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The off-line scheduling scheme of existing wireless sensor network is improved in this paper. Firstly, Bayesian method is introduced and Poisson distribution parameter followed by the number of sensor nodes is regarded as a random variable. We estimate the parameter by Bayesian posteriori estimate and obtain Bayesian estimated correction value. Then, we discuss the relationship between instantaneous event capture probability, average capture probability, average capture energy efficiency and Bayesian estimate value of the distribution parameter. Finally, considering the fact that Bayesian estimation value can be adjusted automatically after posterior samples are included, we propose an online scheduling scheme for asynchronous wireless sensor network. Through simulation comparison between online scheduling scheme and off-line scheduling scheme, our results show that online scheduling scheme can lower the probability of failure to capture stochastic events, increase the probability of capturing events and further save the energy of wireless sensor network, and be more flexible to capturing stochastic events. Moreover, expanding the perception radius of sensors can also enhance capture efficiency on the basis of controlling the working duration of wireless sensor network. [ABSTRACT FROM AUTHOR]

• We give the definition of problem MBA and prove the NP-hardness of the problem. • We consider the MBA problem in three aspects and propose three approximation algorithms corresponding to these three cases. • We conduct extensive simulations to validate our algorithms. The results show the effectiveness of our algorithms. Schedule a mobile charger to replenish energy to sensor nodes for the wireless sensor networks has attracted great attention recently, due to its efficiency and flexibility. Some existing works study the mobile charger scheduling problem by considering that only the depot can recharge or replace the battery for the mobile charger. However, for large-scale wireless sensor networks, the mobile charger is energy inefficient or even may run out of energy during the travel for charging. In this paper, we consider the scenario that there are some service stations in the network area which can be used to replace the battery for the mobile charger, and we study the problem of minimizing the number of used batteries for a mobile charger to charge a wireless sensor network (MBA). We first consider a special case of the MBA problem, in which the depot is the only service station, and we present an approximation algorithm to address it. Then we propose an approximation algorithm for the MBA problem with the assumption that the distance of any two service stations is limited. And finally, we consider the general MBA problem and propose an approximation algorithm. We validate the performance of our algorithms by extensive simulations, and the results show that our proposed algorithms are promising. [ABSTRACT FROM AUTHOR]

• Wireless Charging is introduced to replenish battery of sensors. • Joint charging and rate allocation are considered to enhance network utility. • A layered multi-stage approach to maximize network utility is proposed. • A fast distributed algorithm is integrated to reduce communication overload and fast convergence. In wireless rechargeable sensor networks, how to optimize energy resources for maximizing the sensor data is a challenging problem. In this paper, mobile charging vehicle scheduling, sensor charging time splitting and rate control with battery capacity constraints are considered together to maximize network utility. However, they are considered independently in exist works even though these problems are interdependent. In order to improve network performance through collaborative optimization of three problems, a joint optimization problem is formulated firstly. Then, a multistage approach is developed to jointly optimize the three subproblems iteratively. Furthermore, an accelerated distributed algorithm is integrated to improve the convergence speed of rate control. The results of extended experiments demonstrate that proposed approach can obtain higher network utility and charging efficiency compared to other charging scheduling methods. [ABSTRACT FROM AUTHOR]

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things (IoT) topology has used to collect physical, physiological, vital signs of patients in consumer-centric e-health or consumer’ wellness care services. In such healthcare systems, varieties of medical sensors are attached to the patients to collect vital signs from those who are under observation. The data gathering process in IoT-enabled Wireless Sensor Network (WSN) suffers from the congestion problem. The effect of this translates on missing packets, a decrease of reliability and throughput degradation in IoT-enabled WSN. This article proposes a distributed congestion control algorithm for IoT-enabled WSNs to effectively resolve the congestion for healthcare applications. The proposed scheme alleviates congestion by a priority-based data routing strategy. Furthermore, this article presents a priority queue based scheduling scheme for better reliability. We analyze the properties of the proposed congestion control mechanism mathematically and validate its performance through extensive simulation and real-life experiments. The application of this work can be used to an early warning system in detecting abnormal heart rate, blood pressure, ECG, EMG in the hospital/home care environment to the state-of-art diagnosis. [ABSTRACT FROM AUTHOR]

Summary: Increasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree‐based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV‐based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics‐based technique of Local Path Reestablishment (LPR) and greedy‐based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments, and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments. [ABSTRACT FROM AUTHOR]

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption. [ABSTRACT FROM AUTHOR]

Based on fog computer, an industrial wireless sensor network (F-IWSN) is a novel wireless sensor network in the industry. It not only can more efficiently reduce information transmission latency, but also can more beneficially achieve the real-time control and the rapid resource scheduling. However, similar to other distributed networks, it also faces enormous security challenges, especially those internal attacks. The differences from those traditional security schemes are that, one is the trade-off between security, transmission performance and energy consumption to meet the requirements of information convergence and control, the other constructs a multi-dimensional selective forwarding scheme to achieve the real time transmission. In this paper, we propose a Gaussian distribution-based comprehensive trust management system (GDTMS) for F-IWSN. Furthermore, in its trust decision, the grey decision making is introduced to achieve the trade-off between security, transmission performance and energy consumption. The proposed trade-off can effectively select the secure and robust relay node, namely, a trust management-based secure routing scheme. In addition, the proposed schemes are also applicable to defending against bad mouthing attacks. Simulation results show that, the comprehensive performance of GDTMS is better than other similar algorithms. It can effectively prevent the appearance of network holes, and balance the network load, promote the survivability of the network. [ABSTRACT FROM AUTHOR]

