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Location assisted delay-less service discovery method for IoT environments
Journal Pre-proof Location assisted delay-less service discovery method for IoT environments Ahmad AlZubi, Abdulaziz Alarifi, Mohammed Al-Maitah, Omar A. Albasheer
PII: DOI: Reference:
S0140-3664(19)31478-1 https://doi.org/10.1016/j.comcom.2019.11.045 COMCOM 6047
To appear in:
Computer Communications
Received date : 21 October 2019 Revised date : 12 November 2019 Accepted date : 26 November 2019 Please cite this article as: A. AlZubi, A. Alarifi, M. Al-Maitah et al., Location assisted delay-less service discovery method for IoT environments, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.11.045. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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LOCATION ASSISTED DELAY-LESS SERVICE DISCOVERY METHOD FOR IoT ENVIRONMENTS
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Ahmad AlZubi1, Abdulaziz Alarifi2, Mohammed Al‐Maitah3*, Omar A. Albasheer4 1,2,3 Computer Science Department, Community College, King Saud University, Saudi Arabia; 4 Al dar University College, School of Engineering and Technology *Corresponding Author: [email protected]
Abstract
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Location aided services (LAS) is a significant demand for wireless communication devices for service discovery and resource utilization in internet of things (IoT) environments. IoT devices provide services that are more significant about location and cost efficiency for both privacy and communication. In this paper, a location assisted delay-less service discovery (LDSD) for IoT users is introduced to minimize request-response delays. The proposed service discovery method
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searches cost effective resource for serving user requests with the knowledge of the resource location. With the knowledge of the location, LDSD classifies the resources based on service request delivery delay and availability for speedy resource mapping and service response. This resource discovery is also facilitated for replicated resource mapping considering service cost
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and request delivery delay ensuring minimum resource replication. The performance of the proposed LDSD is verified through experiments and the benefit of the discovery method is assessed using the metrics: response time, failure rate, resource utilization, request delivery time, resource availability and service latency. Experimental results demonstrate the reliability of the proposed LDSD by minimizing response time, failure rate, request delivery time and service latency and improving resource utilization and availability. Keywords— Location aided services, Non-Delay Tolerant Network, Resource Replication, Service Cost, and IoT Environment 1. Introduction
Location
based
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The
computing
system
comprises
of
heterogeneous
mobile
communicating devices, infrastructures and other smart elements that are interconnected ondemand to meet user requirements. Smart phones, human wearable devices, Internet of Things (IoT), sensing units, etc. are some of the elements of IoT environments that are grouped for sharing or communication purposes. The feature of the computing system rationalizes the behavior of the devices enabling interoperability, flexibility and collaborative functionalities in a
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communicating and resource managing environment [1,2]. The constructive features of the IoT environment are employed by end-user applications for service discovery, access and reusability. The individual and shared computing environment employs sophisticated communication technologies for application services by unifying service-level interface of the traditional
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communication networks. The flexibility is extended for multi-level service provisioning, complex processing, data processing, user management, different from simple information processing and querying process. Non delay-tolerant network demands expediting resource
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discovery by exploiting the constructive features of the computing environment. The different levels of service response are utilized for meeting the user demands in an IoT communication [36].
End-user applications make use of service discovery protocols for improving the efficiency of computing and network systems. The autonomous systems demand the support of
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service discovering architectures and methods for reliable communication and information access. Some common challenges faced by pervasive applications are user density, heterogeneous request processing, distributed resource access and management and in-time
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service response. The reliability of applications is supported by the service protocols for a certain extent by relying on other applications and services. Interoperability is fundamental characteristic of the computing systems to improve the reliability of communication [7, 8]. Obviously, the pervasive computing intends to improve the service availability using information systems and communication technologies. This design aim of pervasive systems has extended centralized communication to distributed autonomous computing and access environment [9,10]. In a distributed computing environment, the resources are fetched and accessed from multiple locations and storages. Therefore, the availability of resources at the required time interval needs to be synchronized; causing service delays and prolonged response time. The difficulty in service discovery is replaced by alternative resource mapping for the users initiating requests by integrating heterogeneous communication instances. The user requirements are satisfied by
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discovering a best-fit alternate service from the distributed environment with minimum delay and operational cost [11, 12].
Achieving user satisfaction for a similar range of requests with the limited resource access is a tedious task. The availability of the resource varies with time, capacity, downlink time and traffic [13]. Despite the external factors, multi-level cooperative communication interface
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and interoperable architectures are designed supporting user preferences [14,15]. Location based services (LBS) is one of the prominent development in communication that facilitates reliable service discovery and sharing. The end-user applications are designed to support location sharing services by exchanging their locations. In such communication scenario, the location privacy and
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precision improves the rate of communication, service sharing and reliability of information access [16,17]. The first stage of service discovery begins with location sharing process of the nearby service providing infrastructure. Location based service discovery improves the precision
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and reliability of the communicating devices by on-demand discovery [18, 19]. However, the diverse characteristics of the end-users and the application demands increase the request processing time and response besides resource mapping cost [20,21]. With the consideration of additive features of the pervasive systems and location services, this manuscript introduces location assisted delay-less service discovery for IoT user request processing in pervasive
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environment. The major contributions of this manuscript can be described as follows: (i) Designing a location assisted service discovery feature for user concentrated pervasive devices for improving resource utilization with controlled response time.
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(ii) Designing a cost effective service replication method to minimize service delivery latency and to improve resource availability to satisfy user demands. (iii) A comparative study of the proposed and existing methods using different metrics to verify the reliability of the proposed method. The remainder of the manuscript is organized as follows: Section 2 discusses the works related to the proposed method with its pros and cons. In Section 3, the proposed LDSD method is briefly discussed with computations and its working. Section 4 describes the experimental configurations and the comparative analysis of the proposed and existing methods followed by the conclusion in Section 5.
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2. Related Works
Meng et al. [22] proposed a big data management model for improving the quality of experience (QoE) in pervasive edge computing services. The big data management model adapts communication by verifying the transmission rates to satisfy QoE requirements. The data analysis rate of the edge services are improved using a tensor-fast convolutional neural network
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based deep learning. The variation in computation time of different services cannot be confined to result in lesser delay. Adjacent subtraction based localization (ASL) is introduced by Wang et al. [23] for improving the location privacy of pervasive computing systems. Efficient privacy-preserving localization
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(EPPL) is designed with the principles of ASL to improve the correctness, efficiency and privacy of computing systems. Different from the other privacy preserving methods, this method does not require homomorphic encryption technique.
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Chen et al. [24] proposed a joint communication and computation (JCC) mechanism for improving the flexibility of service provisioning in mobile-edge integrated cloud. Flexibility in JCC is achieved by allocating resources by pre-estimating the computation offloading issues of an end-user. It ensures optimal quality of service (QoS) satisfaction balancing resource demands and user-level offloading. The efficiency of balancing solutions generated is resolved by an
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additional admission control based on user ranks. This method is efficient in improving user, system and resource utilization rate under abnormal latency.
Cervantes et al. [25] presented pervasive system composition protocol (PSCP) for integrating ad-
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hoc and IoT network communications. This protocol is designed based on dynamic partial solutions (DPS) to mitigating the spatial and temporal constraints in service reliability. The adaptive and dynamic nature of the networks is preceded by the heuristic DPS to minimize service adaption time and messages. PSCP protocol recomposes discarded services based on their identification. Therefore, the chances of service availability is improves and hence the services handled is high. Contrarily, the discovery process takes more time restricting user requests due to higher failure rates.
