportation network. Furthermore, another part of the simulation program has been modified to simulate the architecture described in Subsection 3.2. This part simulates a tourist preference message that is received, preprocessed, and afterward a route is generated based on the calculations of weights described in Subsection 3.3. The final step applies Dijkstra algorithm, which is a well-known algorithm that finds the best path between two end points (Cormen et al., 2001), in order to look for the route with the best weight toward the suggested tourism destination. Table 2 presents a summary of the parameters that controlled the simulation experiments that have been conducted to evaluate GRMT. For GRUP and shortest route approaches we have based this evaluation over the same analytical model and parameters values described in Almobaideen et al. (2015).
N . SHi, VC × W . SHVC + N . SHi, C × W . SHC WH . RSi =
+ N . GHi, VC × W . GHVC + N . GHi, C × W . GHC TN . HCC
(1)
Where WH . RSi is the calculated weight of HCCs of road segment i of the whole route to the tourism destination. This weight is calculated by adding the values of multiplying the number of specialized HCCs and general HCCs by their corresponding weight whether they are close and very close divided by the total number of HCCs in the whole geographical area (TN . HCC). For example N . SHi , VC represents the number of specialized HCCs that are very close to road segment i, and W . SHVC is the weigh associated with this type of HCCs. Another example is the term that multiplies the number of general HCCs that are just close to road segment i,N . GHi , C, by its associated weight, W . GHC. Next, and since we prefer that the HCCs are distributed evenly along the road segment instead of cluttering in a small part of it, we measure that distribution. This is done logically by dividing the whole road segment into multiple equal fragments of length D. This would result of NF . RSi fragments of road segment i, and accordingly we use Eq. (2):
Dis. RSi =
NF . RSi, HCC NF . RSi
(2)
Where Dis . RSi is a measure of the even distribution of HCCs over road segment i, and NF . RSi , HCC is the number of fragments of road segment i given that each one of these fragments include at least one HCC. Given that we have measured the weight of HCCs existence around the road segment i, and measured how even they are distributed along that road segment, we can now calculate a combined weight CW . RSi for this road segment as in Eq. (3):
CW . RSi = (α × Dis. RSi ) + (1 − α ) × WH . RSi
4. Evaluation results and discussion In order to evaluate GRMT and compare it to GRUP and Dijkstra's shortest route we have created five different topologies, see Subsection 3.1, using different seeds for the random number generator of the simulator. In each of these topologies, fifteen different sources and destinations have been selected randomly and the average of the (75) simulation runs has been calculated as the final result presented in all the figures of this section. These experimental results are discussed next. In GRMT, as it mainly focuses on finding a route that is best served with HCCs, the total number of HCCs that are close to the selected route is expected to be more than both GRUP and Dijkstra's shortest route. Fig. 3 confirms that GRMT results in a larger average number of specialized and general HCCs that are close to the resulted route than the other two approaches. This is because GRMT takes the number of HCCs into account when selecting the path, which ensures, for tourists with chronic diseases, to be resuscitated in less time in case of any emergency. In this figure, one can notice that as the terrain area increases the number of found HCCs decreases since the same number of HCCs is distributed over a larger area. GRMT is able to perform better than GRUP and the Shortest_route approaches for all values of terrain
(3)
Where α is a weight factor that gives more importance to a certain term in Eq. (3) over the other. Setting α to a value greater than 0.5 makes (Dis . RSi) more effective in the final combined weight and vice versa. After calculating the combined weight of each road segment, we calculate the HCCs weight of the whole route (WH.route), which is composed of these N road segments as shown in Eq. (4): N
WH . Route =
∑i =1 CW . RSi (4)
N
In order to select the best route to the tourism destination, we have to take another factor, other than the HCCs, into consideration since a tourist needs to go through the shortest route possible as well as being served with the largest number of HCCs. To achieve this target we calculate a weight of each road segment in regards to its length or the distance a tourist has to go through that segment as in Eq. (5):
Distance. RSi =
ED. RSi Field . Diameter
Table 2 Values of the parameters that controlled the experiments conducted using the developed simulator.
(5)
Where ED . RSi is the Euclidian distance between the two stations that constitute road segment i. and the Field. Diameter represents the largest distance in the geographical area where the tourist is supposed to move. Finally we find the FW.route which divides the weight of HCCs of that route (WH . Route) by the sum of distance weights of all road segments (Distance . RSi) as shown in Eq. (6):
FW . route =
WH . Route N ∑i =1 Distance .
RSi
(6)
3.4. GRMT simulation environment GRMT has been simulated and compared with GRUP that has been 6
Parameter
Value
Number of stations Number of healthcare centers Alpha Very close vicinity Close vicinity Specialized HCC within very close vicinity weight Specialized HCC within close vicinity weight General HCC within very close vicinity weight General HCC within close vicinity weight Fragments granularity Poor network connection weight Moderate network connection weight Good network connection weight
35 50, 35, and 20 0.4 300 m 600 m 1 0.9 0.85 0.7 300 m 0.3 0.6 0.9
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served with larger number of HCCs even though this comes at the expense of slight increase of the path length. Given the context of a tourism experience, a tourist would not mind being routed via slightly longer path to reach a tourism destination for the benefit of being always close to HCC that could provide the required treatment in case it is needed. Consequently, this longer route could give a tourist the opportunity of seeing locations that were not listed in the tour itinerary. This could enrich his/her tourism experience as well as convenience. Fig. 7 shows an evaluation of GRMT, GRUP and Dijkstra's shortest route with regards to the average ratio of the wireless network connection's quality that covers the selected route. The quality of wireless network connection over different road segments has been assumed to vary into three classes, poor, moderate, and good. Each one of these classes has been assigned a weight as 0.3, 0.6, and 0.9 respectively. The results of this experiment show that GRMT selects a route that is covered with slightly less quality of wireless network connections compared with the other two approaches and specially GRUP. This is due to the fact that GRUP focuses on selecting a path that is covered with a good quality network connection without considering the number and distribution of HCCs along that route as GRMT does. GRMT assumes that a tourist is always connected to the IoT in order to rely on the medical information related to his/her current health status to the nearest HCC. Based upon the related literature of this paper, it is noted that an increasing number of solutions rely on the availability of ABC channels to the internet for various parties of the tourism industry, e.g. the tourists, transportation media, tourism sits, health care centers. This assumption is valid and comes as a fact in the era of IoT. Accordingly, the metric presented in Fig. 7 is a vital one and prefers GRMT over its rival approaches. In order to study the effect of HCCs density in the terrain area we have changed the number of distributed HCCs in a 7.5 km × 7.5 km terrain. Various numbers of HCCs ranging from 20, 35, and up to 50 HCCs have been distributed randomly all over the terrain area. Results shown in Fig. 8 emphasize that GRMT always selects the route that is best served with HCCs regardless of the density of their distribution. This indicates that GRMT is able to serve tourist even when the area of the tourist attraction destination is different from what has been assumed in this paper. This in turn would increase the confidence of a tourist, who needs special health monitoring, in GRMT as an assistant that keeps an eye on his/her health status, communicate such information with close HCCs, and guide him/her through a path that is best served by such HCCs regardless of their density over the area of the destination. Such a confidence would make a big difference in increasing the number of active travelers from the sample targeted by this research.
