Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes

Future Generation Computer Systems 98 (2019) 565–576

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Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs

Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes ∗

Manikandan Rajasekaran a , Abdulsalam Yassine b , M. Shamim Hossain c , , Mohammed F. Alhamid c , Mohsen Guizani d a

Department Department c Department d Department b

of of of of

Electrical and Computer Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario, P7B 5E1, Canada Software Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario, P7B 5E1, Canada Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844-1023, USA

highlights • • • • •

An autonomous monitoring system model for IoMT in healthcare facilities is proposed. The novelty of this work lies in the proposed energy distribution mechanism. A reward-based mechanism as a means of distributing the energy among the node. Utilizing the Analytical Hierarchy Process (AHP) to devise a reward-based scheme. The system could yield a higher life cycle of its node using a reward-based scheme.

article

info

Article history: Received 31 October 2018 Received in revised form 4 January 2019 Accepted 13 January 2019 Available online 4 April 2019 Keywords: Internet of Medical Things (IoMT) Autonomous sensor nodes Energy charging Healthcare Reward-based protocol

a b s t r a c t The Internet of Medical Things (IoMT) is an essential paradigm for ubiquitous monitoring in healthcare environments. The IoMT system collects data (e.g. temperature, hazardous contamination, light intensity, room and patient status, etc.) from connected medical devices and sensor nodes to a central or distributed computer network. In order for these devices and sensor nodes to continue operating, they must be charged with sufficient energy at all times. In this paper, we propose an IoMT system that employs autonomous mobile chargers, which is equipped with wireless energy transfer technology, to support sensor nodes recharging requests. In this model, the mobile charger must distribute the energy among the sensor nodes so that their operation is not interrupted. This is challenging because the mobile charger carries limited amount of energy that may not be sufficient to satisfy all recharging requests. In this paper, we propose a reward-based energy charging decision mechanism that allows mobile chargers and sensor nodes to coordinate the charging process. The proposed reward-based mechanism utilizes the Analytical Hierarchy Process (AHP) for fair distribution of energy among the nodes. This paper presents the theoretical analysis of the model and the simulation experiments. Our results show that the proposed model can support a larger number of active nodes with less energy compared to conventional first come first served methods. Also the coverage utility of sensor nodes is much higher using our method compared to the on-demand recharging request schemes found in existing studies. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The IoMT is a paramount paradigm for providing cost-effective solutions for people’s health and safety [1]. It is estimated that IoMT market will reach $136.8 billion globally by 2021 with ∗ Corresponding author. E-mail addresses: [email protected] (M. Rajasekaran), [email protected] (A. Yassine), [email protected] (M.S. Hossain), [email protected] (M.F. Alhamid), [email protected] (M. Guizani). https://doi.org/10.1016/j.future.2019.01.021 0167-739X/© 2019 Elsevier B.V. All rights reserved.

nearly 4 million connected medical devices [2]. These IoMT devices are not only providing ubiquitous monitoring of individuals and facilities but also are supporting care-providers with essential data to diagnose related problems and allow for timely intervention [3,4]. Furthermore, the evolution of IoMT has led to the mass deployment of sensor devices and nodes which provide the necessary infrastructure for data acquisition and connectivity [5– 7]. Recent development in sensor devices and network research including cloud computing and data processing is witnessing a dramatic movement towards developing IoMT platforms [8,9]. In such platforms, devices and sensors provide crucial services for

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caregivers that save lives and help maintain a healthy environment. However, the reliability of the IoMT platform depends on the ability of these devices and sensor nodes to provide updated information about the environment so that issues are solved promptly [10,11]. These nodes have in common that they are powered by batteries that keep them alive. However, the batteries add weight and cost to the node and most importantly are not practical in hospitals due to the necessity to check on them regularly or otherwise fail when they are critically needed. One possible deployment is to install sensor nodes capable of harvesting their energy sources from the environment. The shortcoming of this solution is in the unpredictability of the surrounding environment which adds unwarranted risks of interrupted monitoring services in healthcare facilities [12]. This paper addresses the need for innovative ways to maintain the life cycle of the sensor node infrastructures in healthcare facilities so that the IoMT platform continue to provide crucial services for caregivers. The challenges of recharging sensor nodes in an IoT system are discussed in great details in the work [13]. The investigation shows that there is still a large gap in developing systems that ensure energy efficiency. This is particularly crucial for enabling future technologies in the IoMT [14,15]. Several techniques were proposed in the literature to solve issues related to wireless charging of sensor nodes in which a mobile charger travels from a service station and visits all nodes in the network. However, most of the studies show that sensor nodes suffers from charging efficiency due to the distance travel and path selection [16]. This has lead several researchers to consider prediction on the life time of the sensor node before dispatching the mobile charger to the sensor node as described in [17]. By doing so, the mobile charger would improve its path to reach the nodes with lower life time. Other approaches (e.g. [18]) propose on-demand policy where the mobile charger travels in a hob-by-hob fashion to the neighbor nodes of the lowest energy level. Other studies (e.g. [12, 19–21] and [22]) propose solutions based on routing strategies, enhanced protocols and clustering techniques. However, with limited amount of mobile charging capacity and the overhead computations, we need a much more efficient solution especially in critical deployment (e.g. hospitals) with stringent requirements where several nodes are considered energy-critical. Some of the open questions include: How to determine which sensor node should be charged first and how to balance the distribution of energy in a fair fashion among the sensor nodes. Contrary to the existing methods, our solution is based on a much more detailed distribution of energy among the sensor nodes. We consider the performance of the sensor node before deciding on the amount of energy to be given. For this approach to work we propose a reward-based mechanism where energy is distributed among sensor nodes following a priority determination process. Our mechanism assesses the profile of parameters of the sensor node and compare it with an expected performance. The result of this comparison provides a ranking/score of energy consumption that later used to determine the amount of charge. In this regard, our model allows the mobile charger to determine the priority of the nodes that need to be recharged so that their life time is extended. Another important aspect of our model is in the ability to compare the energy consumption profile of the node after each recharging process, which lead to the detection of anomaly performance that might indicate a failure or error in the node. This is an important aspect in our model that saves energy and reduces catastrophic failures in the IoMT system [15]. This paper is structured as follows: In Section 2, we discuss the related work followed by detail discussion on the system model, interaction protocol, system parameters, and the analytical hierarchy process in Section 3. Section 4 provides the evaluation results of the proposed system model. Finally, in Section 5 we summarize the findings and provide direction for future work.