A service concentrated middleware is designed by Belllavista et al. [26] for improving the adaptability and liveliness of ubiquitous computing services. User requirements are serviced through aggregation and service abstractions that are extendable by the edge nodes. The middleware manages the dynamic changes in user demands analyzing the semantics of the
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resources through different levels of abstraction. This method is effective in achieving lesser execution time irrespective of the service requests. Cao et al. [27] proposed a service recommendation model for improving the service precision of heterogeneous IoT applications. This model is designed for improving the QoS of the end-user service requirements by exploiting the relationship between services and resources. The model
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trains the relationship between services and resources for generating recommendations based on user requests. XWARE is an interoperable framework for pervasive computing systems designed by Roth et al. [28]. This framework is intended to bind communication and service discovery features of
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heterogeneous middleware. This integrated framework is customizable and flexible for smart computing environments irrespective of the density of users/ devices. This interoperable device achieves lesser access time but the rate of failure is quiet high.
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Kolomvatsos [29] proposed a distributed update management scheme for pervasive IoT applications. This management scheme accounts the autonomous nature of the nodes and collaborates the functions of the centralized server with the nodes for the update status retrieval. With the server dependency, the update process is performed in a timely manner. This facilitates unsynchronized time update minimizing the impact of load in the network.
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Sosa-Reyna et al. [30] proposed a model-driven development (MDD) methodology for improving the interoperability between heterogeneous communication devices in IoT. MDD satisfies abstraction, viewpoint, service-concentration and granularity that interoperates with
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different IoT layers for achieving better service qualities in IoT. Zhang et al. [31] designed iPOJO, for integrating the services of heterogeneous communication components in ubiquitous cloud services. This iPOJO architecture is similar to XWARE [16] but adapts domain specific language to define the workflow in clouds. The workflow is modeled as a directed-acyclic graph for ease of interoperability between different device interconnections. Lu et al. [32] proposed a multi-stage request-processing (MSRP) scheme for cloud storage to minimize retrieval delay and to improve the decoding success rates. Based on Luby transform codes, a delay model is developed for analyzing the storage access delay over multiple levels of information retrieval. The data packets are distributed across storages for higher availability ath the time of request processing. The accessibility is pursued by an optimization problem that is designed to minimize delay at the time of request processing. The requests are scheduled in their
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arriving order to minimize delay. This scheme is more precise for two-level request processing storages. The rate of failures due to sensitive requests in an heterogeneous environment is considered as a setback of MSRP. Yang et al. [33] proposed cloud information retrieval (CIR) framework for improving the reliability of information discovery and access in distributed cloud platform. This framework
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operates in a conventional and upgraded manner to achieve better reliability. The retrieval is based on keywords that searches encrypted data stored in cloud. The reliability of the service discovery is achieved by using key words and risk estimation. This search method is adaptable to different service discovery methods. Alternatively, in the updated service discovery, single entity
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queries are used for service discovery. This method achieves better service discovery precision and minimizes communication complexity but fails to minimize service failures.
From the above survey, it is clear that the method proposed in the past aim at improving
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service reliability of pervasive environments with the consideration of instant communication defect. The defects are based on time or resource availability, failing to meet the optimization constraint. The interoperable frameworks are either model driven [30] or middleware based [26, 28, 31] or service concentrated [27]. The interoperability of the frameworks and service environments are less feasible in service discovery due to asynchronous connection with the
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infrastructure or resource devices. In this manuscript, a balanced service discovery and cost effective replication is presented by incorporating the advantages if interoperability of cloud and
3. Methodology
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pervasive environments.
In this manuscript, the resources are considered to be centralized and distributed. Centralized resources are maintained, updated and leased by the cloud platform. Distributed devices include smart sensing, activating devices or even a mobile user, located at different sites across the geographical region. Resources are of any type including audio, video, multimedia, and data and so on. Resource discovery and service mapping are the prime focus in this manuscript. Resource discovery based on location and replication cost is focused to exploit the balance between heterogeneous communication environments. The advantages in integration are used for delay-less response for the varying request density in a IoT environment. In-time response in these environments is challenging due to self-adaptable and heterogeneous resource
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access. The replication and localization errors at the time of resource discovery and mapping are addressed in the following sections
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Figure 1 Representation of IoT Environment. Figure 1 represents the IoT environment. This computing environment is a collection of different types of users, Services, applications components. The different element of computing environment is available irrespective of time and location. Users are either mobile or static who form the service consuming part of the network. The resource warehouses, medicate communicating and resource demanding elements are synchronized through internet more specifically through cloud platforms. IoT computation is designed to ensure ease of service reliability for coping up the user demands. In
this
IoT
environment,
both
infrastructure
and
infrastructure-less
communication and storage elements are deployed. To satisfy user demands of varying type, the environment endorses miniature sensing devices to complex processing systems. With the
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development in technology, IoT services are compatible with a wide – range of devices and communication modes. Service discovery and responsive resources are expected to be the reliability deciding factors in this environment. In this manuscript, service discoveries are initiated by the end – user for which the storage or computing or processing systems in the IoT environment are expected to response in a timely and congestion free manner. The features of
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location aided service discovery helps to minimize response delay in a IoT environment. Based on the located services, requests are directed towards appropriate resources or re-directed to other resources based on availability and service rates. Table 1 describes the elements of a IoTcommunication environment. Class
End user
User
Devices
User/ Resource
Access Points Gateways
Infrastructure
Resource
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Sensors
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Base Station
Description The end-user is the service requestor and has permissions to access resources from distributed systems. The user performs the request through the device supportive applications. Homogeneous or heterogeneous users or wireless devices that act as a user or as a resource or a request handler. These are the connection establishing units in the network. They are capable of handling multiple user requests at a same time. Infrastructure supports wireless communication technologies for covering a large geographical region. These devices are used to observe the environmental changes and transmits the information to the nearest repository A centralized processing unit of the cloud network. This interconnects a variety of storage and other servers. These host a range of applications and lends services on request with authentication The storage acts as a repository for saving and retrieving files, information and other data received from multiple sources These are computational systems that facilitates data processing in an on-demand fashion
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Elements
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Table 1 Elements and their description of the IoT Environment
Server
Cloud
Storage
Data Analysis and Processing
Service
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3.1 Location assisted Delay-less Service Discovery (LDSD) Method Service discovery in IoT environment is an instantaneous process. The end –user devices broadcast service discovery message to its like neighbors or infrastructures. The request of the user is processed at different levels till the service repository is reached. The different levels of request processing include request forwarding and buffering to retain the liveliness of the request.
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Let N represent the number of available resources and 𝑆 denotes the services leased by the resources. The resources are distributed across a geographical region in the form of groups. The resources present in the same region that are equidistance to the end – user are considered as a single group 𝐺. The number services expected from a specific group is thus ∑𝑥𝑖 1 𝑁𝑖
𝑆 . Let
𝐺 𝑆
𝛼∗ 𝑠 𝐺 ∗ 𝑠
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∅
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𝑅 be the number of requests processed in available 𝑁 for 𝐺 , then
(1)
Where ∅ , is the service mapping cost, 𝐺 is the number of resource groups, 𝛼 is the number of service requests and 𝑠
is the average 𝑆 in a 𝐺. Let 𝑑 represent the distance between the user
and service group 𝐺 that is estimated using equation (2) 𝑥
𝑥 𝑥
𝑥
Where 𝑥 , 𝑦
𝑦 𝑦
𝑦
𝑦
and 𝑥 , 𝑦
, if the user is fixed /static 𝑡
𝑣 , if the user is mobile
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𝑑
(2)
are the position co-coordinates of the end – user and service 𝐺 .