Fig. 3. Average Number of, close and very close, Specialized and General HCCs.
areas. GRMT offers the path to a tourism destination that is served by the largest possible number of HCCs in addition of being as short as possible. This would encourage tourists with health concerns while moving between tourism sites to go forward and be active travelers. GRMT selects the route that is best served with HCCs, taking into account the vicinity of these HCCs and their specialty. Fig. 4 shows a comparison between the three approaches in regard with the average number of two HCC types, i.e. specialized as in (a), and general as in (b), that are very close, i.e. located within 300 meters, as the terrain area increases. This figure shows that GRMT routes a tourist through a path that passes very close to many more healthcare centers than GRUP and Dijkstra's shortest. This achievement is very crucial for tourists with chronic diseases as it allows them to get as fast as possible to those very close HCCs which could provide basic health treatment or specialized one in case of emergency. Take into consideration that GRMT allows the details of the health status of the patient tourist to be collected and sent beforehand to the nearest SHCCs so that in an emergency case a specialized doctor who already knows what the case is, will be ready to treat this patient. Fig. 5 shows the average number of the two types of HCCs, specialized in (a) and general in (b), which are close to the selected route, i.e. within 600 meters, as the terrain area increases. This figure also shows that GRMT routes a tourist through a better route that is served by a larger number of HCCs than the routes selected by GRUP and Dijkstra's shortest route. This gives tourists with chronic diseases an assurance of finding a close HCC in case a very close one is busy or currently un-available for one reason or another. This would encourage tourists to visit more attractions and to spend more time in the locations they are interested in. Even though we have taken the distance of the route into account, the selected path in GRMT is longer in distance and requires slightly more time duration to reach than GRUP and Dijkstra's shortest selected routes as shown in Fig. 6. This is because GRMT prefers routes that are
Fig. 4. (a) Average Number of Specialized HCC within 300. (b) Average Number of General HCC within 300
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Fig. 5. (a) Average Number of Specialized HCC within 600. (b) Average Number of General HCC within 600
5. Conclusion Offering free barriers tourism experiences for tourists with special needs is vital for attracting such tourists and enhancing the tourism supply. Therefore, the main purpose of this study was to propose a route selection approach that is based on the vicinity of the medical centers as well as the distance of that route. However, it could be concluded that the proposed approach GRMT can contribute in improving mHealthcare system and enhancing the overall tourism experience. Simulation experiments have been conducted to evaluate GRMT in comparison to GRUP that is based on user preferences in regards to both public transportation means and networks coverage. The study findings indicate that GRMT allows tourists with health problems to select a route towards their tourism destination that is best served with medical centers. Also providing a route with acceptable quality network coverage, even though less than that of GRUP could enhance the satisfaction level for mobile users. Based upon the fact that there have been several lacunae in the etourism literature in the context of ICT in the tourism context; it is reasonable to suggest that this research could extend our knowledge for implementation of IOT in the context of smart mobile tourism. Additionally, the proposed approach could improve the quality of tourism experience and decrease the health concerns for patient tourists. In terms of directions for future research, further work could be done to explore the suggested approach GRMT in different tourism contexts, and settings, e.g. taking the network coverage into consideration. Tourism managers and policy makers could use such approach to provide accessible services for tourists with special needs and attract this important tourism segment. Moreover, such applications could
Fig. 7. Average quality of network connection coverage over the selected route.
Fig. 8. Average number of close and very close HCCs to the selected route as the density of their distribution increases.
Fig. 6. (a) Average Duration of traveling along the selected path. (b) Average Distance of the selected path.
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154–161. Javaid, N., Abbas, Z., Fareed, M.S., Khan, Z.A., Alrajeh, N., 2013. M-ATTEMPT: a new energy-efficient routing protocol for wireless body area sensor networks. Procedia Comput. Sci. 19, 224–231. Li, X., Wang, Y., 2011. Measuring the effectiveness of US official state tourism websites. J. Vacat. Mark. 17 (4), 287–302. Page, S.J., Hall, M.C., 2003. Managing Urban Tourism. Person Education Limited, Essex. Richards, V., Pritchard, A., Morgan, N., 2010. Envisioning tourism and visual impairment. Ann. Tour. Res. 37 (4), 1097–1116. Sheldon, P.J., 1997. Tourism information technology. In: International Handbook on the Economics of Tourism, (ISBN-13: 978-0851991818). Stiller, C., Roß, F., Witte, H., 2006. A handheld/server based assistance system for tourists with special needs. In: ACST'06 Proceedings of the 2nd IASTED International Conference on Advances in Computer Science and Technology, pp. 263–268. Su, H., Wang, Z., An, S., 2013. MAEB: routing protocol for IoT healthcare. Adv. Internet Things 03 (02), 8–15. http://dx.doi.org/10.4236/ait.2013.32a002. The United Nations, 2015. World Population Ageing. Department of Economic and Social Affairs. Tussyadiah, I.P., 2014. Toward a theoretical foundation for experience design in tourism. J. Travel Res. 53 (5), 543–564. Umamaheswari, S., Priya, R.M., 2013. An efficient healthcare monitoring system in vehicular ad hoc networks. Int. J. Comput. Appl. 78 (7), 45–49. Wen, I., 2009. Factors affecting the online travel buying decision: a review. Int. J. Contemp. Hosp. Manag. 21 (6), 752–765. Yang, L., Yang, S.H., Plotnick, L., 2013. How the internet of things technology enhances emergency response operations. Technol. Forecast. Soc. Chang. 80, 1854–1867. Yoo, H.J., Cho, N., Yoo, J., 2009. Low energy wearable body-sensor-network. In: Engineering in Medicine and Biology Society. EMBC 2009. Annual International Conference of the IEEE, Minneapolis, MN, 2009, pp. 3209–3212. http://dx.doi.org/ 10.1109/IEMBS.2009.5333156. Zhan, F.B., Noon, C.E., 1998. Shortest path algorithms: an evaluation using real road networks. Transp. Sci. 32 (1), 65–73.