A fundamental aspect in performance engineering of wireless sensor networks (WSN) is optimizing the set of links that can be concurrently activated to meet a given signal-to-interference-plus-noise ratio (SINR) threshold. The solution of this combinatorial problem is a key element in wireless link scheduling. Another key architectural goal in WSN is connectivity. The connectivity of sensor nodes is critical for WSN, as connected graphs can be used for both data collection and data dissemination. In this paper, we investigate the joint scheduling and connectivity problem in WSN assuming the SINR model. We propose algorithms to build connected communication graphs with power-efficient links to be scheduled simultaneously in one time slot. The algorithms aiming at minimizing the number of time slots needed to successfully schedule all the given links such that the nodes can communicate without interference in the SINR model. While power-efficient and interference-free schedules reduce energy consumption, minimization of the schedule length (shortest link scheduling) has the effect of maximizing network throughput. We propose one greedy randomized constructive heuristic, two local search procedures, and three greedy randomized adaptive search procedures metaheuristics. We report computational experiments comparing the effectiveness of the proposed algorithms. Our simulation also shows the trade-off between power consumption and schedule length and the results indicate that not only the overall performance of our algorithms, but also show that the total power and schedule length value of its solutions are better than the existing work. [ABSTRACT FROM AUTHOR]

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network. [ABSTRACT FROM AUTHOR]

• Polynomial time algorithm for the discrete speed model and fixed order of jobs. • Polynomial time algorithm for the sense-and-aggregate model. • Heuristic for the continuous speed model for arbitrary order of jobs. We study the following flow shop scheduling problem on two processors. We are given n jobs with a common deadline D , where each job j has workload p i , j on processor i and a set of processors which can vary their speed dynamically. Job j can be executed on the second processor if the execution of job j is completed on the first processor. Our objective is to find a feasible schedule such that all jobs are completed by the common deadline D with minimized energy consumption. For this model, we present a linear program for the discrete speed case, where the processor can only run at specific speeds in S = { s 1 , s 2 , ⋯ , s q } and the job execution order is fixed. We also provide a m α − 1 -approximation algorithm for the arbitrary order case and for continuous speed model where m is the number of processors and α is a parameter of the processor. We then introduce a new variant of flow shop scheduling problem called sense-and-aggregate model motivated by data aggregation in wireless sensor networks where the base station needs to receive data from sensors and then compute a single aggregate result. In this model, the first processor will receive unit size data from sensors and the second processor is responsible for calculating the aggregate result. The second processor can decide when to aggregate and the workload that needs to be done to aggregate x data will be f (x) and another unit size data will be generated as the result of the partial aggregation which will then be used in the next round aggregation. Our objective is to find a schedule such that all data are received and aggregated by the deadline with minimum energy consumption. We present an O (n 5) dynamic programming algorithm when f (x) = x and a greedy algorithm when f (x) = x − 1. Finally, we investigate the performance of the flowshop problem when the order of jobs is fixed by comparing it to the approximation algorithm with an arbitrary order. We show experimentally that the approximation ratio is close to 1 when there are few machines and when there are more jobs. [ABSTRACT FROM AUTHOR]

In recent years, Wireless Sensor Networks (WSNs) are widely placed in real-time monitoring systems like environmental, structural, patient monitoring, etc. The major criterion for WSN includes energy efficiency and network lifetime. Scheduling is used as a large number of data packets focus on the same queue at the same time. Only limited data scheduling schemes have been implemented in WSN to enhance the performance. The existing First Come First Serve (FCFS) and Dynamic Multilevel Priority (DMP) have some technical challenges like delay, packet drop and high energy consumption due to starvation and deadlock. In this paper, we proposed a new algorithm to optimise and rank the incoming data packets based on multi-attributes decision-making methodology named as Packet Rank Based Data Scheduling (PRBDS), the algorithm selects packet priority, deadline, and size as the metrics to rank the incoming data packets. A simulation result shows when compared with existing scheduling, PRBDS not only provides less energy consumption, also significantly reduces the packet drop and increases the lifetime. Thus, the proposed algorithm is most suitable for real-time monitoring system since it combines data ranking method with scheduling algorithm to create accurate and reliable results to evaluate the incoming data packets. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) is an architecture that facilitates the feasibility of the distributed control of the modernized industrial systems mainly through the Internet of Things and cloud computing. This article proposes an optimal scheduling framework for the real-time operation of smart microgrids in the IIoT environment using an average consensus-based algorithm. The introduced framework suggests a fog layer as a complementary layer of IIoT to reduce latency and provide local computation and data storage for the proposed industry. Security of the system against probable attacks is the other concern that should be observed. To this end, this article sets out to evaluate the impact of a particular type of attack called the denial of service on the performance of the proposed method. Accuracy, feasibility, and fast response of the scheme are demonstrated through simulation results on a microgrid test system in the presence of dispatchable and nondispatchable generation units with heterogeneous devices. [ABSTRACT FROM AUTHOR]

To optimize communication efficiency and reduce the data transmission of shuffle in large-scale clusters running for MapReduce jobs, this paper built a distributed collaborative data mapping model by replacing the communication locality using storage locality. Then it extracted the local features of jobs by random sampling and machine learning method in order to realize the effective deployment of map tasks. Finally, it selected the nodes with good communication links based on the software define network technology due to its global flexible control capabilities, and scheduled the map tasks to such nodes. Experimental results show that the model has better optimization effect on shuffle-intensive jobs. The communication delay is reduced by 4.3% to 5.8%. This solution can reduce shuffle traffic and data migration delay and it is suitable for various scheduling strategies and network topologies. [ABSTRACT FROM AUTHOR]