Here, 𝑡 and 𝑣 denote the service request transmit time and user velocity, if the position of the
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user is subjected to change. If the end- user is present within the service group, then request message reaches the service resource repository in time. If the end-user is located for away or possesses mobility, then it relies on infrastructures and other like devices for service request processing. For achieving a timely request mapping for a user separated by 𝑑, ∅ must be achieved as minimum satisfying 𝐺
≃
√𝛼 𝑆 |𝑡𝑠 𝑡𝑟 |
Such that 𝑠𝑎𝑣𝑔 ≃ max
∗
(3) (4)
The prime goal in achieving maximum 𝐺 in minimum distance is the delay factor. As discussed, end- users expect minimum cost and service distance resources for delivering efficient services. Let 𝑡
be the maximum liveliness period of the user service request. Irrespective of the delay
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due to congestion and traffic in the shared environment, the request is to be served (allocated with resource) before 𝑡
time i.e. 𝑡
. Starting from 𝑡 , the service response time 𝑡
must be less than its liveliness
𝑡𝑚𝑎𝑥 . To prevent congestion, the request processing devices and infrastructures use
backoff propagation. In backoff propagation, the device/ infrastructures buffer the request until the wireless channel is free. If 𝑡 is the back off time for a request, 𝐺 at minimum 𝑑 is responded
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𝑡
. The backoff time relies on the channel utility for the shared group G.
𝑡
From the intermediate device/ infrastructure perspective, the number of requests handled 𝑅 is computed using equation (5) 𝑅
,𝑁 𝜖 𝐺
∑
(5)
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Where 𝑅 are the numbers of requests received and transmitted to the same 𝑅 respectively.
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Let 𝑐 represent the capacity of the infrastructure / the device processing the request of an enduser. The time at which the request is delivered to the 𝑅 ∑
ℝ𝐷
∑
∗
, ℝ 𝐷 is computed as
ℝ 𝐷 endorses 𝑡 , 𝑡 provided ℝ 𝐷 is valid for requests processed within 𝑡
(6) . Let 𝑙 represent the
levels such that the transmission delay 𝑡 𝑡
𝑙
∑
1 ∗ 𝑡
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neighbor in the levels of request processing between the end-user and 𝐺 . The 𝑡 is estimated for all the
is estimated as
𝜌 ∗ 𝑡
level. Based on equation (7) there are two possible
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Where 𝜌 is the backoff probability in the 𝑖
(7)
corollaries (i.e.) request processing with backoff and without backoff. Corollary 1: The condition when backoff probability is false i.e. 𝜌
0
Analysis 1: If the backoff probability is 0, then the service rate of 𝑅
by the device/
infrastructure is seamless. The 𝑐 of the device is continuously utilized for relaying service requests such that ∑ Therefore, 𝑡 where 𝜌
𝜌 ∗ 𝑡 𝑙
0.
1 ∗ 𝑡 . Considering ℝ 𝐷
0, equation (6) is given as
ℝ𝐷
∗ 𝑡 ∗ 𝑙
1
If the distance is minimum, where 𝑙
Therefore, selecting a G that ensures 𝑡
𝑠
(8)
2(the minimum) then
∗ 𝑡
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ℝ𝐷
The 𝑠
for a single level of transmission
𝑡
(9)
is sufficient for maximizing ∅𝑐 .
required in this case is thus estimated as 𝑆
𝑅
(10)
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Substitute equation (9) in equation (1) 𝐺𝑛 𝑅 𝐺𝑛 𝑆 , 𝛼
(11)
𝑅
Is the minimum cost for mapping service if 𝑡 equation (11) is satisfied for 𝜌 Corollary 2: 𝜌
𝑡
. Therefore, an N with ∅ of the above
0 where ℝ 𝐷 is the least delivery time of the request (equation (9))
1
Analysis 2: When 𝜌
1 , the chances of backoff time for a request are experienced by the neighboring
devices/infrastructure. In this case, equation (7) holds for all 𝑙
∗ 𝑡 𝑆
Here, 𝑆
1 ,𝑡
𝑙
1 ∗ 𝑡
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2 . Therefore ℝ 𝐷 is estimated as ℝ𝐷
of
∅𝑐
𝑡
𝑡
𝑖𝑓 𝑙
(12)
𝑅 as the number of handled requests is estimated post backoff. Therefore, the cost
∅
𝑅𝑛 ∗ 𝑆 𝑅 𝑛
𝐺
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of service mapping from equation (1) is represented as
(13)
Equation (13) is valid if there exists a 𝑡 at any level of request processing. Figure 2 represents the 0 𝑜𝑟 1.
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ℝ 𝐷 when 𝜌𝑖
𝟎 𝒐𝒓 𝟏
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Figure 2 Level 2 Based Representation of ℝ 𝑫 for 𝝆𝒊 Based on 𝜌
1 𝑜𝑟 0, the service is discovered with the awareness of its position 𝑥 , 𝑦 . The N in G
advertises position for the requesting users. With the previous experience of the users, the position of N in G with ℝ 𝐷 is differentiated with respect to delay estimated using equation (9) and (12). The
end- user request processing neighbor device/ infrastructure estimates the ∅ in both cases considering 𝜌 , irrespective of the location and distance. The location of the N with lesser ∅ is preferred
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another G is opted for 𝑅 processing. The location aided service discovery proceeded by ∅ and ℝ 𝐷
evaluation results in
reliable responses to the end –users. In this context, the replication factor of the resources is
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accounted to improve the consistency of S mapping within a located G. Based on the ∅ , replication is facilitated for improving service reliability. 3.2 Cost Effective Service Replication back off time (i.e.) 𝑡
𝑡
0𝑟 𝑡
𝑡
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The corollaries 1 and 2 are discussed with respect to backoff probability. The adversary impact of
𝑡
is mitigated by replicating services among the
available 𝐺 to retain lesser ∅𝑐 . Replicated resource access must also ensure lesser ∅ despite the 𝑙 in resource mapping and service request processing Replication of resources in successive G is followed by resource un-availability in the previous G. The resource unavailability 𝑅
𝑅
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equation (14) 1
Where 𝑟
and 𝑟 are the active and idle time of a resource. If the 𝑅
is computed using
(14) is true for either 𝑅 or
user.
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𝑅 , then replication of services occurs in the nearest G to prevent additional 𝑡 for the end – The rate of replication increases ∅ with increase in G with the same services. For 𝑅 or 𝑅 requests the required replicas is thus estimated as arg min 1
∏
𝑅
Now, the availability of the resource 𝑅 𝑅
∑
∑
Where 𝑑𝑖
.
𝐺, 𝐺
∏
𝑖 , 1
𝑅
𝑖 …, 1
∏
𝑅
𝑖
(15)
is estimated post the replication process as
. 𝑑𝑖
𝐺, 𝐺
(16)
is the diversity distance function representing the d between first G and
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replicated G. If the replicated resource is available in the same level of the location of N, then response time for mapping a resource to the request is estimated as 𝑡
∗
∑
Where 𝑡 is the resource mapping time and 𝑘 ∈ 𝑅
(17)
∗
𝑅 .
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For the 𝑅 in a G, the time taken to map a resource is ℝ 𝐷 replicated resource it is 𝑡 𝑡
. Obviously, the time 𝑡
whereas for a migrated/
must also ensure lesser ∅ satisfying 𝑡
𝑡𝑚𝑎𝑥 . For this purpose, the stability of the allocated resource in G in terms of delivery
delay and response are verified. The minimum number of replications ensures seamless service 𝐵
𝑅𝑒𝑝
𝑠
of
of 𝑅 requests is computed using equation (18) 𝑡
Where, B is the bandwidth of the wireless link.
replications of N in a new G must be 𝑡
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The time delay for 𝑅 with 𝑅𝑒𝑝
(18)
instead of its
delivery delay ℝ 𝐷 as in equation (8) or (12). Therefore, the expected delay for request delivery ℸ 𝐷 for replicated resources is ℸ𝐷
∗
𝑐 ∗ 𝑠
relies on the 𝑅𝑒𝑝
is active therefore, the response time will be 𝑡
the first replicated service mapping, 𝑡 𝑡
𝑡𝑜 . This proves that all 𝑅 will undergo corollary 2 conditions. Contrarily, the 𝑅
implies 𝐺
is 𝑅 and hence, ℝ 𝐷
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equation (12) for 𝑅𝑒𝑝
∅
of the resource for 𝑅 requests. In
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The factor 2 1
(19)
𝑅ℎ ∗ 𝑆 𝑅 ℎ
𝐺
∗ 𝑡
𝑆
𝑡𝑜 and 𝑠
𝑆
in
𝑅 that
(20)
For the offloaded service request and resource mapping in the newly replicated G, cost is given by equation (20) and 𝑡
𝑡
𝑡𝑜 for the delivery time as in equation (19).