enhance the ethical practices for tourism and promote the social responsibility for tourism industry as it represents a basic human right and should be accessible for all individuals (Richards et al., 2010). The proposed routing approach GRMT is not considered as an alternative method for any routing approaches, neither of Google Maps nor any other geographic routing applications. However, GRMT could be integrated with these systems and expanded to be used not only in tourism applications but also in our daily lives to get along in urban areas as a mobile eHealthCare assistant. References Akter, S., D'Ambra, J., Ray, P., 2013. Development and validation of an instrument to measure user perceived service quality of mHealth. Inf. Manag. 50 (4), 181–195. Aldous, J., Wilson, R., 2000. Graphs and Applications: An Introductory Approach. Springer. Allan, M., 2015. Accessible tourism in Jordan: travel constrains and motivations. Eur. J. Tour. Res. 10, 109–119. Almobaideen, W., Saadeh, M., Al-Anbaki, N., Zaghloul, R., Aladwan, A., 2015. Geographical route selection based on user public transportation and service preferences. In: 9th International Conference on Next Generation Mobile Applications, Services and Technologies. Cambridge, UK, pp. 144–149. Basili, A., Liguori, W., Palumbo, F., 2014. NFC smart tourist card: combining mobile and contactless technologies towards a smart tourist experience. In: IEEE 23rd International WETICE Conference. IEEE, Parma, pp. 249–254. Bizjak, B., Knezevic, M., Cvetreznik, S., 2011. Attitude change towards guests with disabilities reflections from tourism students. Ann. Tour. Res. 38 (3), 842–857. Buhalis, D., 2003. eTourism: Information Technology for Strategic Tourism Management. Pearson (Financial Times/Prentice Hall), London (ISBN 0582357403). Chen, C., Wu, C., 2009. How motivations, constraints, and demographic factors predict seniors' overseas travel propensity. Asia Pac. Manag. Rev. 14 (3), 301–312. Chung, N., Lee, H., Lee, S.J., Koo, C., 2015. The influence of tourism website on tourists' behavior to determine destination selection: a case study of creative economy in Korea. Technol. Forecast. Soc. Chang. 96, 130–143. Cormen, T.H., Leiserson, C.E., Rivest, R.L., 2001. Dijkstra's Algorithm. Introduction to Algorithms. pp. 595–601. Disabled World (TM), 2013. World facts and statistics on disabilities and disability issues. URL. http://www.disabledworld.com/disability/statistics/#ixzz2HJ698Oj (Accessed on 01 May 2013). Doukas, C., Maglogiannis, I., 2012. Bringing IoT and cloud computing towards pervasive healthcare. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, http://dx.doi.org/10.1109/imis.2012.26. Fröber, U., Lutherdt, S., Stiller, C., Roß, F., Witte, H., Kurtz, P., 2006. The design of a tourist assistance system for several handicapped and elderly. In: Proc. of IEA, . Gretzel, U., Yuan, Y.L., Fesenmaier, D.R., 2000. Preparing for the new economy: advertising strategies and change in destination marketing organizations. J. Travel Res. 39 (2), 146–156. Gretzel, U., Sigala, M., Xiang, Z., Koo, C., 2015. Smart tourism: foundations and developments. Electron. Mark. 25 (3), 179–188. Gustafsson, E., Jonsson, A., 2003. Always best connected. IEEE Wirel. Commun. 10 (1), 49–55. Ho, C.I., Lee, Y.L., 2007. The development of an e-travel service quality scale. Tour. Manag. 28 (6), 1434–1449. Istepanian, R.S., Sungoor, A., Faisal, A., Philip, N., 2011. Internet of m-health Things “mIoT”. In: IET Seminar on In Assisted Living 2011, pp. 1–3. http://dx.doi.org/10.1049/ ic.2011.0036. Jassim, M.A., Almobaideen, W.A., 2013. A segment-based tree traversal algorithm for enhancing data gathering in wireless sensor networks. In: International Conference on Electronics, Signal Processing and Communication Systems, Venice, Italy, pp.
Professor Wesam Almobaideen ([email protected]) has received his Ph.D. in Computer Networks from the University of Bologna, Bologna, Italy, Master degree in Computer Science from the University of Jordan, and B.Sc. degree in Computer Science from Mu'ta University, Karak, Jordan. He is currently a full professor at the Computer Science Department, The University of Jordan. His research interests include: Wireless network, Computer and network security, Routing protocols, Internet of Things, Smart tourism. Rand Krayshan has received her Bachelor's degree from The University of Jordan in Computer Science in 2013, and her Master's degree from The University of Jordan in Computer Science, in 2016. Her research interests include: Wireless networks, Geographical routing protocols, Internet of Things (IoT). Dr Mamoon Allan is currently an associate professor of tourism marketing at the Tourism Department, Faculty of archaeology and Tourism, the University of Jordan. He was working in tourism and hospitality fields in three countries: Australia, Jordan and Libya. He completed his PhD in Tourism Marketing at Edith Cowan University in Perth, Western Australia. Dr Allan conducts international research in the fields of accessible tourism in the Middle East, the behavior and motivations of tourists with disabilities, smart tourism and also e-tourism marketing. Maha Saadeh, is a Ph.D. student in computer science at the University of Jordan. She worked as research and teaching assistant at the computer science department, The University of Jordan from September 2009 to September 2010. Then she received her M.Sc. degree in computer science from the same university in 2011. She has a number of publications in a number of local and international journals and conferences. Her research interests include: wireless networks, network security, and the Internet of Things (IoT).
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Contents lists available at ScienceDirect
Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Internet of Things: Geographical Routing based on healthcare centers vicinity for mobile smart tourism destination Wesam Almobaideena,⁎, Rand Krayshana, Mamoon Allanb, Maha Saadeha a b
King Abdullah II School of Information Technology, The University of Jordan, Amman 11942, Jordan Faculty of Archaeology and Tourism, The University of Jordan, Amman 11942, Jordan
A R T I C L E I N F O
A B S T R A C T
Keywords: Internet of Things Geographical Routing Mobile smart tourism destination Healthcare tourism Tourists
Tourists with chronic diseases, being afraid of unexpected emergency situation, might be less motivated to travel to remote destinations due to their health concerns, particularly while on the move. Smartphone, sensor devices, cellular communication, and the Internet of Things (IoT) are recent technologies that provide new mobile location based services. Such services could help mobile tourists, i.e. tourist who is currently moving between various sites, to get innovative set of services which make their trip easier and safer. Designing efficient solutions that help tourists with chronic diseases to find an appropriate geographical route from a source to a tourism destination through a transportation network is crucial to healthcare systems. This paper proposes a new approach in generating geographical routes that are best served with close medical centers. Our approach, called Geographical Routing for Mobile Tourist (GRMT), selects a route that is best served with medical centers, and goes through the path that is as shortest as possible in regards with the distance. We have modeled and simulated the proposed approach using simulation program. Simulated experimental results show that GRMT allows tourists to select geographical routes that are served with the highest number of close medical centers although the selected route is slightly longer in distance.