2. Related work The main building blocks of the IoMT-based healthcare monitoring system is founded on integrating several components including sensor nodes, actuators, mobile devices, etc. that are connected through the Internet for a common objective [3,23]. Several studies investigate possible system deployment and architectures. Also, improvement of the lifetime of sensor nodes in IoMT environment is the subject of attention in the literature. In this section, we discuss these studies especially those that are representative of the state-of-the-art and close to our work. We will also discuss the techniques and approaches of re-charging sensor nodes using wireless charging techniques. The work presented in [1] proposed an IoMT model powered by sensor nodes. The study proposes new IoT architecture enabled by Radio Frequency Identification (RFID) and sensor nodes for the consistent and secure exchange of data for medical environments. Similar to [1], the study in [7] discussed medical applications of the IoMT architectures and their integration with edge computing [24]. In [25,26] and [27], the authors discussed several IoT models for monitoring the environment using wireless sensor nodes. Although these studies are not particularly investigating the medical application of the proposed models, the technologies are applicable for such a setting. In [28] a software and hardware system implementation of sensor nodes was investigated to monitor and measure the healthcare environment and collect information within the facility. The data is used to alert facility personnel on events and issues when needed. In addition to the studies presented above, several approaches were proposed to extend the life of the sensor nodes in the IoMT system. Among these approaches is the use of wireless chargers via magnetics-resonant coupling as described in [29]. According to his proposed method both the charging node and the node that is receiving the service should contain inductive coils such that both resonate at the same frequency. However, a potential problem could arise from strategically placing energy transmitting nodes in the system in order to provide wide coverage and also maintain efficiency. The author in [30] provided a method to optimistically place wireless charging nodes such that maximum nodes are covered while charging efficiency is maximized. The author proposed a swarm intelligence based algorithm for decentralized, self-organizing system. Wireless Rechargeable Sensor nodes usually consist of the normal sensor node and a Wireless Charging Node. In [31], the authors proposed a technique to improve the lifetime of sensor networks by dedicated energy transmitters using task-based energy charging schedules to provide energy requirements to other nodes. The proposed system aims at covering all the sensors in the range of the energy transmitters and minimizing the number of energy transmitters and energy consumption of the transmitters. Other approached include energy harvesting from the environment. However, as we mentioned earlier in section ‘‘Introduction’’, energy availability in these systems can be uncertain and intermittent ending up having a set of nodes that are well charged while a set of nodes may not be able to harvest the charge at all. To solve this problem, the research in [32] introduced a concept called Dynamic Energy Trading (DET) which takes advantage of the wireless communication between the nodes. In this model, nodes may have different hardware to harvest energy from its surroundings, and the nodes that harvest more energy can sell to the needy nodes. For methods involving a charging vehicle that travels to the location of the node and provides charge, several studies address problems related to the path which the charging vehicle must take as well as the scheduling of the charger. For example, the studies in [33] and [34] provide new methods for scheduling and coordinating mobile charger based on the demands of the

M. Rajasekaran, A. Yassine, M.S. Hossain et al. / Future Generation Computer Systems 98 (2019) 565–576

nodes in the network. These methods divide the sensors into groups based on priority such that the charging node can provide services to the nodes based on the urgency of the request. The authors defined the methods on improved models such that whenever the energy of a node falls below a particular threshold value, the node reports its status to a base station which will then schedule a mobile charger to server the node. The mobile charger is given a time within which it should charge the requesting node and comeback. The number of nodes that the charger can charge during this tour is called the charging throughput. One drawback in these models is that the mobile charger does not take requests dynamically once it is set for a charging tour, but incoming requests are added to a charging pool which categorizes the urgency of the incoming requests. The work in [35] tackles the problem of coordinating and servicing charging requests from nodes using a reward-based algorithm in which the mobile charger maximizes the charging service for each tour. The model devises a mechanism for the mobile charger such that all nodes are charged to their full capacity regardless of the number of visits. The work presented in this paper falls within the theme of [12] and [20], however, the focus of this study in IoMT platforms where the criticality of the environment plays a major role in defining an efficient energy distribution. For this reason, we design a new interaction protocol, procedures, and algorithms to satisfy the sensor nodes in the system. Specifically, we discuss the granularity of energy distribution based on the node profile and its performance. And, we design a strategy based on the Analytical Hierarchy Process (AHP) to allocate energies from the mobile charger to the sensor nodes. To the authors best knowledge this is the first time in the literature that such an approach has been taken. Next, we provide details of the system model. . However, this model suffers from inefficiencies of energy distribution because the nodes are sometimes given more energy than what they actually needs. In the contrary our reward-based model determines the best allocation to the node regardless of the requested amount of energy. Therefore, the amount of energy that is given to the node is according to its actual need and not to the maximum requested amount. Parallel to the above studies, several researchers investigated various models of wireless energy transfer to extend the life time of sensor nodes in IoT systems. For example, the work in suggested a modified MAX–MIN Ant System with Equalization recharging strategy to extend the lifespan of the sensor nodes. However, the authors relies on routing strategies and not on fair distribution of energy. While the mobile charger in their model can optimize the number of serving nodes, it does not optimize on the amount of energy distribution. Similar to [36], the work in [37] used an optimum storage-node placement for efficient and energetic wireless recharging which also lack the optimal distribution of energy among the nodes. In an effort to minimize the charging time and cycles, [16] suggested a routing method based on the shortest Hamiltonian cycle. The work in [17] suggested a prediction model to determine the amount of energy needed for each sensor node before dispatching the mobile charger. The model reduces the costs and energy consumption. The model relies on predicting the remaining lifetime of the node based on the energy consumption variation of sensor nodes. The accuracy of the model is the main issue in such model and require overhead computation of prediction algorithms to maximize the efficiency of energy distribution. Our model does not rely on prediction, but rather on the reported performance of each node which is more accurate as it represents the current state of the node. On-demand recharging is explored by [18], where the mobile charger traverse the hops of lowest energy nodes to reach its target. In [38], a mobile charger visits a cluster of nodes