The user imitates a request for service discovery that is forwarded to the destination through devices/ infrastructures. The resource is initially discovered based on distance. If the number of user request exceed the 𝑐 of the resources, then the nearest replicated G is searched for service mapping. The numbers of replicated resources concede mapping of requests without higher 𝑡 . If the request has to be processed with multi- level, then the next nearest resource is
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mapped. In both cases of resource mapping, the request delivery time is estimated based on their 𝑡 . If 𝑡 exceeds 𝑡
then the request is dropped, to prevent this request is forwarded to
the replicated G. The response time of the resource is determined based on its availability and service mapping cost. To satisfy 𝑡 ∅ such that 𝑡
𝑡
𝑡
and 𝑡
𝑡
, the requests are mapped to a resource with lesser is minimum.
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4. Performance Evaluation The working of the proposed LDSD is evaluated through an IoT integrated pervasive computing model for information sharing applications. The information sharing is modeled to
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query a resource that is accessible from an open source cloud platform. The resource is a coupled server and storage database with different application service information [34]. The model is and their configurations are presented in Table 2.
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constructed using Opportunistic Network Environment (ONE) simulator. The model parameters Table 2 Model Parameters and Configuration
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Number of devices Gateways Request/Sec 𝒕𝒎𝒂𝒙 Replications 𝒓𝒊𝒕 Bandwidth
Configuration Device Resource 100 10 16 50 3000 600ms 12 24ms 2Mbps 100Mbps
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Parameter
The performance of the propose LDSD is compared with the existing MSRP [32], PSCP-DPS [25] and CIR [33] for the metrics: response time, failure rate, resource utilization, request delivery time, resource availability and service latency. 4.1 Response Time Analysis
This ensures the least response time due to earlier request delivery. To ensure 𝑡 𝑡
, the overflowing requests are mapped to replicate G that ensures 𝑡
𝑡
𝑡
.
Therefore, delivery of requests in both G and replicated G is less. Instantly, the available resource is mapped to the request minimizing the response time [35]. The 𝑡 observed for 30
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service request is less and hence the response time is less compared to other request densities. The above two factors minimize the response time of the varying requests is the proposed LDSD.
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Figure 3 Service Requests versus Response Time Figure 3 illustrates the comparison of response time between the existing methods and
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proposed LDSD. IN the proposed LDSD, distance based service discovery is performed [36]. The resources with shorter 𝑑 are opted by the end- users either in 𝑙 or 𝑙
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4.2 Failure Rate Analysis
1 .
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Figure 4 Service Requests versus Failure Rate Figure 4 portrays the comparison of failure rate between MSRP, PSCP- DPS, CIR and the proposed LDSD respectively. The number of unmapped or expired requests 𝑡
𝑡
𝑡 𝑜𝑟 𝑡
in the proposed method is less. This is achieved by minimizing the delivery time
of
mapping cost. The neighboring devices/ infrastructure is segregated based on levels with the estimation of distance metric. Similarly, the request is prevented from queuing for a long time by selecting cost effective replicated G. For varying request density, the resource is either directly and ℝ 𝑑 , for replicated N) is assigned. The
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(based on d and ∅ ) or indirectly (based on ∅
instantaneous process of resource mapping for any number of requests with ∅ and ℝ 𝑑 , awareness of LDSD ensures lesser failure rate of the requests.
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4.3 Resource Utilization Analysis
Figure 5 Response Time versus Resource Utilization
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A comparative analysis of resource utilization between the existing methods and proposed LDSD is illustrated in Figure 5. Resource utilization is accounted based on the maximum service requests mapping in replicated and non – replicated G. The incoming requests are mapped with the resources in the G, with minimum failure rate. As the failure rate is less, the ratio of utilized resources is high with lesser ∅ . The overall response time for the entire request
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varies with ℝ 𝐷 and 𝑡
in mapping S from resources. This improves the resource utilization
of the cloud with lesser 𝑡 and satisfying 𝑡
𝑡
𝑡
.
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4.4 Request Delivery Time Analysis
Figure 6 Devices versus Request Delivery Time
The request delivery time in the proposed LDSD is more precise by differentiating the liveliness of the request. Request liveliness is maintained by mapping resources instantly from G or replicated G. Initially, the delivery time of the requests is less as resource is discovered with the consideration of d and ∅ if the request density due to increased device increases, d cannot be retained and hence ∅ and ℝ 𝐷 are accounted. Therefore, though the request is directed to the replicated resource, minimum ℝ 𝐷 based mapping ensures lesser request delivery time in the proposed LDSD (Figure 6). In the Figure 6, when device = 80, delivery time is observed to be
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high where 𝑡 of the request is high.
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4.5 Availability Analysis
Figure 7 Replicated Resources versus Availability
Figure 7 portrays the comparison of resource availability between the existing methods and the proposed LDSD. The resource replication is planned to satisfy to or 𝑡
𝑡
𝑡
and minimum ∅ . The average services in G is pre- estimated with the density of requests and services, Similarly, 𝑅 number of requests are to be serviced through replication; the replication is not allowed for all 𝑅 . The rate of replication satisfying 𝐺 and 𝑅
is ensured for 𝑅 such that 𝑅𝑒𝑝
is allocated for 𝑅 requests
is required to deliver requests with ℸ 𝐷 delay. 𝑡
. Unlike the
previous methods, replication is ensured with minimum ∅ for serving 𝑅
instead of 𝑅 .
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This delay ensures the liveliness of the requests 𝑅 Therefore, the number of serviced requests is also high.
such that 𝑡
𝑡
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4.6 Service Latency Analysis
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Figure 8 Replicated Resources versus Service Latency The comparative study of latency for the replicated resources is illustrated in figure 6. The proposed LDSD achieves an average latency effectiveness compared to the existing methods. This is because, LDSD requires 𝑅𝑒𝑝
for serving 𝑅 requests. The number of
requests exceeding 𝑐 of the N, is distributed among all the non-replicated and replicated resources for delay less response. The replicated resources are minimum, then proposed method achieves lesser latency. In other average replications, due to minimum ∅ and lesser ℝ 𝐷 , LDSD requires additional time for mapping the resources. Comparatively the proposed LDSD achieves quiet less latency for the varying replications. 5. Conclusion
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In this manuscript, a location assisted service discovery method for improving the reliability of IoT system is introduced. This service discovery method is designed for improving the reliability of request processing by minimizing response delay and resource mapping cost. The design goal of the proposed method is differentiated between backoff and normal requests by improving the resource availability rate. Resource availability is improved by placing minimum cost effective
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replications that ensure minimum service request delivery and lesser response. The overall performance of the service discovery and request mapping process is evaluated through a simulator model. The results show the consistency of the method by improving resource availability and utilization rate and minimizing response time, failure time, request delivery time
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and service latency. In future intelligent and optimization techniques are used to optimize the system performance effectively.