1. Introduction Recently, pertinent tourism literature pays much attention to quality tourism experience which is considered as the core of tourism industry (Tussyadiah, 2014). Thus, implementation of Information and Communication Technologies (ICTs) and its applications in tourism industry could enhance different tourism experiences (Chung et al., 2015). Likewise, it is acknowledged that new information technologies have fundamentally changed the nature and scope of tourism industry (Ho and Lee, 2007). More specifically, new technologies, quick changes in the different business environments, industry structures, and tourists' needs and requirements continue to challenge tourism destination in radical ways (Gretzel et al., 2000). While tourism industry is fragmented and information-intensive; the information-structure produced by the ICTs has the ability to integrate all aspects and segments of such industry (Buhalis, 2003). This is supported by the fact that information quality plays a vital role in tourists' decision-making processes (Wen, 2009). Difficulties and complications in the modern transportation networks and human's inefficient route finding methods result in a large amount of excessive travel. The problem of finding the best path is an
important task in many networks and transportation related analysis (Zhan and Noon, 1998). It is well accepted that people with special needs represent a significant tourism niche market which is growing rapidly (Allan, 2015). Thus, it is estimated that special needs market share is worth more than 117 billion USD annually (Bizjak et al., 2011). One of the main segments of the people with special needs market is the aging people. According to the UN statistics, the number of people in the world aged 60 years or above will grow by 56% between 2015 and 2030, from 901 million to 1.4 billion (The United Nations, 2015). Another important segment of with special needs market is people with disabilities, as there are more than 650 million people with disabilities worldwide (Disabled World, TM, 2013). However, travel constraints are considered as the main challenges facing tourists with special needs (Page and Hall, 2003). By reviewing the literature, it is noted that the most often cited constraints to travel are a lack of time, financial concerns, physical and emotional costs, health status (objective and self-reported), perceived disability, age, security considerations, lack of information, family approval, and family responsibilities (Chen and Wu, 2009). These
⁎
Corresponding author. E-mail addresses: [email protected] (W. Almobaideen), [email protected] (R. Krayshan), [email protected] (M. Allan), [email protected] (M. Saadeh). http://dx.doi.org/10.1016/j.techfore.2017.04.016 Received 30 March 2016; Received in revised form 18 April 2017; Accepted 20 April 2017 0040-1625/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Almobaideen, W., Technological Forecasting & Social Change (2017), http://dx.doi.org/10.1016/j.techfore.2017.04.016
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particular tourists based on preference messages sent from his/her smartphone. The third attribute is immediacy; GRMT provides on righttime services based on tourist relevant information, targeted HCCs specialty and on being online for timely information delivery. The fourth attribute is location-based; GRMT for tourists' mHealth assumes the provision of context-specific information services using GPS. The fifth attribute is interactivity; GRMT allows a tourist to specify his/her preferences, to determine his destination, and have relevant information from the nearest HCC when an emergency even occurs. The last attribute is mobility; GRMT allows a tourist to have mHealth services while on the move. The rest of this paper is organized as follows. Section 2 provides a background on previous related work. Section 3 briefly discusses our proposed approach GRMT. The experimental evaluation results along with brief discussion are presented in Section 4, and finally Section 5 concludes the paper.
constraints have been classified into three factors ordered according to their importance as perceived risks, time constraints, and personal reasons. “Worry about healthcare at destination” is listed as the second important item of the first and most important constraint factor, i.e. perceived risks (Chen and Wu, 2009). Overall, ICTs could help to tackle different types of travel constraints and facilitate tourism experiences specifically for tourists who have health concerns during their tourism experiences, especially while moving between various sites in a territory which they are not familiar with its transportation networks and the locations of healthcare centers. Smart tourism involves multiple components that are supported by ICT, for example, smart destinations which refers to the integration of ICTs into physical infrastructure. Barcelona, for instance offers travelers interactive bus shelters that provide touristic information and bus arrival times as well as USB ports for charging mobile devices (Gretzel et al., 2015). Always Best Connected (ABC) at any point in time is one of the mobile tourists' most important needs (Gustafsson and Jonsson, 2003). IoT and the ABC concept allow tourists to have connectivity which enables them to have access to applications using the devices and network technologies that best fit their needs. This is considered critical especially for tourists with health concerns due to their chronic diseases that could cause an emergency condition to which a quick response operation has to be done (Yang et al., 2013). In this paper, we propose a new approach, called GRMT, to select an appropriate route for tourists with special health conditions to move from their source location to the tourism destination through transportation networks. This approach finds the shortest path that is bestserved with healthcare centers. The proposed approach can be adopted and implemented as an extension of the services provided by many map applications such as Google Maps, Yahoo Maps, Flash Earth, and EarthBrowse. In other words, GRMT is not intended as a replacement of such wellestablished and widely used map applications. Instead, it is an added feature for such popular applications, especially those provided over mobile devices in order to target such a large population of mobile travelers and tourists who are looking for eHealthCare services which GRMT provides. Assuming that the basic mobile services provided by such applications are stable, the competition between them remains in providing distinguished services that target wide population and here where solutions provided by GRMT come into consideration. The contribution of this study is summarized as follows:
2. Literature review and related work Several developments have revolutionized the nature and scope of the tourism industry on the global context including the usage of computer reservation systems in the 1970s; evolving the internet in 1990s and the continuing development of the ICTs (Sheldon, 1997). It is obvious that tourism industry is one of the main users of the internet (Gretzel et al., 2000). The internet is a significant marketing tool to better communicate with different consumers in tourism sector due to its interactive ability (Li and Wang, 2011). Many ICT related research efforts and applications developments have focused on tourism, as it is one of the fastest and largest growing sectors in the world. They have mainly concentrated on providing tourists with services to make their trips easier, safer and more comfortable. In medical tourism, most of the efforts have laid eyes on developing systems that serve patients whom travel to receive medical treatments, while few of them have studied the needs of patient-tourists that are prone to have medical emergencies during their trips (Chen and Wu, 2009; Fröber et al., 2006). Increasing the number of people with special health conditions in our society has raised the awareness toward providing free barriers services and facilities. In Stiller et al. (2006), a handheld/server based assistance system is proposed to undertake different tourism activities for tourists with physical limitations. They have included a scalable and easy to learn user interface design, a routing algorithm that does not only search for the shortest way, but also the way with fewer obstacles. So users ensure going through the most comfortable way to reach their destinations. They have also taken into consideration the possibility to add medical observations as well as an emergency system. This possibility is what GRMT proposes and evaluates in terms of routing a traveler through the path that keeps him/her as close as possible to medical health care centers. Basili et al. (2014) have introduced ‘Tourists Assistance System (TAS)’ that takes the safety of patient travelers and their health concerns into consideration. The system consists of four main components; Internet to allow tourists to be connected anytime and anywhere, a handheld device, control center, and server and database. The handheld device represents a personal digital assistant (PDA), which is connected through the Internet to the control center where the medical information is monitored and contains a human operator who can send out help in case of emergency. GRMT is similarly a TAS that monitors the health status of a tourist and guides him/her to the nearest medical center. Wireless Sensor Network (WSN) is the most used technology in patient monitoring and collecting physiological information and vital signs specifically in the form of a WBSN. In Umamaheswari and Priya (2013), a Vehicular Ad-hoc NETwork (VANET) and WBSN based healthcare monitoring system is proposed. The system's main objective is to monitor the patients' health continuously and while in the move by
1- A new approach, called GRMT, of finding a geographical route that is best served with healthcare centers is proposed. This approach serves tourists for being assured to be rapidly routed to a near healthcare center once an emergency condition arises. 2- An architecture of IoT that connects a mobile tourist's Wearable/ Wireless Body Sensor Network (WBSN), a smartphone, a cellular network, healthcare centers, and other components of the proposed GRMT approach is introduced. 3- An analytical model that describes the way of differentiating between various available routes is presented. In this model, healthcare centers are assumed to vary in terms of offered services. Specialized healthcare centers provide medical services related to the tourists medical conditions. On the other hand, general healthcare centers offer general medical treatment that could help in classification and initial treatment of an emergency case. In Akter et al. (2013) several unique attributes of mHealth have been specified. While designing GRMT, we have taken into consideration satisfying these attributes as much as possible. The first attribute is accessibility; GRMT as a mHealth for mobile tourists provides ubiquitous and unison accessibility for HCCs at any-time and anywhere. The second attribute is personalized solutions; GRMT provides mHealth for individualized solutions taking into account the specific needs of 2
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no single study exists which has proposed and evaluated the approach proposed by GRMT. A solution such as presented in this paper would have potential social impact for tourists who need special healthcare and would like to undertake different tourism experiences. This is because it provides a service which allows them to overcome perceived leisure constraints. Specifically, a traveler is assured that when any health problem emerges while moving between tourism sites in a foreign destination, potential tourist would be routed through the fastest path toward the nearest healthcare center that will have the required information about their health status. Consequently, specialized medical treatment would be provided to such patients as soon as possible and by professional staff. This in turn would encourage such tourism market segment to have free barriers and accessible tourism experience. However, it is worth mentioning that GRMT approach supports the current and main notion of the United Nations World Tourism Organization (UNWTO) entitled as “Tourism for All” which seeks promoting universal accessibility.