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based on need where sensor nodes place themselves in clusters according to their energy capacity. In [39], the authors studied the optimal deployment of mobile chargers (drones) to maximize the efficiency of recharging sensor nodes. In this setup the drone chargers fly above the sensor nodes and transmit their energy wirelessly to charge their batteries. Although this approach is promising, the deployment of multiple drone mobile charger is not scalable in large setting of sensor nodes. In comparison with the above discussed model, we investigate a system of rechargeable sensor nodes and mobile chargers which are responsible for transmitting their available energy to deprived nodes. However, the focus of this study is on IoMT platforms where the criticality of the environment plays a major role in defining an efficient energy distribution. For this reason, we propose an efficient reward-based distribution model where nodes report their performance of energy consumption in each recharging cycle. By doing so, the system determines the actual performance of the node compared with the expected energy consumption and then determines the amount of energy needed to extend the lifetime of the node. Unlike existing methods, our model does not require prediction or routing strategies. The nodes are served based on the priority of their request. For such system to be realized, we design a strategy based on the Analytical Hierarchy Process (AHP) to allocate energies from the mobile charger to the sensor nodes. To the authors best knowledge this is the first time in the literature that such an approach has been taken. Next, we provide details of the system model. 3. System model Figs. 1(a) and 1(b) represent the high-level architecture of the IoMT and the infrastructure deployment of the sensor nodes in a healthcare environment respectively. The IoMT platform collects information from the sensor nodes, manages the devices, processes, and stores the data for healthcare applications. The sensing infrastructure consists of sensor nodes, base stations, and autonomous mobile chargers. These components are responsible for monitoring the environment. Each node is tasked to provide sensing information about its environment (e.g., temperature, humidity, hazards, movement, etc.) and communicates this information to the base station directly or through a relay station. The nodes are also required to report on their energy status to make sure their sensing assignments are not interrupted by lack of battery energy. The base station receives the charging request and relays it to the autonomous mobile charger which communicates with the nodes to learn about its status and consult the energy profile it stores to plan the charging process. The autonomous mobile charger in our model is equipped with higher capacity batteries and capable of transferring its power using a wireless link. We assume that the mobile charger charges itself through a docking station assigned specifically for that purpose or from a renewable energy source. However, there is a limitation to the amount of charge that it can hold and distribute. Therefore, it must implement a mechanism to be fair with all charging requests. In this paper, our focus on the design of such a mechanism, but before we get into the details, we start by describing the interaction protocol of our model followed by the system parameters. 3.1. Interaction protocol The interaction protocol shown is the mean by which the nodes communicate their requests for charging services. The design of the protocol takes into consideration the functionalities and the requirements to keep the whole IoMT system working at all times. Figs. 2(a) and 2(b) shows the messaging structure

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Fig. 1. (a) is a high level conceptional architecture of the IoMT platform, (b) is a floor plan of health institute with sensor nodes and mobile chargers.

between the node and the station as well as the messaging between the node and the mobile node respectively. The interaction protocol is as follows: Each time the power level of a node reaches a minimum critical value, a threshold BL, the respective node sends a RECHARGE REQUEST to the base station. The base station consults the information it has about the status of the node sends an acknowledgment RECHARGE REQUEST ACK to the node. Once the node receives the RECHARGE REQUEST ACK message, the node starts sending its energy profile since the last re-charge to the base station in a message NODE ENERGY PROFILE. The base station compares the energy profile against its expected energy profile which is generated based on the amount of energy it has given the node in the previous recharge. All parameters are compared and based on the difference; the base station produces a numerical measure (Score) to determine a priority scheme. Based on the score, the recharge request may be rejected or accepted. The base station may reject the request by sending a rejection message RECHARGE REQ RJCT to the node. However, there is a minimum fixed amount that will always be guaranteed to the node. Once the base station is ready to recharge it sends

RECHARGE REQ ACPT along with the amount of time it takes for the recharge vehicle to reach the node. The node sends the REMAINING ENERGY INFO which contains the time the node can stay alive. The node can opt to stay in sleep mode until the charging vehicle arrives based on the time that was sent in RECHARGE REQ ACT message. The base station based on the REMAINING ENERGY INFO collected from various nodes that have requested for re-charge chooses the order of serving. For example, the lesser the remaining energy and the greater the distance of the node from the charging vehicle, the higher the priority. The base station sends the charging vehicle to the node in the order of decreasing priority. The charging vehicle reaches the node and sends RECHARGE IND to the node, indicating that it is available for the recharge process. The node responds with RECHARGE IND ACK, and the charging vehicle recharges the node. Once the recharge is complete, the mobile charger sends a complete process message RECHARGE CPLT IND to the node along with the expected energy consumption. The node sends the current energy level to the base station to keep track of how much energy is spent on each node. The mobile charger then travels to the next