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Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1439-053. References
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LOCATION ASSISTED DELAY-LESS SERVICE DISCOVERY METHOD FOR IoT ENVIRONMENTS Ahmad AlZubi1, Abdulaziz Alarifi2, Mohammed Al-Maitah3*, Omar A. Albasheer4 Computer Science Department, Community College, King Saud University, Saudi Arabia; 4 Al dar University College, School of Engineering and Technology *Corresponding Author: [email protected]
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The authors declare that there is no Conflicts of Interest
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Conflicts of Interest
PII: DOI: Reference:
S0140-3664(19)31478-1 https://doi.org/10.1016/j.comcom.2019.11.045 COMCOM 6047
To appear in:
Computer Communications
Received date : 21 October 2019 Revised date : 12 November 2019 Accepted date : 26 November 2019 Please cite this article as: A. AlZubi, A. Alarifi, M. Al-Maitah et al., Location assisted delay-less service discovery method for IoT environments, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.11.045. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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LOCATION ASSISTED DELAY-LESS SERVICE DISCOVERY METHOD FOR IoT ENVIRONMENTS
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Ahmad AlZubi1, Abdulaziz Alarifi2, Mohammed Al‐Maitah3*, Omar A. Albasheer4 1,2,3 Computer Science Department, Community College, King Saud University, Saudi Arabia; 4 Al dar University College, School of Engineering and Technology *Corresponding Author: [email protected]
Abstract
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Location aided services (LAS) is a significant demand for wireless communication devices for service discovery and resource utilization in internet of things (IoT) environments. IoT devices provide services that are more significant about location and cost efficiency for both privacy and communication. In this paper, a location assisted delay-less service discovery (LDSD) for IoT users is introduced to minimize request-response delays. The proposed service discovery method
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searches cost effective resource for serving user requests with the knowledge of the resource location. With the knowledge of the location, LDSD classifies the resources based on service request delivery delay and availability for speedy resource mapping and service response. This resource discovery is also facilitated for replicated resource mapping considering service cost
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and request delivery delay ensuring minimum resource replication. The performance of the proposed LDSD is verified through experiments and the benefit of the discovery method is assessed using the metrics: response time, failure rate, resource utilization, request delivery time, resource availability and service latency. Experimental results demonstrate the reliability of the proposed LDSD by minimizing response time, failure rate, request delivery time and service latency and improving resource utilization and availability. Keywords— Location aided services, Non-Delay Tolerant Network, Resource Replication, Service Cost, and IoT Environment 1. Introduction
Location
based
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The
computing
system
comprises
of
heterogeneous
mobile
communicating devices, infrastructures and other smart elements that are interconnected ondemand to meet user requirements. Smart phones, human wearable devices, Internet of Things (IoT), sensing units, etc. are some of the elements of IoT environments that are grouped for sharing or communication purposes. The feature of the computing system rationalizes the behavior of the devices enabling interoperability, flexibility and collaborative functionalities in a
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communicating and resource managing environment [1,2]. The constructive features of the IoT environment are employed by end-user applications for service discovery, access and reusability. The individual and shared computing environment employs sophisticated communication technologies for application services by unifying service-level interface of the traditional
of
communication networks. The flexibility is extended for multi-level service provisioning, complex processing, data processing, user management, different from simple information processing and querying process. Non delay-tolerant network demands expediting resource
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discovery by exploiting the constructive features of the computing environment. The different levels of service response are utilized for meeting the user demands in an IoT communication [36].
End-user applications make use of service discovery protocols for improving the efficiency of computing and network systems. The autonomous systems demand the support of
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service discovering architectures and methods for reliable communication and information access. Some common challenges faced by pervasive applications are user density, heterogeneous request processing, distributed resource access and management and in-time
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service response. The reliability of applications is supported by the service protocols for a certain extent by relying on other applications and services. Interoperability is fundamental characteristic of the computing systems to improve the reliability of communication [7, 8]. Obviously, the pervasive computing intends to improve the service availability using information systems and communication technologies. This design aim of pervasive systems has extended centralized communication to distributed autonomous computing and access environment [9,10]. In a distributed computing environment, the resources are fetched and accessed from multiple locations and storages. Therefore, the availability of resources at the required time interval needs to be synchronized; causing service delays and prolonged response time. The difficulty in service discovery is replaced by alternative resource mapping for the users initiating requests by integrating heterogeneous communication instances. The user requirements are satisfied by
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discovering a best-fit alternate service from the distributed environment with minimum delay and operational cost [11, 12].
Achieving user satisfaction for a similar range of requests with the limited resource access is a tedious task. The availability of the resource varies with time, capacity, downlink time and traffic [13]. Despite the external factors, multi-level cooperative communication interface
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and interoperable architectures are designed supporting user preferences [14,15]. Location based services (LBS) is one of the prominent development in communication that facilitates reliable service discovery and sharing. The end-user applications are designed to support location sharing services by exchanging their locations. In such communication scenario, the location privacy and
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precision improves the rate of communication, service sharing and reliability of information access [16,17]. The first stage of service discovery begins with location sharing process of the nearby service providing infrastructure. Location based service discovery improves the precision
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and reliability of the communicating devices by on-demand discovery [18, 19]. However, the diverse characteristics of the end-users and the application demands increase the request processing time and response besides resource mapping cost [20,21]. With the consideration of additive features of the pervasive systems and location services, this manuscript introduces location assisted delay-less service discovery for IoT user request processing in pervasive
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environment. The major contributions of this manuscript can be described as follows: (i) Designing a location assisted service discovery feature for user concentrated pervasive devices for improving resource utilization with controlled response time.
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(ii) Designing a cost effective service replication method to minimize service delivery latency and to improve resource availability to satisfy user demands. (iii) A comparative study of the proposed and existing methods using different metrics to verify the reliability of the proposed method. The remainder of the manuscript is organized as follows: Section 2 discusses the works related to the proposed method with its pros and cons. In Section 3, the proposed LDSD method is briefly discussed with computations and its working. Section 4 describes the experimental configurations and the comparative analysis of the proposed and existing methods followed by the conclusion in Section 5.
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2. Related Works
Meng et al. [22] proposed a big data management model for improving the quality of experience (QoE) in pervasive edge computing services. The big data management model adapts communication by verifying the transmission rates to satisfy QoE requirements. The data analysis rate of the edge services are improved using a tensor-fast convolutional neural network
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based deep learning. The variation in computation time of different services cannot be confined to result in lesser delay. Adjacent subtraction based localization (ASL) is introduced by Wang et al. [23] for improving the location privacy of pervasive computing systems. Efficient privacy-preserving localization
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(EPPL) is designed with the principles of ASL to improve the correctness, efficiency and privacy of computing systems. Different from the other privacy preserving methods, this method does not require homomorphic encryption technique.
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Chen et al. [24] proposed a joint communication and computation (JCC) mechanism for improving the flexibility of service provisioning in mobile-edge integrated cloud. Flexibility in JCC is achieved by allocating resources by pre-estimating the computation offloading issues of an end-user. It ensures optimal quality of service (QoS) satisfaction balancing resource demands and user-level offloading. The efficiency of balancing solutions generated is resolved by an
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additional admission control based on user ranks. This method is efficient in improving user, system and resource utilization rate under abnormal latency.
Cervantes et al. [25] presented pervasive system composition protocol (PSCP) for integrating ad-
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hoc and IoT network communications. This protocol is designed based on dynamic partial solutions (DPS) to mitigating the spatial and temporal constraints in service reliability. The adaptive and dynamic nature of the networks is preceded by the heuristic DPS to minimize service adaption time and messages. PSCP protocol recomposes discarded services based on their identification. Therefore, the chances of service availability is improves and hence the services handled is high. Contrarily, the discovery process takes more time restricting user requests due to higher failure rates.
A service concentrated middleware is designed by Belllavista et al. [26] for improving the adaptability and liveliness of ubiquitous computing services. User requirements are serviced through aggregation and service abstractions that are extendable by the edge nodes. The middleware manages the dynamic changes in user demands analyzing the semantics of the
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resources through different levels of abstraction. This method is effective in achieving lesser execution time irrespective of the service requests. Cao et al. [27] proposed a service recommendation model for improving the service precision of heterogeneous IoT applications. This model is designed for improving the QoS of the end-user service requirements by exploiting the relationship between services and resources. The model
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trains the relationship between services and resources for generating recommendations based on user requests. XWARE is an interoperable framework for pervasive computing systems designed by Roth et al. [28]. This framework is intended to bind communication and service discovery features of
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heterogeneous middleware. This integrated framework is customizable and flexible for smart computing environments irrespective of the density of users/ devices. This interoperable device achieves lesser access time but the rate of failure is quiet high.