We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter’s buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non-decreasing and has increasing differences in the queue backlog; (ii) is non-increasing and has increasing differences in the battery state; and (iii) is submodular in the buffer and battery states. Taking advantage of these structural properties, we derive an approximate value iteration algorithm that provides a controllable tradeoff between approximation accuracy, computational complexity, and memory, and we prove that it converges to a near-optimal value function and policy. Our numerical results confirm these properties and demonstrate that the resulting scheduling policies outperform a greedy policy in terms of queuing delay, buffer overflows, energy efficiency, and sensor outages. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consisting of numerous nodes function in such a manner that each node that collects data from its physical environment, conveys them to the base station for rigorous analysis. Maintaining the functional coverage, energy consumption and the lifetime are among the most challenging factors for WSN. The aim of this paper is to provide an effective coverage with a simultaneous reduction in energy consumption using mobile nodes and evolutionary firefly algorithm. The methodology involves dividing network area into equal cells though smaller size, in order to facilitate the estimation of the physical coverage. In areas with desirable and sufficient coverage, the operational scheduling of nodes is set in such a manner to activate some sensors to ensure necessary coverage and deactivate others. This, to a great extent, decreases the demand for energy. Under the circumstances where the cells are deprived of appropriate or sufficient coverage, mobile nodes are used to improve the coverage. This is achieved by using firefly algorithm to determine the optimum deployment of mobile nodes to perform the job. Results of simulation show that the proposed method has greater superiority over its comparative techniques, particularly on aspects such as the rate of network field area, energy consumption and network lifetime to mention a few. [ABSTRACT FROM AUTHOR]

Nodes in original S-MAC protocol just can visit the channel in the scheduling and listening stage. The working schema may result in data latency and high conflict. To solve those above problems, we split scheduling duty into multiple micro-duties. By using different micro-dispersed contention channel, the sensor nodes reduce the collision probability of the data. Aiming at detecting the fixed duty cycle in S-MAC protocol, on the basis of the micro-duty and buffer queue length, this paper presents an adaptive duty cycle and back-off algorithm. While using different back-off algorithm with different duty cycles, sensor node Fast-Binary Exponential Backoff and Conflict-Avoid-Binary exponential Backoff algorithm separated are applied to reduce data latency further reduce the conflict probability. Combining both of the improvements, we propose a modified S-MAC protocol. Comparing the performance of S-MAC protocol and Division-Multiple Access-Media Access Control (MDA-SMAC) protocol on the NS-2 simulation platform, the results show that MDA-SMAC protocol performs better in terms of energy consumption, latency, and effective throughput than S-MAC protocol [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the overlapped sub-regions (faces) are generated due to the intersections among the sensing ranges of nodes. The faces play a significant role in solving the three problems k-coverage (i.e., all the points in the interested field should be covered by at least k active nodes while maintaining connectivity between all active nodes), coverage scheduling and cover sets. To find the faces and discover their coverage degrees, this article presents a distributed algorithm that runs in three steps. First, a colored graph called Intersection Points Colored Graph (IPCG) is proposed, in which the vertices are defined by the range-intersections of nodes-devices and are colored according to the position of these intersections in relation to the ranges of the nodes. The vertex that located on perimeter of the node's range is colored by red, while the green vertex is an intersection of two ranges inside the range of a third node. The edge that joins two red vertices is colored by red and the edge that joins two green vertices is colored by green while the edge that joins two distinct colored vertices is colored by blue. Second, based on their properties and distinct features, the faces in IPCG are classified into five classes (simple, negative, red, green and positive). Third, based on faces classification, the Three Colored Trees algorithm is proposed to extract the faces in linear time in terms of the number of vertices and edges in IPCG. [ABSTRACT FROM AUTHOR]

The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Monitoring Systems (SMSs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in SMS needs an intelligent scheduling mechanism that efficiently handles the dynamic traffic load without sacrificing the energy efficiency of network. This paper presents a centralized TDMA scheduling based medium access control (MAC) protocol, called Energy Traffic Priority Scheduling MAC (ETPS-MAC) that accommodates variable traffic load while maintaining Quality-of-Service (QoS) assurance in hierarchical WSNs. The ETPS-MAC protocol employs priority scheduling algorithm which considers two factors for assigning priority, the energy factor and the traffic load factor to avoid packet buffering and maintains minimum data packet delay in case of high traffic load. Moreover, a novel rank-based clustering mechanism in FPS-QMAC protocol prolongs the network lifetime by minimizing the distance between the cluster head (CH) and the base station (BS). Both analytical and simulation models demonstrate the superiority of the ETPS-MAC protocol in terms of energy consumption, transmission delay, data throughput and message complexity when compared with the existing TDMA based MAC protocols. [ABSTRACT FROM AUTHOR]

Network Lifetime (NL) is a pivotal issue in real time data monitoring using Wireless Sensor Network (WSN). In this paper, we propose a distributed MAC protocol for NL maximization based on spatial correlation and opportunistic transmission, which we named Spatial Correlation based Clustered Opportunistic Protocol (SCCOP). SCCOP employs a novel spatial correlation model to cleave the network into correlated clusters. The transmission scheduling in clusters is done according to NL maximization condition, which incorporates channel statistics of individual sensors into carrier sensing and backoff strategy. From each cluster, an energy-efficient sensor node with the best channel is scheduled to transmit its data directly to the access point (AP) as the representative node for the whole cluster. Accordingly, SCCOP achieves significant reduction in redundant data transmission as well as energy consumption in the wireless network. The simulation results show that as the node density in the network increases, SCCOP has better performance enhancement in terms of NL, throughput, energy saving and outage. [ABSTRACT FROM AUTHOR]