using a VANET which is an ad-hoc network that is integrated with a vehicle system to connect it with the internet and other nearby vehicles. WBSN is a set of health related sensors that are placed on a special suit so they become wearable and collect the patients' Personal Health Information (PHI). Based on PHI, the medical staff can recognize the condition of the patient. In case of any emergency situations, the information will be forwarded to an Ambulance. Thus the patient can get complete medical care and monitoring of their health condition from any place. GRMT uses the concept of PHI that can be collected by a WSN attached to the tourist body or environment. A prototype for placing heterogeneous sensor nodes on human body for health monitoring is presented in Javaid et al. (2013). MobileHealthcare is the new technology or providing healthcare services using the two new technologies; WSN and IoT. Many applications and studies have used the concept of IoT in health related systems for patient's surveillance and monitoring. Istepanian et al. (2011) have introduced a new concept of integrating the functionalities of mobile-healthcare and IoT in m-IoT. However, its concept is related to the new (4G Health), a future innovative health services based on the IPv6 connectivity. They tested the system using thermal sensor nodes, for measuring body temperature as an example for healthcare applications. Cloud computing and IoT concept together constitute a good combination for sensor data processing and management in an efficient way. Doukas and Maglogiannis (2012) present a platform for patientrelated sensor data management, in which IoT allows the sensor devices to communicate directly with the cloud applications. Whereas a routing protocol called Movement Aided Energy Balance (MAEB), a prototype of the IoT healthcare systems has been introduced by Su et al. (2013). More specifically, it is an ambulatory system that uses WBAN and a broader telemedicine system, where WBAN collects patient's physiological information and vital signs and transmits them to the Internet through the Access Gateway (GA), which in turn transmits them to the telemedicine system. Coordinators, using neighbor's discovery procedure, get information about its neighbor's movement and energy, and then using MAEB forwarding the most suitable coordinator to forward data will be selected. The study compares the current proposed approach with a geographical routing approach that is proposed by Almobaideen et al. (2015). We will refer to this approach as Geographical Routing based on User preferences (GRUP). A combined criterion was used to provide a service that suggests geographical routes based on user preferences in terms of public transportation and services. Public transport media preferences included bus, train, metro, and walking … etc. on the other hand, GRUP is also based on service related preferences, which include the best wireless network connection quality such as General Packet Radio Service (GPRS), 3G, and 4G … etc. along the available paths. The aim was to provide continuous and best quality of internet connection for tourists or travelers in general, while they are moving to their destination. GRUP has been modeled, simulated via C++ program, and then evaluated to prove that its combined criteria outperform others in selecting geographical routes when considering the preferred public transport media and the higher quality network connections. A summary of the related previous studies is presented in Table 1. Reflection to explore the healthcare systems; medical tourism systems, patients monitoring systems and the concept of IoT in health-related technologies, this study has found the necessity for developing a new approach for patient-tourists with chronic diseases that finds the best route that fits their health status and medical conditions. In this paper we have focused on patient-tourists, who suffer from chronic diseases and cases that require constant monitoring. Collecting patient's health information in one location and make them accessible for services providers and patients is considered a major challenge. Despite the fact that many studies have investigated the problem of monitoring patient's physiological and vital signs in healthcare systems,
3. Geographical Routing for Mobile Tourists (GRMT) In this section we present the main components of GRMT starting with the transportation network model in Section 3.1. The general architecture is illustrated in Section 3.2. The main phases of the GRMT protocol are described in Section 3.3. Section 3.4 sheds light on the simulation environment assumed to evaluate and compare the protocols under consideration. 3.1. Network model GRMT finds a geographical route in a public transportation network, e.g. in Fig. 1, which is represented by a graph (Aldous and Wilson, 2000) as follows: 1 The transportation network, of the city where the tourist travels, is represented as a graph G with a set of nodes N, edges E, and healthcare centers HCC. 2 Each n ϵ N is a transportation station (intersection). 3 Each e ϵ E is an edge that represents a road segment which connects two transportation stations. 4 Each SHCC ϵ HCC is a specialized healthcare center relative to the mobile tourist's health conditions, and each GHCC ϵ HCC is a healthcare center that provides general medical services. 5 Each road segment represents a transport line, for example Bus, and has its own characteristics such as duration time, and its departure and termination stations. 6 Each w ϵ W represents a road segment weight that is calculated based on the length of the route and the vicinity of healthcare centers, as explained in Section 3.3. 7 Each tourism site has a station close to it. 3.2. General architecture of GRMT The architecture of GRMT is illustrated in Fig. 2. The tourist smartphone device is assumed to be GPS enabled and allows the tourist to send a preference message and get back a map that shows the resulted geographical route leading towards the intended destination. The following details describe the content of the preference message which is sent from the user smartphone and contains the following fields: 1 Time: The time when the message is created. 2 Location: The tourist's current location read by a GPS device and represents the source location. 3 Destination: The user intended tourism destination. 4 Personal Health Information (PHI): A specification of a tourist's health 3
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Table 1 Summary of the related previous work. Reference
Study Description
Purpose
Results
Stiller et al. (2006)
A handheld system that routes tourists with physical limitations through a way with fewer obstacles.
Fröber et al. (2006)
A design process for a Tourist Assistance System (TAS) that provides tourists with special health conditions with some services. VANET and WBSN are used together for collecting PHI and monitoring patients' physiological and vital signs. A prototype of health monitoring using WBSN, using two types of communication; multi and single hop. Integration of the IoT concept with mobilehealthcare in m-IoT. A platform based on Cloud Computing for management of mobile and wearable healthcare sensors data. A prototype of the IoT healthcare systems. A routing protocol called Movement Aided Energy Balance (MAEB). Geographical routing protocol (GRUP). Selects the route based on user preferences.
Allow people with disabilities and health problems to participate in holidays by taking their needs and conditions into account. Monitor patient-tourists signs and take an action in case of any emergency.
The system was developed to consider special health conditions. It was tested and has shown a high level of acceptance with some further hints to improve the usability. Developed and tested a strategy for determining the requirements and deploying design criteria for an assistance system. Based on modern healthcare systems, an application of the two technologies WBSN and VANET was developed and tested. The results have shown that the proposed routing algorithm has less energy consumption and more reliable as compared to multi-hop communication. Tests and results have shown the successful functionality of the system in real time environments Allowed direct communication of the sensor devices with the Cloud application. Highly scalable in the context of data stored, users and sensors supported. In comparison with three other approaches, MAEB has shown better performance in terms of energy consumption, packet delivery ratio, and throughput. GRUP allows mobile users to select geographical routes according to their preferred transportation media. And in comparison with Dijkstra's shortest path and other route finding approaches in term of the transmission rate of available network connection, GRUP better satisfies mobile users' needs.