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Kolomvatsos [29] proposed a distributed update management scheme for pervasive IoT applications. This management scheme accounts the autonomous nature of the nodes and collaborates the functions of the centralized server with the nodes for the update status retrieval. With the server dependency, the update process is performed in a timely manner. This facilitates unsynchronized time update minimizing the impact of load in the network.
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Sosa-Reyna et al. [30] proposed a model-driven development (MDD) methodology for improving the interoperability between heterogeneous communication devices in IoT. MDD satisfies abstraction, viewpoint, service-concentration and granularity that interoperates with
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different IoT layers for achieving better service qualities in IoT. Zhang et al. [31] designed iPOJO, for integrating the services of heterogeneous communication components in ubiquitous cloud services. This iPOJO architecture is similar to XWARE [16] but adapts domain specific language to define the workflow in clouds. The workflow is modeled as a directed-acyclic graph for ease of interoperability between different device interconnections. Lu et al. [32] proposed a multi-stage request-processing (MSRP) scheme for cloud storage to minimize retrieval delay and to improve the decoding success rates. Based on Luby transform codes, a delay model is developed for analyzing the storage access delay over multiple levels of information retrieval. The data packets are distributed across storages for higher availability ath the time of request processing. The accessibility is pursued by an optimization problem that is designed to minimize delay at the time of request processing. The requests are scheduled in their
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arriving order to minimize delay. This scheme is more precise for two-level request processing storages. The rate of failures due to sensitive requests in an heterogeneous environment is considered as a setback of MSRP. Yang et al. [33] proposed cloud information retrieval (CIR) framework for improving the reliability of information discovery and access in distributed cloud platform. This framework
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operates in a conventional and upgraded manner to achieve better reliability. The retrieval is based on keywords that searches encrypted data stored in cloud. The reliability of the service discovery is achieved by using key words and risk estimation. This search method is adaptable to different service discovery methods. Alternatively, in the updated service discovery, single entity
of
queries are used for service discovery. This method achieves better service discovery precision and minimizes communication complexity but fails to minimize service failures.
From the above survey, it is clear that the method proposed in the past aim at improving
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service reliability of pervasive environments with the consideration of instant communication defect. The defects are based on time or resource availability, failing to meet the optimization constraint. The interoperable frameworks are either model driven [30] or middleware based [26, 28, 31] or service concentrated [27]. The interoperability of the frameworks and service environments are less feasible in service discovery due to asynchronous connection with the
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infrastructure or resource devices. In this manuscript, a balanced service discovery and cost effective replication is presented by incorporating the advantages if interoperability of cloud and
3. Methodology
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pervasive environments.
In this manuscript, the resources are considered to be centralized and distributed. Centralized resources are maintained, updated and leased by the cloud platform. Distributed devices include smart sensing, activating devices or even a mobile user, located at different sites across the geographical region. Resources are of any type including audio, video, multimedia, and data and so on. Resource discovery and service mapping are the prime focus in this manuscript. Resource discovery based on location and replication cost is focused to exploit the balance between heterogeneous communication environments. The advantages in integration are used for delay-less response for the varying request density in a IoT environment. In-time response in these environments is challenging due to self-adaptable and heterogeneous resource
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access. The replication and localization errors at the time of resource discovery and mapping are addressed in the following sections
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Figure 1 Representation of IoT Environment. Figure 1 represents the IoT environment. This computing environment is a collection of different types of users, Services, applications components. The different element of computing environment is available irrespective of time and location. Users are either mobile or static who form the service consuming part of the network. The resource warehouses, medicate communicating and resource demanding elements are synchronized through internet more specifically through cloud platforms. IoT computation is designed to ensure ease of service reliability for coping up the user demands. In
this
IoT
environment,
both
infrastructure
and
infrastructure-less
communication and storage elements are deployed. To satisfy user demands of varying type, the environment endorses miniature sensing devices to complex processing systems. With the
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development in technology, IoT services are compatible with a wide – range of devices and communication modes. Service discovery and responsive resources are expected to be the reliability deciding factors in this environment. In this manuscript, service discoveries are initiated by the end – user for which the storage or computing or processing systems in the IoT environment are expected to response in a timely and congestion free manner. The features of
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location aided service discovery helps to minimize response delay in a IoT environment. Based on the located services, requests are directed towards appropriate resources or re-directed to other resources based on availability and service rates. Table 1 describes the elements of a IoTcommunication environment. Class
End user
User
Devices
User/ Resource
Access Points Gateways
Infrastructure
Resource
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Sensors
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Base Station
Description The end-user is the service requestor and has permissions to access resources from distributed systems. The user performs the request through the device supportive applications. Homogeneous or heterogeneous users or wireless devices that act as a user or as a resource or a request handler. These are the connection establishing units in the network. They are capable of handling multiple user requests at a same time. Infrastructure supports wireless communication technologies for covering a large geographical region. These devices are used to observe the environmental changes and transmits the information to the nearest repository A centralized processing unit of the cloud network. This interconnects a variety of storage and other servers. These host a range of applications and lends services on request with authentication The storage acts as a repository for saving and retrieving files, information and other data received from multiple sources These are computational systems that facilitates data processing in an on-demand fashion
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Elements
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Table 1 Elements and their description of the IoT Environment
Server
Cloud
Storage
Data Analysis and Processing
Service
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3.1 Location assisted Delay-less Service Discovery (LDSD) Method Service discovery in IoT environment is an instantaneous process. The end –user devices broadcast service discovery message to its like neighbors or infrastructures. The request of the user is processed at different levels till the service repository is reached. The different levels of request processing include request forwarding and buffering to retain the liveliness of the request.
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Let N represent the number of available resources and 𝑆 denotes the services leased by the resources. The resources are distributed across a geographical region in the form of groups. The resources present in the same region that are equidistance to the end – user are considered as a single group 𝐺. The number services expected from a specific group is thus ∑𝑥𝑖 1 𝑁𝑖
𝑆 . Let
𝐺 𝑆
𝛼∗ 𝑠 𝐺 ∗ 𝑠
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∅
of
𝑅 be the number of requests processed in available 𝑁 for 𝐺 , then
(1)
Where ∅ , is the service mapping cost, 𝐺 is the number of resource groups, 𝛼 is the number of service requests and 𝑠
is the average 𝑆 in a 𝐺. Let 𝑑 represent the distance between the user
and service group 𝐺 that is estimated using equation (2) 𝑥
𝑥 𝑥
𝑥
Where 𝑥 , 𝑦
𝑦 𝑦
𝑦
𝑦
and 𝑥 , 𝑦
, if the user is fixed /static 𝑡
𝑣 , if the user is mobile
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𝑑
(2)
are the position co-coordinates of the end – user and service 𝐺 .
Here, 𝑡 and 𝑣 denote the service request transmit time and user velocity, if the position of the
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user is subjected to change. If the end- user is present within the service group, then request message reaches the service resource repository in time. If the end-user is located for away or possesses mobility, then it relies on infrastructures and other like devices for service request processing. For achieving a timely request mapping for a user separated by 𝑑, ∅ must be achieved as minimum satisfying 𝐺
≃
√𝛼 𝑆 |𝑡𝑠 𝑡𝑟 |
Such that 𝑠𝑎𝑣𝑔 ≃ max
∗
(3) (4)
The prime goal in achieving maximum 𝐺 in minimum distance is the delay factor. As discussed, end- users expect minimum cost and service distance resources for delivering efficient services. Let 𝑡
be the maximum liveliness period of the user service request. Irrespective of the delay
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due to congestion and traffic in the shared environment, the request is to be served (allocated with resource) before 𝑡
time i.e. 𝑡
. Starting from 𝑡 , the service response time 𝑡
must be less than its liveliness
𝑡𝑚𝑎𝑥 . To prevent congestion, the request processing devices and infrastructures use
backoff propagation. In backoff propagation, the device/ infrastructures buffer the request until the wireless channel is free. If 𝑡 is the back off time for a request, 𝐺 at minimum 𝑑 is responded
Journal Pre-proof within 𝑡𝑠
𝑡
. The backoff time relies on the channel utility for the shared group G.