Mobile elements have shown the advantages in wireless sensor networks not only in data collection and transmission but also in the field of region coverage. The mobile sensor nodes (MSNs) whose mobility is controlled, visits the predetermined locations and collects the environmental information as per the procedure called MSNs traversal algorithms. The major performance bottleneck in MSNs is the energy source which is impractical to replace after the deployment. In random deployment, single or a group of MSNs may fall at the same location or at different locations, whose starting positions of traversal and traversal paths may differ from the preplanned. This work formulates the problem of scheduling the MSNs in distributed manner so that the entire region is covered without coverage holes with minimum energy depletion. Finally, some computationally practical algorithms for multiple MSNs with fault and non-fault tolerant support are presented and their performances compared. [ABSTRACT FROM AUTHOR]

The energy harvesting communication system enables energy to be dynamically harvested from natural resources and stored in capacitated batteries for data transmission, the design greatly improves the service life of the wireless node. In such a system, the amount of harvesting energy is uncertain and the battery capacity is limited. As a consequence, battery overflow and energy dropping may happen to cause energy underutilization and greatly reduce energy utilization. To maximize the data throughput by controlling energy scheduling, we study the problem of offline power scheduling for energy harvesting wireless sensor networks nodes with random energy arrivals and a finite battery.We assume energy arrivals is invariant at a transmission stage period T, but randomly varies across different periods, and is drawn from a random distribution. We propose a simple offline power control policy, based on the characteristics of random energy changes, the power scheduling policy can be changed according to the distribution of energy collection The data throughput can reach 99% of the optimal offline algorithm. Simulation results further validate the efficiency of the proposed offline algorithms. [ABSTRACT FROM AUTHOR]

Data collection is a problem of which a detailed and indepth study has been made. It is known for numerous approaches and a variety of measures and methods that have the objective of validation. The efficacy of data collection still remains a baffling issue and a challenging task. Algorithms do exist in this area, but each considers only either a single feature or few thereof. This paper presents a time orient location energy availability data rate (LEAD) based scheduling algorithm for the improvements of the performance of data collection in wireless sensor networks. The salient feature of this method is its scheduling the sensors for the polling point on the basis of energy and location of the sensor. The method involves the identification of sensors of a small number of which one of which is chosen as the polling point. Computation of the LEAD weight for the sensors of any region is made and a tiny set with higher LEAD weight is selected. The mobile collector selects the polling point based on the LEAD weight and only one visit is done for the polling point when it has enough score and that too in a periodic manner. Data collection is done on a route whose selection is based on energy, hop, reliable routing. This scheme has the specific advantage of improving data aggregation and network lifetime maximization. [ABSTRACT FROM AUTHOR]

IEEE standardized a highly efficient, low power, reliable, deterministic and time-frequency enabled medium access control protocol, IEEE 802.15.4e time slotted channel hopping (TSCH) as an amendment to the medium access control (MAC) protocol defined by the IEEE 802.15.4 standard. Once a communication schedule is built, the IEEE 802.15.4e TSCH is able to execute such a communication schedule. IEEE 802.15.4e TSCH standard lea ves out of scope defining how that communication schedule is built, updated and maintained. In order to fill this gap, we introduce such a scheduling algorithm, called as DIVA. DIVA is specifically designed for IEEE 8092.15.4e TSCH networks. It is a totally distributed scheduling algorithm for divergecast traffic where divergecast means that traffic flows in all directions in contrast to convergecast where traffic flows from leave nodes to root. In order to gain more comprehension about DIVA, we conducted extensive simulations. We also compared DIVA with Berkeley's Aloha-based algorithm. [ABSTRACT FROM AUTHOR]

This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what the minimum queueing delay is when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer scheduling policy is proposed in this paper, and characterized by a constrained Markov decision process. Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent linear programming problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal scheduling policy. The optimal scheduling policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state. [ABSTRACT FROM AUTHOR]

Many industrial wireless sensor networks deploy the IEEE 802.15.4-2015 standard with low power consumption and the Internet networking capabilities. The time-slotted channel hopping (TSCH) is an amendment to the medium access control protocol defined by the IEEE 802.15.4 standard. Furthermore, the Internet engineering task force (IETF) defines an architecture for IPv6 over the TSCH mode of IEEE 802.15.4 (6TiSCH) to provide deterministic properties on wireless sensor networks. The TSCH leverages the scheduled time slot to achieve low power operation and frequency hopping to combat external interference and multi-path fading. Although TSCH provides the channel hopping mechanism to increase transmission reliability, it is still susceptible to periodic interference for the specific frequency. In this research, we focus on the issue of random channel hopping in the error-prone wireless channel condition and the scheduling for different service requirements. We propose a priority-based scheduling using best channel (P-SBC) mechanism for periodic sensing data and emergency packets transmission in 6TiSCH networks. The simulation results show that the proposed P-SBC achieves higher reliability with significantly lower end-to-end latency for emergency packets transmission as compared to TSCH standard. [ABSTRACT FROM AUTHOR]