Umamaheswari and Priya (2013) Javaid et al. (2013)
Istepanian et al. (2011) Doukas and Maglogiannis (2012) Su et al. (2013)
Almobaideen et al. (2015)
Monitor the patients' health continuously and to collect the patients' Personal Health Information (PHI) Patient vital signs monitoring.
Patient surveillance and monitoring. Allow the sensor devices to communicate directly with the cloud applications and managing patient-related data on the cloud. Ambulatory system that collects patient's physiological information and vital signs. Provide a path based on two user preferences; transportation media and network connectivity.
information that exists in various databases. It is connected to the geographical maps and transportation DB, tourism information DB, and the healthcare DB. These databases are described as follows:
conditions such as their diseases and needed medicines, sensitivity to other certain medicines, and any other related information. Each tourist, as a potential IoT object, is assumed to be equipped with small wireless sensors that should be placed in various parts of the body in a form of a WBSN (Yoo et al., 2009; Jassim and Almobaideen, 2013). These sensors collect various information that could represent health performance measures such as blood pressure, heart beats, or any other medical measures. This information is then sent to the tourist's smartphone device which possibly aggregates this information and sends it through a cellular communication network to the Tourists Serving Unit (TSU). The TSU is a main component which exits on the cloud-centric IoT and conducts the required computations and analyses based on the
1 The geographical and transportation database: Contains all the information of transportation network topology including the stations, road segments, driving speed over each segment, and other related records. 2 The Healthcare database: Contains information about all healthcare centers including their geographical location, whether they are specialized or general healthcare centers, and medical treatment they are specialized in. 3 The tourism location database: Stores information of the name, location, opening hours of different attractions or tourism destina-
Fig. 1. An example of the transportation network topology.
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Fig. 2. Geographical Routing for Mobile Tourist Architecture.
illustrated as follows:
tion.
Phase I: data aggregation The tourist's smartphone receives Personal Healthcare Information (PHI) message from the sink node of a Wireless Body Sensor Network (WBSN) (Jassim and Almobaideen, 2013). It then aggregates this information and encapsulates it into a Tourist Preference Message (TPM) accompanied with the other needed information such as the current time, the location of the targeted destination, and the current tourist's location. The TPM is then sent to the TSU through a cellular connection. Phase II: data processing and analysis After receiving the TPM, the received information will be retrieved and analyzed to check whether there is a path that has been previously generated from the current location to the target destination. If there is no path, road segments weights are calculated and the route with the highest weight is selected. Phase III: route generation In this phase and based on the aggregated information, a route that is best-served with medical centers is generated. A route consists of multiple road segments that connect the stations a route goes through. We refer to the set of N road segments that constitute a path as RS1, RS2 …RSN. This phase consists of multiple steps.
The TSU consists of the following modules: 1 The Message Reception Module: This Module receives the user preference message, de-capsulates its contents, and forwards it to the preprocessing module, should it be the first query for a particular source and destination. In case of being a message for a previously known source and destination it is forwarded to the cache module. 2 The Preprocessing Module: This module analyzes the information extracted from the tourist's preference message and consults the tourism locations database and the geographical database for transportation routes that are available between a tourist current location and the intended tourism destination sites. 3 The Route generation: Among the paths that could been found, the route generation module selects the best one after consulting the healthcare database for near HCCs, the location of such close HCCs, whether they are GHCCs or SHCCs, and does a SHCC specialty relates to the health problems of the tourist according to what has been mentioned in the preference message. The resulted route is then sent to the cache module and the route offering module. 4 The Cache Module: Keeps a cache of recent resulted routes for a certain source and destination and specified preferences. In case of receiving the same message again, the cache module can provide a path immediately without recalculating and generating the route again in order to increase the system performance. 5 The Route Offering Module: This module formats the resulted route in a way suitable to be displayed on the user's smartphone.
The first step starts with checking the vicinity of HCCs to each road segment that is part of a route to the destination. Vicinity in our model has two levels, specifically very close and close. We have set the very close vicinity distance to be 300 meters. We search also within a greater distance represents the close level of vicinity which we have set to 600 meters, see Table 1. It also shows that we have assigned different weights to various types of healthcare centers based on their level of vicinity and whether they are specialized or general.
3.3. GRMT phases and analytical description GRMT works in three main phases; data aggregation, data processing, and route generation. The details of each one of these steps are 5
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introduced in (Almobaideen et al., 2015) as mentioned in the related work section. The same simulator that has been used to simulate GRUP was amended with extra modules that implement the steps of GRMT. The simulator contains a module that builds a random transportation network topology, see Subsection 3.1. This topology consists of transportation stations that are assumed to be scattered around the terrain area that represents a city or a town. A road segment connects two transportation stations and has its own characteristics such as the duration time, network coverage quality, and its departure and terminal stations. In the amended simulator, HCCs are then randomly distributed over the specified geographical area covered by the transportation network. Furthermore, another part of the simulation program has been modified to simulate the architecture described in Subsection 3.2. This part simulates a tourist preference message that is received, preprocessed, and afterward a route is generated based on the calculations of weights described in Subsection 3.3. The final step applies Dijkstra algorithm, which is a well-known algorithm that finds the best path between two end points (Cormen et al., 2001), in order to look for the route with the best weight toward the suggested tourism destination. Table 2 presents a summary of the parameters that controlled the simulation experiments that have been conducted to evaluate GRMT. For GRUP and shortest route approaches we have based this evaluation over the same analytical model and parameters values described in Almobaideen et al. (2015).