𝑡
From the intermediate device/ infrastructure perspective, the number of requests handled 𝑅 is computed using equation (5) 𝑅
,𝑁 𝜖 𝐺
∑
(5)
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Where 𝑅 are the numbers of requests received and transmitted to the same 𝑅 respectively.
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Let 𝑐 represent the capacity of the infrastructure / the device processing the request of an enduser. The time at which the request is delivered to the 𝑅 ∑
ℝ𝐷
∑
∗
, ℝ 𝐷 is computed as
ℝ 𝐷 endorses 𝑡 , 𝑡 provided ℝ 𝐷 is valid for requests processed within 𝑡
(6) . Let 𝑙 represent the
levels such that the transmission delay 𝑡 𝑡
𝑙
∑
1 ∗ 𝑡
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neighbor in the levels of request processing between the end-user and 𝐺 . The 𝑡 is estimated for all the
is estimated as
𝜌 ∗ 𝑡
level. Based on equation (7) there are two possible
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Where 𝜌 is the backoff probability in the 𝑖
(7)
corollaries (i.e.) request processing with backoff and without backoff. Corollary 1: The condition when backoff probability is false i.e. 𝜌
0
Analysis 1: If the backoff probability is 0, then the service rate of 𝑅
by the device/
infrastructure is seamless. The 𝑐 of the device is continuously utilized for relaying service requests such that ∑ Therefore, 𝑡 where 𝜌
𝜌 ∗ 𝑡 𝑙
0.
1 ∗ 𝑡 . Considering ℝ 𝐷
0, equation (6) is given as
ℝ𝐷
∗ 𝑡 ∗ 𝑙
1
If the distance is minimum, where 𝑙
Therefore, selecting a G that ensures 𝑡
𝑠
(8)
2(the minimum) then
∗ 𝑡
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ℝ𝐷
The 𝑠
for a single level of transmission
𝑡
(9)
is sufficient for maximizing ∅𝑐 .
required in this case is thus estimated as 𝑆
𝑅
(10)
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Substitute equation (9) in equation (1) 𝐺𝑛 𝑅 𝐺𝑛 𝑆 , 𝛼
(11)
𝑅
Is the minimum cost for mapping service if 𝑡 equation (11) is satisfied for 𝜌 Corollary 2: 𝜌
𝑡
. Therefore, an N with ∅ of the above
0 where ℝ 𝐷 is the least delivery time of the request (equation (9))
1
Analysis 2: When 𝜌
1 , the chances of backoff time for a request are experienced by the neighboring
devices/infrastructure. In this case, equation (7) holds for all 𝑙
∗ 𝑡 𝑆
Here, 𝑆
1 ,𝑡
𝑙
1 ∗ 𝑡
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2 . Therefore ℝ 𝐷 is estimated as ℝ𝐷
of
∅𝑐
𝑡
𝑡
𝑖𝑓 𝑙
(12)
𝑅 as the number of handled requests is estimated post backoff. Therefore, the cost
∅
𝑅𝑛 ∗ 𝑆 𝑅 𝑛
𝐺
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of service mapping from equation (1) is represented as
(13)
Equation (13) is valid if there exists a 𝑡 at any level of request processing. Figure 2 represents the 0 𝑜𝑟 1.
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ℝ 𝐷 when 𝜌𝑖
𝟎 𝒐𝒓 𝟏
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Figure 2 Level 2 Based Representation of ℝ 𝑫 for 𝝆𝒊 Based on 𝜌
1 𝑜𝑟 0, the service is discovered with the awareness of its position 𝑥 , 𝑦 . The N in G
advertises position for the requesting users. With the previous experience of the users, the position of N in G with ℝ 𝐷 is differentiated with respect to delay estimated using equation (9) and (12). The
end- user request processing neighbor device/ infrastructure estimates the ∅ in both cases considering 𝜌 , irrespective of the location and distance. The location of the N with lesser ∅ is preferred
Journal Pre-proof by the end –user. If the S leased by N in G overflows 𝑅𝑛 , the next feasible location of next N in
another G is opted for 𝑅 processing. The location aided service discovery proceeded by ∅ and ℝ 𝐷
evaluation results in
reliable responses to the end –users. In this context, the replication factor of the resources is
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accounted to improve the consistency of S mapping within a located G. Based on the ∅ , replication is facilitated for improving service reliability. 3.2 Cost Effective Service Replication back off time (i.e.) 𝑡
𝑡
0𝑟 𝑡
𝑡
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The corollaries 1 and 2 are discussed with respect to backoff probability. The adversary impact of
𝑡
is mitigated by replicating services among the
available 𝐺 to retain lesser ∅𝑐 . Replicated resource access must also ensure lesser ∅ despite the 𝑙 in resource mapping and service request processing Replication of resources in successive G is followed by resource un-availability in the previous G. The resource unavailability 𝑅
𝑅
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equation (14) 1
Where 𝑟
and 𝑟 are the active and idle time of a resource. If the 𝑅
is computed using
(14) is true for either 𝑅 or
user.
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𝑅 , then replication of services occurs in the nearest G to prevent additional 𝑡 for the end – The rate of replication increases ∅ with increase in G with the same services. For 𝑅 or 𝑅 requests the required replicas is thus estimated as arg min 1
∏
𝑅
Now, the availability of the resource 𝑅 𝑅
∑
∑
Where 𝑑𝑖
.
𝐺, 𝐺
∏
𝑖 , 1
𝑅
𝑖 …, 1
∏
𝑅
𝑖
(15)
is estimated post the replication process as
. 𝑑𝑖
𝐺, 𝐺
(16)
is the diversity distance function representing the d between first G and
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replicated G. If the replicated resource is available in the same level of the location of N, then response time for mapping a resource to the request is estimated as 𝑡
∗
∑
Where 𝑡 is the resource mapping time and 𝑘 ∈ 𝑅
(17)
∗
𝑅 .
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For the 𝑅 in a G, the time taken to map a resource is ℝ 𝐷 replicated resource it is 𝑡 𝑡
. Obviously, the time 𝑡
whereas for a migrated/
must also ensure lesser ∅ satisfying 𝑡
𝑡𝑚𝑎𝑥 . For this purpose, the stability of the allocated resource in G in terms of delivery
delay and response are verified. The minimum number of replications ensures seamless service 𝐵
𝑅𝑒𝑝
𝑠
of
of 𝑅 requests is computed using equation (18) 𝑡
Where, B is the bandwidth of the wireless link.
replications of N in a new G must be 𝑡
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The time delay for 𝑅 with 𝑅𝑒𝑝
(18)
instead of its
delivery delay ℝ 𝐷 as in equation (8) or (12). Therefore, the expected delay for request delivery ℸ 𝐷 for replicated resources is ℸ𝐷
∗
𝑐 ∗ 𝑠
relies on the 𝑅𝑒𝑝
is active therefore, the response time will be 𝑡
the first replicated service mapping, 𝑡 𝑡
𝑡𝑜 . This proves that all 𝑅 will undergo corollary 2 conditions. Contrarily, the 𝑅
implies 𝐺
is 𝑅 and hence, ℝ 𝐷
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equation (12) for 𝑅𝑒𝑝
∅
of the resource for 𝑅 requests. In
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The factor 2 1
(19)
𝑅ℎ ∗ 𝑆 𝑅 ℎ
𝐺
∗ 𝑡
𝑆
𝑡𝑜 and 𝑠
𝑆
in
𝑅 that
(20)
For the offloaded service request and resource mapping in the newly replicated G, cost is given by equation (20) and 𝑡
𝑡
𝑡𝑜 for the delivery time as in equation (19).