In a clustered cognitive radio sensor network (CRSN), the available free channels should be considered during cluster head (CH) election. Energy consumption desires have to be taken into consideration in order to improve the fairness. To meet these goals, this paper proposes a Particle Swarm Optimization-based Energy Efficient Channel Assignment (PSOEECA) technique for clustered CRSN. The election of CHs is based on the number of free channels and residual energy. At the beginning of each frame, the elected CH broadcasts a beacon signal about the selected operation mode to the cluster members (CMs). The CH performs channel assignment based on the predicted residual energy of the CMs using PSO technique. In the inter-cluster channel assignment, for transmission of data across multiple clusters, each CH exchanges the assigned channel information among them for scheduling transmission and ensuring the correctness of data delivered to the sink. Simulation results show that the proposed technique enhances throughput, network lifetime and minimizes delay and overhead. [ABSTRACT FROM AUTHOR]

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime. [ABSTRACT FROM AUTHOR]

This paper studies how to maximize the lifetime of a centralized sensor system and meanwhile maintain a certain level of the estimation performance. The model is as follows. A group of sensors measure the states of a dynamic process and transmit the measurements to a remote estimator, which computes the estimates of the states. Constrained by its energy budget, each sensor has limited transmission times and may not transmit data at each time slot. Meanwhile, the time duration to maintain a certain level of the estimation performance of the estimator depends on the scheduling of sensor transmission. The notion of lifetime is defined as the largest time duration within which the system maintains a required estimation performance under a given schedule. This paper aims at studying the optimal scheduling which maximizes the lifetime of the system when a level of estimation performance is required. Both the scenarios of deterministic and stochastic scheduling are considered. In deterministic scheduling, three algorithms are proposed, where the schedules are given by solving convex problems and then discretizing the scheduling variables. In stochastic scheduling, two relaxed problems are considered, where the bounds of the original objective are used. The maximum of lifetime is also studied. Examples are given in the end. [ABSTRACT FROM AUTHOR]

To support different QoS requirements of diverse types of services, a cross-layer QoS scheme providing different QoS guarantees is designed. This scheme sets values of service priorities according to services’ data arrival rates and required end-to-end delay to endow different services with diverse scheduling priorities. To support QoS requirements better and maintain fairness, this scheme introduces delay and throughput weight coefficients. The methods of calculating the coefficients are also proposed. Through decomposing the optimization problem that uses weighted network utility as its optimization objective using Lyapunov optimization technique, this scheme can simultaneously support different QoS requirements of various services. The throughput utility optimality of the scheme is also proved. To reduce the computational complexity of the scheme, a distributed media access control scheme is proposed. A power control algorithm for this cross-layer scheme is also designed, and this algorithm transforms the power control into the solution of a multivariate equation. The simulation results evaluated with Matlab show that, compared with the existing works, the algorithm presented in this paper can simultaneously satisfy the delay demands of different services with maintaining high throughput. [ABSTRACT FROM AUTHOR]

Round Robin based Intermittent Periodic Transmit (RR-IPT) has been proposed which achieves highly efficient multi-hop relays in multi-hop wireless backhaul networks (MWBN) where relay nodes are 2-dimensionally deployed. This paper newly investigates multi-channel packet scheduling and forwarding scheme for RR-IPT. Downlink traffic is forwarded by RR-IPT via one of the channels, while uplink traffic and part of downlink are accommodated in the other channel. By comparing IPT and carrier sense multiple access with collision avoidance (CSMA/CA) for uplink/downlink packet forwarding channel, IPT is more effective in reducing packet loss rate whereas CSMA/CA is better in terms of system throughput and packet delay improvement. [ABSTRACT FROM AUTHOR]

Industrial automation and control systems are increasingly deployed using wireless networks in master-slave, star-type configurations that employ a slotted timeline schedule. In this paper, the scheduling of (re)transmissions to meet real-time constraints in the presence of non-uniform interference in such networks is considered. As packet losses often occur in correlated bursts, it is often useful to insert gaps before attempting retransmissions. In this paper, a quantum Earliest Deadline First (EDF) scheduling framework entitled ‘Eligible EDF’ is suggested for assigning (re)transmissions to available timeline slots by the master node. A simple but effective server strategy is introduced to reclaim unused channel utilization and replenish failed slave transmissions, a strategy which prevents cascading failures and naturally introduces retransmission gaps. Analysis and examples illustrate the effectiveness of the proposed method. Specifically, the proposed framework gives a timely throughput of 99.81% of the timely throughput that is optimally achievable using a clairvoyant scheduler. [ABSTRACT FROM AUTHOR]

Current data center (DC) topologies provide redundant paths to satisfy significant communication requirements. Researchers have proposed various approaches including static and dynamic ones to utilize path diversity more effectively. The static methods adopt hash-based way to distribute flows onto multiple paths randomly, while the dynamic ones relying on centralized controller place flows on candidate paths. Unfortunately, the existing flow-scheduling policies have ignored and even resulted in network bandwidth fragmentation which adversely slows down transmission rate of new flows or affects chances of accepting new flow requests, especially when the granularity of flows is larger. This phenomenon is considered as a bottleneck for higher utilization of bandwidth resource in DCs. In this paper, we are the first to identify and define network bandwidth fragmentation within DC. Accordingly, we present Ashman, a flow-based dynamic scheduling approach to reduce bandwidth fragmentation by proactively considering potential large flows. This policy dynamically schedules the existing large flows to resolve congestions and maximize bandwidth utilization and network throughput. We describe our design and implementation in OpenFlow framework with unmodified hosts. Our evaluation on the software-defined networking simulator called Mininet demonstrates that Ashman effectively reduces bandwidth fragmentation, and thereby achieves higher network bandwidth utilization and the overall throughput of DC. [ABSTRACT FROM AUTHOR]