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Fig. 2. Interaction protocol between the nodes, station and mobile charger.

node, and the process continues. Next, we discuss the system parameters. 3.2. System parameters and energy representation In conventional recharging of sensor nodes, when a node requests for a recharge, the mobile charger fully recharges the battery of the node. In situations where the energy of the mobile charger is limited, we need to distribute the energy to the nodes in a fair and balanced fashion. Currently, there is limited work on mechanisms that determine the amount of charge to be given to the node and whether the node is efficiently consuming the energy or it is getting wasted due to malfunction or other reasons. In this paper, we specify certain parameters that system should log from the nodes to determine the energy consumption by various components and analyzes them to determine the amount that is a reflection of the present as well as the past energy performance of the node. These parameters will then be used to create a profile for the node for further assessment by the reward-allocation mechanism. Several parameters contribute to the operation of the sensor node and its consumption of energy [40,41]. The following are the main parameters/components of the node that contribute to energy consumption. Energy for transmission(et ): Transmission of data involves converting the digital sensor value to analog value, modulating the signal, transmitting it through the antenna. Apart from the basic transmission process, the transmission may involve retransmission based on the underlying transport protocol. Hence for transmission of a single byte involves some energy, if the message is re-transmitted, then the energy consumption is more. The transmission energy profile gives an idea about the environment in which the node is present. In other words, if the environment is bad, then it leads to data loss and consequently to multiple re-transmissions consuming more energy. Since measuring of energy spent for the transmission of each byte is difficult, the

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total energy spent /total number of bytes transmitted gives a rough estimate [41]. Energy for sampling (es ): Sampling is the acquisition of data at specific intervals of time. It involves activating of the sensor, converting and processing the sensor data to digital format and storing them in the memory if needed. Since sampling involves activating various circuits for taking the actions mentioned above, it requires a considerable amount of energy. Generally, the greater the sampling rate, the greater will be the amount of energy consumption [42]. CPU energy consumption(ec ): The node itself is based on a micro-controller. The micro-controller consumes energy depending on various states of the CPU such as active, power saves, and sleep modes. The CPU is switched to various states periodically during the life cycle of the sensing assignment [43]. Energy for reception(er ): Reception of data involves listening to the channel acquiring the signal, amplifying, and converting it to digital format. Reception of data generally means that the node is expected to forward the data from some neighboring node in a multi-hop communication. The sensor nodes generally lay low and do not communicate with each other. Since measuring the energy spent for each byte received is difficult, the total energy spent on reception gives a rough estimate. Energy for memory access(em ): More energy is spent for writing or reading energy from the memory. Also, the initialization of memory consumes maximum energy and occasionally whenever a sensor node goes to sleep or when the power is extremely low, the sensor node can save the currently sensed value in the memory before the sleep mode. Then, while in the waking up, instead of turning on the sensor, it can read the value from memory and transmit it. Such process allows to greatly saves energy. This type of approach is applicable only in situations where node density is higher such that the values average out and the sensed parameters do not vary rapidly. Energy for initialization(ein ): This rarely happens, during initial node startup or in the case where the node is reset to handle any erroneous event. In initialization, the node consumes a high amount of energy, as it involves memory initialization and reading from memory. Energy spend by sensor module(esm ): The sensor module itself consumes energy. This is inevitable and accounts for 60% of the energy consumed [41]. As mentioned earlier, the above parameters are used to construct a profile for each node. The node profile is then used to determine the amount of recharge each node is expected to receive from the mobile charger. Formally, let N = {1, 2, 3, . . . , n} be the set of nodes and E be the total available energy carried by the mobile charger MC . Without loss of generality, we omit the mobile charger’s need for energy during its tour from one node to another. The total energy E is divided into two parts, a minimum fixed amount of energy fi is granted to each node upon request regardless of its performance and an mount vi allocated to the node based on its energy profile such that E = ∑n i=1 (fi + vi ). Each node i ∈ N consists of a set of components i = {et , es , ec , er , em , ein , esm } which represents the energy profile of node i. Let ei,exp and ei,act the expected and actual energy consumption values for the energy profile of node i. Ideally, the expected energy profile is generated by the base station while the actual energy consumption is reported by the node and satisfies the following inequality ei,exp ≤ ei,act ≤ ei,exp , where ei,exp and ei,exp represent the minimum and the maximum expected energy consumption of the node respectively. In the case where this condition is not satisfied, then energy for that particular component shall not be allocated. Our system compares between the expected energy profile and actual energy profile to arrive at a re-charge amount that can be incremental or decremental.

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Now, we need to elaborate on the granularity of each component of the energy profile. For any components k ∈ i the following also holds ek,exp ≤ ek,act ≤ ek,exp , where ek,exp and ek,exp represent the minimum and the maximum expected energy consumption of component k ∈ i respectively. We define Sk as the score for each component performance and it is given by Sk =

ek,act − ek,exp ek,exp − ek,exp

(1)