The user imitates a request for service discovery that is forwarded to the destination through devices/ infrastructures. The resource is initially discovered based on distance. If the number of user request exceed the 𝑐 of the resources, then the nearest replicated G is searched for service mapping. The numbers of replicated resources concede mapping of requests without higher 𝑡 . If the request has to be processed with multi- level, then the next nearest resource is
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mapped. In both cases of resource mapping, the request delivery time is estimated based on their 𝑡 . If 𝑡 exceeds 𝑡
then the request is dropped, to prevent this request is forwarded to
the replicated G. The response time of the resource is determined based on its availability and service mapping cost. To satisfy 𝑡 ∅ such that 𝑡
𝑡
𝑡
and 𝑡
𝑡
, the requests are mapped to a resource with lesser is minimum.
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4. Performance Evaluation The working of the proposed LDSD is evaluated through an IoT integrated pervasive computing model for information sharing applications. The information sharing is modeled to
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query a resource that is accessible from an open source cloud platform. The resource is a coupled server and storage database with different application service information [34]. The model is and their configurations are presented in Table 2.
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constructed using Opportunistic Network Environment (ONE) simulator. The model parameters Table 2 Model Parameters and Configuration
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Number of devices Gateways Request/Sec 𝒕𝒎𝒂𝒙 Replications 𝒓𝒊𝒕 Bandwidth
Configuration Device Resource 100 10 16 50 3000 600ms 12 24ms 2Mbps 100Mbps
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Parameter
The performance of the propose LDSD is compared with the existing MSRP [32], PSCP-DPS [25] and CIR [33] for the metrics: response time, failure rate, resource utilization, request delivery time, resource availability and service latency. 4.1 Response Time Analysis
This ensures the least response time due to earlier request delivery. To ensure 𝑡 𝑡
, the overflowing requests are mapped to replicate G that ensures 𝑡
𝑡
𝑡
.
Therefore, delivery of requests in both G and replicated G is less. Instantly, the available resource is mapped to the request minimizing the response time [35]. The 𝑡 observed for 30
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service request is less and hence the response time is less compared to other request densities. The above two factors minimize the response time of the varying requests is the proposed LDSD.
Journal Pre-proof
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Figure 3 Service Requests versus Response Time Figure 3 illustrates the comparison of response time between the existing methods and
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proposed LDSD. IN the proposed LDSD, distance based service discovery is performed [36]. The resources with shorter 𝑑 are opted by the end- users either in 𝑙 or 𝑙
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4.2 Failure Rate Analysis
1 .
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Figure 4 Service Requests versus Failure Rate Figure 4 portrays the comparison of failure rate between MSRP, PSCP- DPS, CIR and the proposed LDSD respectively. The number of unmapped or expired requests 𝑡
𝑡
𝑡 𝑜𝑟 𝑡
in the proposed method is less. This is achieved by minimizing the delivery time
of
mapping cost. The neighboring devices/ infrastructure is segregated based on levels with the estimation of distance metric. Similarly, the request is prevented from queuing for a long time by selecting cost effective replicated G. For varying request density, the resource is either directly and ℝ 𝑑 , for replicated N) is assigned. The
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(based on d and ∅ ) or indirectly (based on ∅
instantaneous process of resource mapping for any number of requests with ∅ and ℝ 𝑑 , awareness of LDSD ensures lesser failure rate of the requests.
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4.3 Resource Utilization Analysis
Figure 5 Response Time versus Resource Utilization
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A comparative analysis of resource utilization between the existing methods and proposed LDSD is illustrated in Figure 5. Resource utilization is accounted based on the maximum service requests mapping in replicated and non – replicated G. The incoming requests are mapped with the resources in the G, with minimum failure rate. As the failure rate is less, the ratio of utilized resources is high with lesser ∅ . The overall response time for the entire request
Journal Pre-proof
varies with ℝ 𝐷 and 𝑡
in mapping S from resources. This improves the resource utilization
of the cloud with lesser 𝑡 and satisfying 𝑡
𝑡
𝑡
.
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pro
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4.4 Request Delivery Time Analysis
Figure 6 Devices versus Request Delivery Time
The request delivery time in the proposed LDSD is more precise by differentiating the liveliness of the request. Request liveliness is maintained by mapping resources instantly from G or replicated G. Initially, the delivery time of the requests is less as resource is discovered with the consideration of d and ∅ if the request density due to increased device increases, d cannot be retained and hence ∅ and ℝ 𝐷 are accounted. Therefore, though the request is directed to the replicated resource, minimum ℝ 𝐷 based mapping ensures lesser request delivery time in the proposed LDSD (Figure 6). In the Figure 6, when device = 80, delivery time is observed to be
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high where 𝑡 of the request is high.
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4.5 Availability Analysis
Figure 7 Replicated Resources versus Availability
Figure 7 portrays the comparison of resource availability between the existing methods and the proposed LDSD. The resource replication is planned to satisfy to or 𝑡
𝑡
𝑡
and minimum ∅ . The average services in G is pre- estimated with the density of requests and services, Similarly, 𝑅 number of requests are to be serviced through replication; the replication is not allowed for all 𝑅 . The rate of replication satisfying 𝐺 and 𝑅
is ensured for 𝑅 such that 𝑅𝑒𝑝
is allocated for 𝑅 requests
is required to deliver requests with ℸ 𝐷 delay. 𝑡
. Unlike the
previous methods, replication is ensured with minimum ∅ for serving 𝑅
instead of 𝑅 .
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This delay ensures the liveliness of the requests 𝑅 Therefore, the number of serviced requests is also high.
such that 𝑡
𝑡
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4.6 Service Latency Analysis
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Figure 8 Replicated Resources versus Service Latency The comparative study of latency for the replicated resources is illustrated in figure 6. The proposed LDSD achieves an average latency effectiveness compared to the existing methods. This is because, LDSD requires 𝑅𝑒𝑝
for serving 𝑅 requests. The number of
requests exceeding 𝑐 of the N, is distributed among all the non-replicated and replicated resources for delay less response. The replicated resources are minimum, then proposed method achieves lesser latency. In other average replications, due to minimum ∅ and lesser ℝ 𝐷 , LDSD requires additional time for mapping the resources. Comparatively the proposed LDSD achieves quiet less latency for the varying replications. 5. Conclusion
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In this manuscript, a location assisted service discovery method for improving the reliability of IoT system is introduced. This service discovery method is designed for improving the reliability of request processing by minimizing response delay and resource mapping cost. The design goal of the proposed method is differentiated between backoff and normal requests by improving the resource availability rate. Resource availability is improved by placing minimum cost effective
Journal Pre-proof
replications that ensure minimum service request delivery and lesser response. The overall performance of the service discovery and request mapping process is evaluated through a simulator model. The results show the consistency of the method by improving resource availability and utilization rate and minimizing response time, failure time, request delivery time
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and service latency. In future intelligent and optimization techniques are used to optimize the system performance effectively.
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Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1439-053. References
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LOCATION ASSISTED DELAY-LESS SERVICE DISCOVERY METHOD FOR IoT ENVIRONMENTS Ahmad AlZubi1, Abdulaziz Alarifi2, Mohammed Al-Maitah3*, Omar A. Albasheer4 Computer Science Department, Community College, King Saud University, Saudi Arabia; 4 Al dar University College, School of Engineering and Technology *Corresponding Author: [email protected]
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1,2,3
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The authors declare that there is no Conflicts of Interest
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Conflicts of Interest