As an important wireless data broadcast technique, on-demand broadcast has been widely used for dynamic and large-scale data dissemination. An important class of emerging data broadcast applications requires monitoring multiple data items continuously in order to support data-driven decision making. Since wireless bandwidth is a precious shared medium, an important problem to solve is how to disseminate data to periodic queries, so that all the requests can be satisfied while the bandwidth consumption is minimized. In this paper, we first propose a new real-time scheduling algorithm called EDFS , which is a variant of the classic EDF [24] algorithm. Based on EDFS , we propose a novel on-line broadcast scheduling algorithm, called EDFS-BS . To our best knowledge, EDFS-BS is the first dynamic priority based broadcast scheduling algorithm that can be utilized to satisfy the timing constraints of periodic queries. We also propose a bandwidth utilization based schedulability test for EDFS-BS , which is used to ensure timing predictability of a periodic query set. Extensive experiments have been conducted to compare EDFS-BS versus existing solutions with comparable quality. The results show that EDFS-BS outperforms them considerably in terms of wireless bandwidth consumption and query service ratio. [ABSTRACT FROM AUTHOR]

The minimum-latency broadcast problem in duty-cycled wireless sensor networks has received significant attention over the last few years. A common approach for the problem is to assign collision-free transmitting times to forwarding nodes for disseminating a message from one source node to all other nodes according to their given duty-cycle schedules and transmission ranges. However, preventing collision for all transmissions may increase latency in the broadcast schedules. This paper proposes a novel strategy of Collision-Tolerant Scheduling (CTS) that offers an opportunity to reduce broadcast latency by allowing collisions at non-critical nodes to speed up the broadcast process for critical ones. The completion of broadcast scheduling, i.e. all nodes receive a broadcast message, is ensured by additionally transmitting the message to non-critical nodes experiencing collision. We employ the scheduling strategy in two proposed broadcast schemes: Degree-based CTS (DCTS) and MIS-based CTS (MCTS), which select forwarding nodes based on the node degree and maximal independent set information, respectively. The results of both theoretical analysis and simulation reveal the remarkable advantages of CTS in minimizing broadcast latency in duty-cycled WSNs. DCTS and MCTS guarantee approximation ratios of ( Δ − 1 ) T and 12 T in terms of broadcast latency, where Δ and T denote the maximum node degree and the number of time slots in a working period, respectively. The two schemes reduce to at least 94 percent of the broadcast latency compared with existing schemes, while slightly increasing the number of transmissions due to the additional transmissions. Thanks to the latency reduction, the proposed schemes require 93 percent less energy than existing ones. [ABSTRACT FROM AUTHOR]

Wireless powered communication networks are becoming an effective solution for improved self-sustainability of mobile devices. In this context, a hybrid access point transfers energy to a group of nodes, which use the harvested energy to perform computation or transmission tasks. While the availability of the wireless energy transfer mechanism opens up new frontiers, an appropriate choice of the network parameters (e.g., transmission powers, transmission duration, and amount of transferred energy) is required in order to achieve high performance. In this paper, we study the throughput optimization problem in a system composed of an access point, which recharges the batteries of two devices at different distances. In the literature, the main focus so far has been on slot-oriented optimization, in which all the harvested energy is used in the same slot in which it is harvested. However, this approach is strongly suboptimal, because it does not exploit the possibility to store the energy and use it at a later time. Thus, instead of considering the slot-oriented case, we address the long-term maximization. This assumption greatly increases the optimization complexity, as it requires to consider, e.g., the channel state statistics and the batteries evolution. Our objective is to find the best scheduling scheme, both for the energy transferred by the access point and for the data sent by the two nodes. We discuss how to perform the maximization with optimal as well as approximate techniques and show that the slot-oriented policies proposed so far are strongly suboptimal in the long run. [ABSTRACT FROM PUBLISHER]

Memory optimization, energy conservation, real-time scheduling and fault tolerance are the essential research topics in the wireless sensor network (WSN). In this paper, a memory-efficient, energy-efficient, real-time and fault-tolerant WSN system LiveOS is designed and implemented. Compared to the other systems, LiveOS has several typical features. Firstly, the optimized Rate-Monotonic Scheduling (RMS) scheduling algorithm is implemented. By doing this, the memory cost of the multithreaded real-time operating system (RTOS) can be 47.3 % optimized if compared to the traditional single-core node. Consequently, the RTOS becomes appropriate to run even on the memory-constrained WSN nodes. Secondly, the new research approach that addresses the WSN challenges by combining both the multi-core hardware technique and the software technique is applied in LiveOS. By means of the multi-core hardware support, the energy cost of LiveOS can be 35 % optimized compared to the traditional single-core platform. Moreover, the real-time scheduling performance and the fault-tolerant ability of the WSN nodes can be optimized significantly. Due to the above features, LiveOS becomes the WSN system which has small memory footprint, long lifetime, high real-time scheduling performance and upstanding fault-tolerant ability. The evaluation on the different prototype nodes proves that LiveOS is appropriate to run on the resource-constrained WSN platforms and is competent to execute the outdoor real-time WSN applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider scenarios where mobility can be exploited to enable reliable communications in wireless networks with scarce resources that are unable to concurrently service their nodes. Specifically, we consider cases where a team of robots operate as mobile access points (APs) that provide service, namely sufficient end-to-end communication routes, to a multihop network of static source nodes which generate data. We introduce the connectivity scheduling problem, a novel framework that combines motion planning of the APs with service scheduling of the source nodes and network routing control so that integrity of communications is guaranteed over time. We formulate the connectivity scheduling problem as a multistage mixed integer programming (MIP) problem, where path planning, service scheduling, and routing decisions are all jointly optimized over a discrete-time horizon. Since MIP problems can grow intractable quickly, we further consider a continuous convex reformulation of the problem and employ sparse optimization techniques, specifically the reweighted $\ell _1$ regularization scheme, to recover the desired integrality structure of the solution. We propose a decentralized method to solve the above relaxation that is based on the recently developed accelerated distributed augmented Lagrangians (ADAL) algorithm. Specifically, we modify ADAL by incorporating in the algorithm the reweighted $\ell _1$ scheme, which enables us to recover the desired sparsity structure of the original MIP at the final solution. Numerical results are presented that validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