The score Sk is used to determine the fair amount of allocating energy to the node based on the energy consumption score for each component. We propose to use the Analytical Hierarchy Process (AHP) [44] to determine the proper distribution of energy based on weights of priority vectors for all components. Next, we provide the details of the AHP method and illustrate its use in our system. 3.3. Analytical Hierarchy Process The AHP method is a tool design for complex decision making where the user can initiate the priorities to reduce the complexity [44]. Specifically, the method aims at deriving scale values of the decisions from a series of pairwise comparison procedures. The user uses subjective and objective assessment for ranking the options. While subjective values are prone to inconsistency, the method embodies a checking process to reduce the miss-judgment in the decision making. AHP examines specific evaluation criteria of pairwise comparison and produces a weight value. Then it ranks the weights to determine the priority and assigns a score to rank the performance of each decision. The final score is the weighted sum for all the criteria. There are three stages in the AHP method as follows: Determining the criteria weights and the pair-wise comparison, calculating the score matrix and prioritizing the components. Pairwise comparison Each component of the sensor node is taken, and a pair-wise comparison is made between every other component with a linear scale with defined criteria. Then, an mxm matrix A is calculated, where m is the evaluation criteria (e.g. a scale from 1 to 9). Each entry axy of the matrix A indicates the importance of the xth criterion in comparison to the yth criterion. Let us take the components energy for transmission and energy for sampling. The higher priority component is kept on the left side of the scale and the lower priority component is placed on the right side of the scale. The scale is calibrated on both the halves of the scale with the center value being 1, which means that both are equally important. For example, the scale can be calibrated from 1 through 9, such that on both sides of the center point, numbers 1 through 9 are marked. Now based on the priority of the component, a number is chosen. In our case, energy for transmission is of very high importance than energy for sampling. Hence, 9 is chosen from the left side of the scale. Generally, if axy > 1, then the xth component is more important than the xth component and vice versa. If two components have the same importance, then the entry axy is 1. The values axy and ayx satisfy the following condition [44]: axy .ayx = 1

(2)

Calculating the priority score matrix The comparison values of the components are represented in matrix B, which is consists of different criteria. The matrix B is nxn where n represent the components of the sensor node. Each component is evaluated based on the scale describe above. For example, if bwz > 1 then the wth component (e.g energy for transmission) is valued more than the zth component (energy for sampling) and vice versa. If two components are of the same importance with respect the

evaluation criteria, then the entry to matrix B is 1. The values axy and ayx satisfy the following condition [44]: bw z . bz w = 1

(3)

In our system we have several components i = {et , es , ec , er , em , ein , esm }. The diagonal entries of the matrix will be 1 since they represent the self comparison of the same components. The entries on the upper triangle of the matrix reflects the evaluation criteria discussed above with actual value is placed in the upper triangle is the evaluation falls in the left side of the scale and reciprocal value in the lower triangle if the evaluation falls in the right side. For the seven components in our system, the matrix B is then can be represented as follows: et et es ec B = er em ein esm

⎛ 1 ⎜ 1/b12 ⎜ 1/b13 ⎜ ⎜ 1/b14 ⎜ ⎜ 1/b15 ⎝ 1/b16 1/b17

es

ec

er

em

ein

b12 1 1/b23 1/b24 1/b25 1/b26 1/b27

b13 b23 1 1/b34 1/b35 1/b36 1/b37

b14 b24 b32 1 1/b45 1/b46 1/b47

b15 b25 b35 b45 1 1/b56 1/b57

b16 b26 b36 b46 b56 1 1/b67

esm b17 b27 ⎟ b37 ⎟ ⎟ b47 ⎟ ⎟ b57 ⎟ ⎠ b67 1

⎞

The next step is to prioritize the components as follows: Perform column-wise summation and to get the normalized relative weight, the sum is divided using the entries on each row, thus producing the priority vectors as follows: Components P1 P2 P3

⎛ ⎜ ⎜ ⎜ P= ⎜ ⎜ ⎜ ⎝

. . .

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

Pn Where Pl = 1/n(b1l + b2l + b3l + · · · + bll ) and n is the order of the nxn square matrix. The priority score is then used to determine how to distribute the energy among the components of the node based on the following: h=

k ∑

Pr .vi

(4)

r =1

where vi is the variable energy value for node i and the entries of h corresponds to the overall score given by the AHP to each component (i = {et , es , ec , er , em , ein , esm }) of the node. The last step is to arrange these scores in decreasing order. Next, we provide the procedures of determining the allocation of energy distribution. 3.4. Energy distribution procedures Fig. 3 describes the process for the sensor node to have its battery recharged by the mobile charger. Procedures 1, 2, and 3 describe the actual steps for the such process at each end. The sensor node continuously checks for the battery level if it hits the threshold BLi . If the its energy becomes low it will contact the base station requesting a charge service. Once it receives the acknowledgment for the charge, the node sends it profile of actual energy consumption since the last charge cycle. The energy profile contains detail information about energy spending for each component as described in procedure 1. The base station uses this profile to compare it with its expected energy profile. A score detailing the performance of the node is then created and used for AHP-based allocation. In procedure 2, the base station

M. Rajasekaran, A. Yassine, M.S. Hossain et al. / Future Generation Computer Systems 98 (2019) 565–576 Procedure-1: Charging Request Require: Active sensor nodes in the environment i ∈ N, Battery level threshold BLi , Current battery level BC i , components k ∈ i Ensure: Node checks for the battery level, communication link is active, components priority P established 1: Set BLi 2: Compare BC i and BLi 3: if BLi
Procedure-2: Charging Calculation by the Base Station Require: Charging Request from i ∈ N, Energy profile from i, Mobile charger capacity Q , Expected energy profile for node i Ensure: Mobile charger capacity Q , communication link is active, components priority Pk established 1: Check for the CHARGING REQUEST from i 2: if CHARGING REQUEST=TRUE then 3:

Calculate the SCORE Sk

=

ek,act −ek,exp ek,exp −ek,exp

of each component

{et , es , ec , er , em , ein , esm }from the actual energy profile gathered from the node i Compare the expected and actual energy consumption profile i Check for previous vi Calculate the energy that will be rewarded for each component using AHP 7: Generate the new expected energy profile 8: Send a TRIGGER to the mobile charger to RECHARGE node i 9: end if 4: 5: 6:

Procedure-3: Charging Process Require: TRIGGER request from the base station, Amount of energy vi , Location of node i Ensure: Mobile charger capacity Q , communication link is active 1: if TRIGGER message = TRUE then 2: Retrieve the location of the node i 3: Start the recharging process 4: On completion share the expected energy profile from the bases station 5: Send a TRIGGER to the mobile charger to RECHARGE node i 6: Update Q 7: Go to the next node 8: else 9: Check for new TRIGGER messages 10: end if

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conditions. The initial values for the energy setup of the node components are given in Table 1. For the AHP pair-wise comparison scale, we followed the same scale as in [45] which is shown in Table 2. When the sensor node battery level goes below the threshold value BLi , it requests the base station for recharge. The base station allocates the amount of energy requested by the node for the first mobile charging cycle. We assume that the mobile charger capacity is 20k joules. Initially we do not have any information about the node energy usage so the base station allocates charge equivalent to the battery capacity of the node. We assume that this initial value is 6k joules and the threshold is 1k joules. For performance evaluation, we first adopt a basic metrics to assess the proposed model. Number of nodes recharged per one cycle, it is defined as the number of nodes that are recharged in a single mobile charging tour. Higher the number of nodes recharged in a single tour, lesser is the chance of node to be lest out to fail. Energy consumption per one cycle, this is the amount of energy distributed over the course of mobile charging cycles. Faulty node detection, this is important in the case of autonomous monitoring in IoMT setup where the detection and identification of faulty nodes ensure the continuous gathering of critical data. We performed all our experiments in comparison with a typical first come first served (FCFS) method of charging sensor nodes without giving consideration to the priority or performance of the node. That is, each node receives the amount of energy its asks for in its request for charging. In the second set of evaluation, we compare our model with the work on-demand recharging strategy presented in [46]. The basic idea of the on-demand recharging strategy is to maximize the covering utility (CU) of the wireless sensing network. According to [46], the mobile charger controls a queue of charging requests from all sensor nodes. It is assumed that each sensor node will receive an amount of energy Eci = µi .Emc where µi is the efficiency of sensor node i and Emc is the working energy of the mobile charger. The time it takes for the mobile charger to complete the charging process is tci = Bimax

. All on-demand recharging requests arrive to the mobile charger following a predefined arrival rate based on a threshold factor of energy level for each sensor node. The Global Effective Coverage(GEC) at time t is defined in [46] as H(t) = ∪(A) where A ∈ N is the set of active sensor nodes and the global coverage (GC) is defined as M = ∪(N). The covering utility (CU) in [46] is the ratio between the Global Effective Coverage(GEC) and the global coverage (GC). µi .Emc

CU =

|H(t)| |M |

(5)

determines the amount of energy that the node should receive using AHP. This energy amount is then communicated to the mobile charger which plans its trip to the node and perform the actual charging process as described in procedure 3.

The basic idea of the on-demand strategy is to serve the nodes that maximizes the coverage utility (CU). The authors proposed a heuristic greedy strategy called Maximal Next Coverage Utility First(MNCUF) in which the mobile charger selects the requests from the queue that maximize the incremental effective coverage. Contrary to our proposed method, the mobile charger serves the sensor node that has the highest score of performance to be served next. As we will see in the results of the experiments, the proposed method outperform the on-demand strategy in terms of the energy distribution and time requires to serve the nodes.

4. Evaluation

4.1. Priority score and energy allocation

For the evaluation of the proposed model we consider a simulated setup of one base station, one mobile charger and several nodes in one floor. The nodes are randomly distributed in an area of 200 × 200 m2 where the simulation of the proposed model considers different scenarios. In order to ensure that the model is fairly evaluated all the tests are carried under the same

The first step is to calculate the priorities of the component and the score using the AHP method as explained in Section 3. This step is completed by the base station based on the initial values and the AHP comparison scale. Table 3 shows the priority vector based on AHP and explained in Section 3.3. Using the AHP method the distribution of the available energy to different

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Fig. 3. Procedures of charging sensing node using mobile charger. Table 1 Simulation parameters.

Table 3 Priority vector based on AHP.

Component

Node’s initial energy state (Joules)

Priority

Priority score

AHP components priority

Energy for transmission(et ) Energy for sampling (es ) CPU energy consumption (ec ) Energy for reception (er ) Energy for memory access (em ) Energy for Initialization (ein ) Energy for Sensor module (esm )

2627 1715 1590 939 698 255 172

P1 P2 P3 P4 P5 P6 P7

032 0.21 0.19 0.11 0.08 0.02 0.002

et es ec er esm em ein

Table 2 AHP comparison scale.

Table 4 Allocation of energy to node’s components based on AHP.

Scale

Degree of preference

Component

1 2 3 4 5 6 7 8

Equal importance Weak Moderate importance Moderate plus Moderate importance Strong plus Very important Very very strong

Initial energy allocation (Joules)

Actual energy consumed (Joules)

Energy for transmission(et ) Energy for sampling (es ) CPU energy consumption (ec ) Energy for reception (er ) Energy for Sensor module (esm ) Energy for memory access (em ) Energy for Initialization(ein )

1642 1072 994 587 436 159 107

1700 1200 1000 590 400 79.5 30.5

component using AHP priority vector is given in Table 4. The base station will then calculate the score as explained in Eq. (1) to determine the final amount of charge that the node will receive from the mobile charger in the first trip. The base station uses the expected and actual energy values to determine the score. This is shown in Table 5. 4.2. Performance analysis In this section, we show the results of evaluating the proposed model. The graphs are plotted to represent the performance with respect to the evaluation metrics described earlier in this section. The detailed performance analysis is as follows.