N . SHi, VC × W . SHVC + N . SHi, C × W . SHC WH . RSi =
+ N . GHi, VC × W . GHVC + N . GHi, C × W . GHC TN . HCC
(1)
Where WH . RSi is the calculated weight of HCCs of road segment i of the whole route to the tourism destination. This weight is calculated by adding the values of multiplying the number of specialized HCCs and general HCCs by their corresponding weight whether they are close and very close divided by the total number of HCCs in the whole geographical area (TN . HCC). For example N . SHi , VC represents the number of specialized HCCs that are very close to road segment i, and W . SHVC is the weigh associated with this type of HCCs. Another example is the term that multiplies the number of general HCCs that are just close to road segment i,N . GHi , C, by its associated weight, W . GHC. Next, and since we prefer that the HCCs are distributed evenly along the road segment instead of cluttering in a small part of it, we measure that distribution. This is done logically by dividing the whole road segment into multiple equal fragments of length D. This would result of NF . RSi fragments of road segment i, and accordingly we use Eq. (2):
Dis. RSi =
NF . RSi, HCC NF . RSi
(2)
Where Dis . RSi is a measure of the even distribution of HCCs over road segment i, and NF . RSi , HCC is the number of fragments of road segment i given that each one of these fragments include at least one HCC. Given that we have measured the weight of HCCs existence around the road segment i, and measured how even they are distributed along that road segment, we can now calculate a combined weight CW . RSi for this road segment as in Eq. (3):
CW . RSi = (α × Dis. RSi ) + (1 − α ) × WH . RSi
4. Evaluation results and discussion In order to evaluate GRMT and compare it to GRUP and Dijkstra's shortest route we have created five different topologies, see Subsection 3.1, using different seeds for the random number generator of the simulator. In each of these topologies, fifteen different sources and destinations have been selected randomly and the average of the (75) simulation runs has been calculated as the final result presented in all the figures of this section. These experimental results are discussed next. In GRMT, as it mainly focuses on finding a route that is best served with HCCs, the total number of HCCs that are close to the selected route is expected to be more than both GRUP and Dijkstra's shortest route. Fig. 3 confirms that GRMT results in a larger average number of specialized and general HCCs that are close to the resulted route than the other two approaches. This is because GRMT takes the number of HCCs into account when selecting the path, which ensures, for tourists with chronic diseases, to be resuscitated in less time in case of any emergency. In this figure, one can notice that as the terrain area increases the number of found HCCs decreases since the same number of HCCs is distributed over a larger area. GRMT is able to perform better than GRUP and the Shortest_route approaches for all values of terrain
(3)
Where α is a weight factor that gives more importance to a certain term in Eq. (3) over the other. Setting α to a value greater than 0.5 makes (Dis . RSi) more effective in the final combined weight and vice versa. After calculating the combined weight of each road segment, we calculate the HCCs weight of the whole route (WH.route), which is composed of these N road segments as shown in Eq. (4): N
WH . Route =
∑i =1 CW . RSi (4)
N
In order to select the best route to the tourism destination, we have to take another factor, other than the HCCs, into consideration since a tourist needs to go through the shortest route possible as well as being served with the largest number of HCCs. To achieve this target we calculate a weight of each road segment in regards to its length or the distance a tourist has to go through that segment as in Eq. (5):
Distance. RSi =
ED. RSi Field . Diameter
Table 2 Values of the parameters that controlled the experiments conducted using the developed simulator.
(5)
Where ED . RSi is the Euclidian distance between the two stations that constitute road segment i. and the Field. Diameter represents the largest distance in the geographical area where the tourist is supposed to move. Finally we find the FW.route which divides the weight of HCCs of that route (WH . Route) by the sum of distance weights of all road segments (Distance . RSi) as shown in Eq. (6):
FW . route =
WH . Route N ∑i =1 Distance .
RSi
(6)
3.4. GRMT simulation environment GRMT has been simulated and compared with GRUP that has been 6
Parameter
Value
Number of stations Number of healthcare centers Alpha Very close vicinity Close vicinity Specialized HCC within very close vicinity weight Specialized HCC within close vicinity weight General HCC within very close vicinity weight General HCC within close vicinity weight Fragments granularity Poor network connection weight Moderate network connection weight Good network connection weight
35 50, 35, and 20 0.4 300 m 600 m 1 0.9 0.85 0.7 300 m 0.3 0.6 0.9
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served with larger number of HCCs even though this comes at the expense of slight increase of the path length. Given the context of a tourism experience, a tourist would not mind being routed via slightly longer path to reach a tourism destination for the benefit of being always close to HCC that could provide the required treatment in case it is needed. Consequently, this longer route could give a tourist the opportunity of seeing locations that were not listed in the tour itinerary. This could enrich his/her tourism experience as well as convenience. Fig. 7 shows an evaluation of GRMT, GRUP and Dijkstra's shortest route with regards to the average ratio of the wireless network connection's quality that covers the selected route. The quality of wireless network connection over different road segments has been assumed to vary into three classes, poor, moderate, and good. Each one of these classes has been assigned a weight as 0.3, 0.6, and 0.9 respectively. The results of this experiment show that GRMT selects a route that is covered with slightly less quality of wireless network connections compared with the other two approaches and specially GRUP. This is due to the fact that GRUP focuses on selecting a path that is covered with a good quality network connection without considering the number and distribution of HCCs along that route as GRMT does. GRMT assumes that a tourist is always connected to the IoT in order to rely on the medical information related to his/her current health status to the nearest HCC. Based upon the related literature of this paper, it is noted that an increasing number of solutions rely on the availability of ABC channels to the internet for various parties of the tourism industry, e.g. the tourists, transportation media, tourism sits, health care centers. This assumption is valid and comes as a fact in the era of IoT. Accordingly, the metric presented in Fig. 7 is a vital one and prefers GRMT over its rival approaches. In order to study the effect of HCCs density in the terrain area we have changed the number of distributed HCCs in a 7.5 km × 7.5 km terrain. Various numbers of HCCs ranging from 20, 35, and up to 50 HCCs have been distributed randomly all over the terrain area. Results shown in Fig. 8 emphasize that GRMT always selects the route that is best served with HCCs regardless of the density of their distribution. This indicates that GRMT is able to serve tourist even when the area of the tourist attraction destination is different from what has been assumed in this paper. This in turn would increase the confidence of a tourist, who needs special health monitoring, in GRMT as an assistant that keeps an eye on his/her health status, communicate such information with close HCCs, and guide him/her through a path that is best served by such HCCs regardless of their density over the area of the destination. Such a confidence would make a big difference in increasing the number of active travelers from the sample targeted by this research.
Fig. 3. Average Number of, close and very close, Specialized and General HCCs.
areas. GRMT offers the path to a tourism destination that is served by the largest possible number of HCCs in addition of being as short as possible. This would encourage tourists with health concerns while moving between tourism sites to go forward and be active travelers. GRMT selects the route that is best served with HCCs, taking into account the vicinity of these HCCs and their specialty. Fig. 4 shows a comparison between the three approaches in regard with the average number of two HCC types, i.e. specialized as in (a), and general as in (b), that are very close, i.e. located within 300 meters, as the terrain area increases. This figure shows that GRMT routes a tourist through a path that passes very close to many more healthcare centers than GRUP and Dijkstra's shortest. This achievement is very crucial for tourists with chronic diseases as it allows them to get as fast as possible to those very close HCCs which could provide basic health treatment or specialized one in case of emergency. Take into consideration that GRMT allows the details of the health status of the patient tourist to be collected and sent beforehand to the nearest SHCCs so that in an emergency case a specialized doctor who already knows what the case is, will be ready to treat this patient. Fig. 5 shows the average number of the two types of HCCs, specialized in (a) and general in (b), which are close to the selected route, i.e. within 600 meters, as the terrain area increases. This figure also shows that GRMT routes a tourist through a better route that is served by a larger number of HCCs than the routes selected by GRUP and Dijkstra's shortest route. This gives tourists with chronic diseases an assurance of finding a close HCC in case a very close one is busy or currently un-available for one reason or another. This would encourage tourists to visit more attractions and to spend more time in the locations they are interested in. Even though we have taken the distance of the route into account, the selected path in GRMT is longer in distance and requires slightly more time duration to reach than GRUP and Dijkstra's shortest selected routes as shown in Fig. 6. This is because GRMT prefers routes that are
Fig. 4. (a) Average Number of Specialized HCC within 300. (b) Average Number of General HCC within 300
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Fig. 5. (a) Average Number of Specialized HCC within 600. (b) Average Number of General HCC within 600
5. Conclusion Offering free barriers tourism experiences for tourists with special needs is vital for attracting such tourists and enhancing the tourism supply. Therefore, the main purpose of this study was to propose a route selection approach that is based on the vicinity of the medical centers as well as the distance of that route. However, it could be concluded that the proposed approach GRMT can contribute in improving mHealthcare system and enhancing the overall tourism experience. Simulation experiments have been conducted to evaluate GRMT in comparison to GRUP that is based on user preferences in regards to both public transportation means and networks coverage. The study findings indicate that GRMT allows tourists with health problems to select a route towards their tourism destination that is best served with medical centers. Also providing a route with acceptable quality network coverage, even though less than that of GRUP could enhance the satisfaction level for mobile users. Based upon the fact that there have been several lacunae in the etourism literature in the context of ICT in the tourism context; it is reasonable to suggest that this research could extend our knowledge for implementation of IOT in the context of smart mobile tourism. Additionally, the proposed approach could improve the quality of tourism experience and decrease the health concerns for patient tourists. In terms of directions for future research, further work could be done to explore the suggested approach GRMT in different tourism contexts, and settings, e.g. taking the network coverage into consideration. Tourism managers and policy makers could use such approach to provide accessible services for tourists with special needs and attract this important tourism segment. Moreover, such applications could
Fig. 7. Average quality of network connection coverage over the selected route.