High-frame rate (HFR) video streaming is emerging as a new paradigm in popular multimedia applications (e.g., mobile cloud gaming) to achieve smooth viewing experience perceived by end users. However, it is severely challenging to guarantee the delivery quality of HFR video over wireless platforms with regard to the high transmission rate and limited network resources. Multihoming capability enables mobile devices to concurrently receive video data with different radio interfaces (e.g., cellular and Wi-Fi). To effectively deliver mobile HFR video over multiple wireless access networks, this paper develops an application-layer transmission scheme dubbed joint FRAme Scheduling and Error Resilience (FRASER). First, we propose an unequal frame scheduling approach by taking advantage of interlaced forward error correction coding and reliability-aware data allocation to minimize the total distortion. Second, we introduce an error resilience scheme at the receiver side to proactively leverage the out-of-order and overdue video packets to mitigate the error propagations. The proposed FRASER is able to substantially reduce the probability of I (Intra) frame loss and consecutive P (Predicted) frame drops. We conduct the performance evaluation through extensive emulations in Exata involving HFR video streaming encoded with H.264 codec. Evaluation results show that FRASER outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and received frame rate. Thus, FRASER is recommended for delivering HFR video streaming to multihomed mobile devices in heterogeneous wireless networks. [ABSTRACT FROM PUBLISHER]

We consider the use of distributed storage (DS) to reduce the communication cost of content delivery in wireless networks. Content is stored (cached) in a number of mobile devices using an erasure correcting code. Users retrieve content from other devices using device-to-device communication or from the base station (BS), at the expense of higher communication cost. We address the repair problem when a device storing data leaves the cell. We introduce a repair scheduling where repair is performed periodically and derive analytical expressions for the overall communication cost of content download and data repair as a function of the repair interval. The derived expressions are then used to evaluate the communication cost entailed by DS using several erasure correcting codes. Our results show that DS can reduce the communication cost with respect to the case where content is downloaded only from the BS, provided that the repairs are performed frequently enough. If devices storing content arrive to the cell, the communication cost using DS is further reduced and, for a large enough arrival rate, it is always beneficial. Interestingly, we show that maximum distance separable codes, which do not perform well for classical DS, can yield a low overall communication cost in wireless DS. [ABSTRACT FROM PUBLISHER]

Communications in wireless sensor networks are mainly controlled by the temporal decisions of the data link layer and the spatial decisions of the network layer. When taken independently, these decisions may not be correlated which affects the latency metric. To tackle this problem, we propose cross-layer algorithms to define TDMA (Time Division Multiple Access) schedules based on routing information. Two main strategies are applied, based on routing tree traversals or on adapted ones using information from the network’s graph. We also present the Optimal scheduling algorithm which finds the scheduling with the minimal latency; the latter is used to compute the efficiency of our heuristics. Extensive simulations have been performed and show improvement in latency up to 29% compared with existing works. The effect of the heuristics on the duty-cycle is studied in the perspective of energy consumption. The duty-cycle is improved up to 11.54%. [ABSTRACT FROM AUTHOR]

Wireless sensor networks comprise nodes with limited power supply. The dense deployment of nodes on a terrain for monitoring the environment causes contention in wireless channels that leads to interference and high energy consumption. The topology of the network changes frequently because of failure of nodes, addition of new nodes, and channel fading. Topology control is an important technique for reducing energy consumption, interference, and maintaining connectivity in the network. This paper provides a survey on state scheduling-based topology control techniques for unattended wireless sensor networks. Topology control algorithms for both flat and hierarchical networks are discussed. The algorithms are further categorized according to the key parameters used for state scheduling. The advantages, disadvantages, and results of the algorithms are tabulated. In addition, we highlight future research directions in designing state scheduling-based topology control algorithms. [ABSTRACT FROM AUTHOR]

We investigated the aggregation convergecast scheduling problem in wireless sensor networks. In order to reduce the time needed for data collection through aggregation convergecast, we propose a scheduling algorithm based on an aggregation tree which enables a small delay lower bound and a time slot allocation method which uses the time slots efficiently. To achieve a small delay lower bound, we take the sum of the receiver's depth and child number as the cost of the transmission links and then construct an aggregation tree gradually by adding to it a link with the minimum cost iteration by iteration. To use the time slots efficiently, we use a neighbor degree ranking algorithm together with a supplementary scheduling algorithm to allocate time slot for the sensor nodes. Experiments show that the proposed scheduling algorithm outperforms other work in most cases by reducing the number of time slots needed for data collection by more than 10 %, which indicates the feasibility of our approach for data collection in wireless sensor networks. [ABSTRACT FROM AUTHOR]