Table 5 Allocation of energy to node’s components in the first trip based on the node’s score. Component

Score (Si )

Energy allocation (Joules)

Energy for transmission(et ) Energy for sampling (es ) CPU energy consumption (ec ) Energy for reception (er ) Energy for Sensor module (esm ) Energy for memory access (em ) Energy for Initialization(ein )

0.42 0.58 0.99 0.97 1 1 1

691 631 990 572 436 159 107

M. Rajasekaran, A. Yassine, M.S. Hossain et al. / Future Generation Computer Systems 98 (2019) 565–576

Fig. 4. Amount of energy consumed over multiple charging cycles.

4.2.1. Energy consumption In this test, we evaluate the amount of charge that the mobile charger is going to provide to one node that is being serviced over multiple trips. The node initially gets its charge and then when it submits a second request its performance is evaluated to determine the appropriate amount of charge to be granted based on AHP. Considering this scheme, we are evaluating the total energy consumed using our method compared to a conventional method where no evaluation of performance is considered. Every node is given an amount equivalent to its request. From Fig. 4, we show the performance of the model after 6 cycles of charging requests. It is clear from the figure that the total energy consumed by the node is much less than using our method compare to the conventional method where energy usage is leading to wastage. 4.2.2. Number of nodes serviced In this test, we evaluate the number of nodes that could be services when the mobile charge has varying amount of energy available to provide to the nodes. We assumed that there are 20 nodes i the experiment requesting charge for each round. This is a realistic scenario simulating the case when the mobile charger could not save energy due to multiple requests. Fig. 5 shows the number of nodes being services when the mobile charger capacity of charge is 100%, 75%, 50%, and 25%. From the figure we can see that the proposed method outperform the conventional method significantly. The reason for this result is that the conventional method will give each node that amount it requested, but in our method the performance of the node determines if the node is getting the amount requested or less. The result of this process is being able to charge as many nodes as possible to extend their life cycle. 4.2.3. Faulty node detection As mentioned earlier it is important to identify if a sensor node is not performing due to a certain fault. Our method can detect that by examining the energy wastage of the node. We assume that the base station has a threshold of energy consumption performance that it uses to alert once the node hits this threshold after consecutive cycles. This is important because a failure of a single node can cause a chain of failures of other nodes leading to a much bigger issue in the IoMT platform. With the graph shown in Fig. 6, it is obvious that the node performance is constantly decreasing and needs attention after a threshold (1k joules). The method allows fault detection through recharge cycles itself thus eliminating a separate mechanism to identify fault.

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Fig. 5. Number of nodes charged versus the charging capacity.

Fig. 6. Detecting a Faulty Node based on the performance of charging and energy consumption.

Fig. 7. Number of nodes charged in one charging tour.

4.2.4. Number of nodes recharged in a single trip The number of nodes recharged in a single charging tour is of utmost importance because, most of the time, nodes fail waiting for the mobile charger. In the proposed method, as seen in Fig. 7, the number of nodes recharging in a single tour is almost twice the number of nodes charged using the conventional method. 4.2.5. Number of nodes recharged in multiple trips In Fig. 8, we examine the number of nodes recharged in multiple trips. We assume that there are 30 nodes in system 25 of them requesting recharge. The mobile charger carries an amount of 20k joules. As can be seen from the figure, using the proposed method we were able to show that the mobile charger can satisfy the requests of the 25 nodes in 4 trips. However, if

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Fig. 8. Number of nodes charged in one charging tour.

Fig. 9. Coverage utility versus the average arrival rate of charging requests.

the mobile charger uses the conventional method it will need 7 trips to satisfy the requests of 25 nodes. Clearly the IoMT system is better off using our method to make sure that the operation of the nodes are not interrupted while waiting for charging. 4.2.6. Coverage utility In this scenario, we consider a scenario of 100 sensor nodes distributed in the environment with 300 points of interest. The requests arrive to the mobile charger following a predefined arrival rate based on a threshold value of energy consumption which is 16% of the initial energy capacity. Figs. 9 and 10 represent the coverage utility of the deployment versus the average arrival rate of the recharging requests and the coverage utility versus the deployed number of mobile chargers respectively. In the first experiment, we examined the performance of the proposed methods against the Maximal Next Coverage Utility First(MNCUF) for the On-demand recharging request and the conventional first come first served (FCFS) algorithm. It is clear from Fig. 9 that our proposed method outperform both algorithms because our mechanism saves on energy and hence capable of serving more active node in the coverage area as we have previously shown in Figs. 5 and 7. In Fig. 10, we changed the arrival rate gradually and tested for different numbers of mobile chargers deployment. In this setup the coverage utility is higher for any given number of mobile chargers compared to both the on-demand and the FCFS algorithms. 5. Conclusion and future work In this paper, we presented an IoMT platform consisting of sensor nodes deployed in healthcare facility. The main issues of

Fig. 10. Coverage utility versus the number of deployed mobile chargers.