Fig. 8. Average number of close and very close HCCs to the selected route as the density of their distribution increases.
Fig. 6. (a) Average Duration of traveling along the selected path. (b) Average Distance of the selected path.
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enhance the ethical practices for tourism and promote the social responsibility for tourism industry as it represents a basic human right and should be accessible for all individuals (Richards et al., 2010). The proposed routing approach GRMT is not considered as an alternative method for any routing approaches, neither of Google Maps nor any other geographic routing applications. However, GRMT could be integrated with these systems and expanded to be used not only in tourism applications but also in our daily lives to get along in urban areas as a mobile eHealthCare assistant. References Akter, S., D'Ambra, J., Ray, P., 2013. Development and validation of an instrument to measure user perceived service quality of mHealth. Inf. Manag. 50 (4), 181–195. Aldous, J., Wilson, R., 2000. Graphs and Applications: An Introductory Approach. Springer. Allan, M., 2015. Accessible tourism in Jordan: travel constrains and motivations. Eur. J. Tour. Res. 10, 109–119. Almobaideen, W., Saadeh, M., Al-Anbaki, N., Zaghloul, R., Aladwan, A., 2015. Geographical route selection based on user public transportation and service preferences. In: 9th International Conference on Next Generation Mobile Applications, Services and Technologies. Cambridge, UK, pp. 144–149. Basili, A., Liguori, W., Palumbo, F., 2014. NFC smart tourist card: combining mobile and contactless technologies towards a smart tourist experience. In: IEEE 23rd International WETICE Conference. IEEE, Parma, pp. 249–254. Bizjak, B., Knezevic, M., Cvetreznik, S., 2011. Attitude change towards guests with disabilities reflections from tourism students. Ann. Tour. Res. 38 (3), 842–857. Buhalis, D., 2003. eTourism: Information Technology for Strategic Tourism Management. Pearson (Financial Times/Prentice Hall), London (ISBN 0582357403). Chen, C., Wu, C., 2009. How motivations, constraints, and demographic factors predict seniors' overseas travel propensity. Asia Pac. Manag. Rev. 14 (3), 301–312. Chung, N., Lee, H., Lee, S.J., Koo, C., 2015. The influence of tourism website on tourists' behavior to determine destination selection: a case study of creative economy in Korea. Technol. Forecast. Soc. Chang. 96, 130–143. Cormen, T.H., Leiserson, C.E., Rivest, R.L., 2001. Dijkstra's Algorithm. Introduction to Algorithms. pp. 595–601. Disabled World (TM), 2013. World facts and statistics on disabilities and disability issues. URL. http://www.disabledworld.com/disability/statistics/#ixzz2HJ698Oj (Accessed on 01 May 2013). Doukas, C., Maglogiannis, I., 2012. Bringing IoT and cloud computing towards pervasive healthcare. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, http://dx.doi.org/10.1109/imis.2012.26. Fröber, U., Lutherdt, S., Stiller, C., Roß, F., Witte, H., Kurtz, P., 2006. The design of a tourist assistance system for several handicapped and elderly. In: Proc. of IEA, . Gretzel, U., Yuan, Y.L., Fesenmaier, D.R., 2000. Preparing for the new economy: advertising strategies and change in destination marketing organizations. J. Travel Res. 39 (2), 146–156. Gretzel, U., Sigala, M., Xiang, Z., Koo, C., 2015. Smart tourism: foundations and developments. Electron. Mark. 25 (3), 179–188. Gustafsson, E., Jonsson, A., 2003. Always best connected. IEEE Wirel. Commun. 10 (1), 49–55. Ho, C.I., Lee, Y.L., 2007. The development of an e-travel service quality scale. Tour. Manag. 28 (6), 1434–1449. Istepanian, R.S., Sungoor, A., Faisal, A., Philip, N., 2011. Internet of m-health Things “mIoT”. In: IET Seminar on In Assisted Living 2011, pp. 1–3. http://dx.doi.org/10.1049/ ic.2011.0036. Jassim, M.A., Almobaideen, W.A., 2013. A segment-based tree traversal algorithm for enhancing data gathering in wireless sensor networks. In: International Conference on Electronics, Signal Processing and Communication Systems, Venice, Italy, pp.
Professor Wesam Almobaideen ([email protected]) has received his Ph.D. in Computer Networks from the University of Bologna, Bologna, Italy, Master degree in Computer Science from the University of Jordan, and B.Sc. degree in Computer Science from Mu'ta University, Karak, Jordan. He is currently a full professor at the Computer Science Department, The University of Jordan. His research interests include: Wireless network, Computer and network security, Routing protocols, Internet of Things, Smart tourism. Rand Krayshan has received her Bachelor's degree from The University of Jordan in Computer Science in 2013, and her Master's degree from The University of Jordan in Computer Science, in 2016. Her research interests include: Wireless networks, Geographical routing protocols, Internet of Things (IoT). Dr Mamoon Allan is currently an associate professor of tourism marketing at the Tourism Department, Faculty of archaeology and Tourism, the University of Jordan. He was working in tourism and hospitality fields in three countries: Australia, Jordan and Libya. He completed his PhD in Tourism Marketing at Edith Cowan University in Perth, Western Australia. Dr Allan conducts international research in the fields of accessible tourism in the Middle East, the behavior and motivations of tourists with disabilities, smart tourism and also e-tourism marketing. Maha Saadeh, is a Ph.D. student in computer science at the University of Jordan. She worked as research and teaching assistant at the computer science department, The University of Jordan from September 2009 to September 2010. Then she received her M.Sc. degree in computer science from the same university in 2011. She has a number of publications in a number of local and international journals and conferences. Her research interests include: wireless networks, network security, and the Internet of Things (IoT).
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