This paper describes a packet scheduling algorithm for wireless sensor networks (WSNs) that meets the proportional fairness principle. Based on the weighted round-robin strategy, the proposed scheduling algorithm allocates a different service quota to different traffic according to the average packet arrival rate. This guarantees proportional fairness in terms of the average packet delivery delay and the average packet loss ratio. Since the scheduling algorithm does not perform high-load operations such as time stamping and sorting, it can be implemented easily and is suitable for resource-limited WSNs. The proposed scheduling algorithm is tested in a WSN and is found to guarantee the proportional fairness of the average packet delivery delay when this is used as the performance metric, and to realize proportional fairness in the average packet loss ratio when all the queues are overflowing and the average packet loss ratio is used as the performance metric. [ABSTRACT FROM AUTHOR]

In recent years, wireless charging technology has provided an alternative to charging equipment. Wireless charging technology has already proved to be useful in our daily lives in phones, buses, restaurants, etc. Wireless charging technology can also be applied in energy-bounded wireless sensor networks (WSNs), and these are called wireless rechargeable sensor networks (WRSNs). The optimized charging path problem is the most widely discussed issue in employing WRSNs with wireless charging vehicles (WCVs). This problem involves determining the most efficient path for charging sensor nodes. Further, charging-scheduling problems also need to be considered in the optimized charging path problem. In this paper, we proposed a multi-module charging strategy (MMCS) used to prolong the lifetime of the entire WRSN. MMCS can be divided into three stages: the charging topology, charging scheduling, and charging strategy stages, with multiple modules in each stage. The best module combination of MMCS is the distance-based module in the charging topology stage, delay-based module in the charging schedule stage, and the average lifetime module in the charging strategy stage. The best module combination enables prolonging the lifetime efficiently, as it considers not only the priority of urgent nodes but also the travel distance of WCV; the delay-based module of the charging schedule stage considers the delay effect on the follow-up nodes. The experimental results show that the proposed MMCS can improve the lifetime of the entire WRSN and that it substantially outperforms the nearest job next with preemption (NJNP) method in terms of lifetime improvement of the entire WRSN. [ABSTRACT FROM AUTHOR]

This paper considers wireless networks where communication links are unstable and link interference is a challenge to design high performance scheduling algorithms. Wireless links are time varying and are modeled by Markov stochastic processes. The problem of designing an optimal link scheduling algorithm to maximize the expected reliability of the network is formulated into a Markov Decision Process first. The optimal solution can be obtained by the finite backward induction algorithm. However, the time complexity is very high. Thus, we develop an approximate link scheduling algorithm with an approximate ratio of $$2(N - 1)(r_{M}\Delta - r_{m} \delta ),$$ where N is the number of decision epochs, r is the maximum link reliability, r is the minimum link reliability, Δ is the number of links in the largest maximal independent set and δ is the number of links in the smallest maximal independent set. Simulations are conducted in different scenarios under different network topologies. [ABSTRACT FROM AUTHOR]

In a wireless sensor network (WSN), sensors collect data that need to be delivered to a base station. This requires that the WSN should be a connected network. However, due to possible failures of sensors, a WSN might be partitioned into multiple isolated blocks. A solution to reconnect the WSN is to deploy mobile relays in the WSN and to schedule the mobile relays to positions that can reconnect the isolated blocks. In this paper, we target at a mobile relay scheduling (MRS) algorithm with the minimal total movement cost such that the partitioned WSN becomes connected. We give a definition for boundary sensors of blocks and show that, to connect two blocks, fewer mobile relays are used if we connect the two blocks through a pair of their boundary sensors. To connect a given pair of boundary sensors from two blocks, we derive the optimal new positions of mobile relays, which minimize the total energy consumption of mobile relays when relaying information between the two blocks. To connect a partitioned WSN with multiple isolated blocks, an MRS problem is formulated and shown to be NP-complete. Then, a greedy MRS algorithm is proposed to select boundary sensor pairs and reconnect the WSN by scheduling mobile relays to optimal new positions of connecting the selected boundary sensor pairs. Since the selected boundary sensors are gateways of blocks and the moved mobile relays serve as routers to connect the blocks, any failure of them may disconnect the WSN again. Accordingly, another greedy MRS algorithm is proposed to reconnect a partitioned WSN such that the reconnected WSN can tolerate failure of one gateway or one router. Extensive simulations demonstrate that the proposed algorithms have better performance over other existing methods in terms of higher success probability and less movement cost. [ABSTRACT FROM AUTHOR]

With the emerging demands for local area services, mobile device-to-device (D2D) communications is conceived as a vital component for next-generation wireless networks to improve spectral reuse, to bring hop gains, and to enhance system capacity. In mobile D2D networks, energy-efficient probing schemes are vital to prolonging the limited battery life of mobile devices. Current solutions mainly focus on optimizing the probing interval during contacts by assumption of exponential distributed contact. However, studies in the area of network science have shown that the contact interval follows a power-law distribution, which indicates that the current solutions are not optimal while, on the other hand, dramatically increasing the difficulty of analyzing this problem. In this paper, we propose a network science approach for adaptive wakeup schedule based on power-law distributed contacts. Our approach requires nodes to stay asleep when a contact is unlikely to happen and to wake up only when the possibility that it successfully contacts another node is relatively high. By predicting node contacts in the future based on network science, our approach significantly reduces energy consumption without degrading the performance of opportunistic networks. Extensive simulations with real-life human and vehicular traces demonstrate the outstanding performance of our approach. The results show that our scheme saves 30% in energy while keeping the same performance in most scenarios, and it enhances the performance in terms of average delivery ratio and delivery delay by over 15%, compared with the existing best wakeup techniques, without considering network science. [ABSTRACT FROM AUTHOR]

Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probability-based prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting. [ABSTRACT FROM AUTHOR]