designing such system lie on making sure that the operational of the IoMT system are not interrupted due to the sensor nodes battery limitations. We proposes a reward-based mechanism as a means of distributing the energy among the nodes so that the life cycle is extended. We also proposed a protocol of interaction and evaluated the system against conventional FCFS methods and on-demand recharging requests that do not consider the performance of the node for energy distribution. The results of our work clearly show the effectiveness of the proposed method. Specifically, we show that energy distribution is more efficient and the number of nodes that can be served using our model is higher. Our future plan, is to extend the work to include an advanced optimized method of energy distribution with the inclusion of green energy harvesting. We will also investigate data analytical solutions to study the behavior of the nodes in the environment for better decision making by the mobile charger. This allows us to predict the failure rate and performance using data analytic solutions as in [47] and [48]. We also plan to include intelligent routing planning mechanism for the mobile charger to maximize the number of nodes serviced in the platform. Another venue of research is to explore the security and privacy issues that these devices might impose on the hospital environment. To address these issues we plan to explore mechanisms of data sharing and protection similar to the ones presented in [49–51]. Acknowledgments The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this research work through ISPP-121. References [1] A.J.J. Valera, M.A. Zamora, A.F.G. Skarmeta, An architecture based on internet of things to support mobility and security in medical environments, in: 7th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, 2010, pp. 1–5. [2] Market Watch, Internet of things (IoT) healthcare market is expected to reach $136.8 Billion worldwide, by 2021 Market Watch, 2016, last visited, Sept 21, 2018 https://www.marketwatch.com/press-release/internetof-things-iot-healthcare-market-is-expected-to-reach-1368-billionworldwide-by-2021-2016-04-12-8203318. [3] A.A.N. Shirehjini, A. Yassine, S. Shirmohammadi, An RFID-based position and orientation measurement system for mobile objects in intelligent environments, IEEE Trans. Instrum. Meas. 61 (6) (2012) 1664–1675. [4] M.S. Hossain, Cloud-supported cyber–physical localization framework for patients monitoring, IEEE Syst. J. 11 (1) (2017) 118–127. [5] R. Gunasagaran, et al., Internet of Things: Sensor to Sensor Communication, IEEE SENSORS, Busan, 2015, pp. 1–4.

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Manikandan Rajasekaran received his Bachelor of engineering degree in electronics and communication engineering in 2015 from TRP engineering college, Tiruchirappalli, Affiliated to the Anna University — Chennai and post graduate diploma in Wireless information networking in 2017 from Fleming college, Peterborough, Ontario, Canada. Currently he is perusing his master of engineering degree in electrical and computer engineering at Lakehead university, Thunder bay, Ontario, Canada. His research interests include wireless communication, wireless sensor networks, network security, optical communication, satellite communication.

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Abdulsalam Yassine received the B.Sc. degree in electrical engineering from Beirut Arab University, Lebanon, in 1993, and the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Ottawa, Canada, in 2004 and 2010, respectively. Between 2001 and 2013, he was a member of the Technical Staff in the Wireless Communication Division, Nortel Networks, and later at Alcatel-Lucent, Ottawa, Canada. From 2013 to 2016, he was a Postdoctoral Fellow at the DISCOVER Laboratory, School of Electrical Engineering and Computer Science, University of Ottawa. Currently, he is an Assistant Professor in the Department of Software Engineering, Lakehead University, Thunder Bay, Ontario, Canada. He serves on the editorial board of the ACM Transactions on Multimedia Computing, Communications and Applications as an associate editor. His research interests are mostly focused on behavior and predictive analytics, big data systems and networks, artificial intelligence, IoT system, smart cities, smart environments, and smart grids research and applications. M. Shamim Hossain [SM’09] is a Professor at the Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. He is also an adjunct professor at the School of Electrical Engineering and Computer Science, University of Ottawa, Canada. He received his Ph.D. in Electrical and Computer Engineering from the University of Ottawa, Canada. His research interests include cloud networking, smart environment (smart city, smart health), social media, IoT, edge computing and multimedia for health care, deep learning approach to multimedia processing, and multimedia big data. He has authored and coauthored approximately 2010 publications including refereed journals, conference papers, books, and book chapters. Recently, his publication is recognized as the ESI Highly Cited Papers. He has served as a member of the organizing and technical committees of several international conferences and workshops. He has served as cochair, general chair, workshop chair, publication chair, and TPC for over 12 IEEE and ACM conferences and workshops. Currently, he is the cochair of the 2nd IEEE ICME workshop on Multimedia Services and Tools for smart-health (MUST-SH 2019). He is a recipient of a number of awards, including the Best Conference Paper Award and the 2016 ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) Nicolas D. Georganas Best Paper Award and the Research in Excellence Award from the College of Computer and Information Sciences (CCIS), King Saud University (3 times). He is on the editorial board of the IEEE Transactions on Multimedia, IEEE Network, IEEE Wireless Communications, IEEE

Multimedia, and IEEE Access, Journal of Network and Computer Applications (Elsevier), Computers and Electrical Engineering (Elsevier), Human-centric Computing and Information Sciences (Springer), Games for Health Journal, and International Journal of Multimedia Tools and Applications (Springer). He also presently serves as a lead guest editor of IEEE Network, and IEEE Access. Previously, he served as a guest editor of IEEE Communications Magazine, IEEE Transactions on Information Technology in Biomedicine (currently JBHI), IEEE Transactions on Cloud Computing, International Journal of Multimedia Tools and Applications (Springer), Cluster Computing (Springer), Future Generation Computer Systems (Elsevier), Computers and Electrical Engineering (Elsevier), Sensors (MDPI), and International Journal of Distributed Sensor Networks. He is a senior member of both the IEEE, and ACM. Mohammed F. Alhamid [M’10] is an Assistant Professor at the Software Engineering Department, King Saud University, Riyadh, KSA. Alhamid received his Ph.D. in Computer Science from the University of Ottawa, Canada. His research interests include recommender systems, social media mining, big data, and ambient intelligent environment.

Mohsen Guizani (S’85–M’89–SM’99–F’09) is currently a professor and the ECE Department chair at the University of Idaho. He currently serves on the editorial boards of several international technical journals including IEEE Wireless Mag., EIC of IEE Network Magazine, and the founder and the editor-in-chief of Wireless Communications and Mobile Computing journal (Wiley). He is on the Advisory board of IEEE IoT journal. He is the author of nine books and more than 400 publications in refereed journals and conferences. He guest edited a number of special issues in IEEE journals and magazines. He also served as a member, chair and the general chair of a number of international conferences. He was the chair of the IEEE Communications Society Wireless Technical Committee and the chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker from 2003 to 2005.