Spatiotemporal charging scheduling in wireless rechargeable sensor networks

doi:10.1016/j.comcom.2020.01.037

Computer Communications

Volume 152, 15 February 2020, Pages 155-170

https://doi.org/10.1016/j.comcom.2020.01.037 Get rights and content

Abstract

Wireless sensor networks have a wide range of applications. However, the battery-constrained energy limits the scope of network applications and timeliness. In this paper, we study how to schedule charging and allocate charging time simultaneously for a wireless sensor network for the purpose of prolonging network lifetime and improve charging efficiency. To this end, a mixed integer optimization model for simultaneous charging scheduling and charging time allocation is established through the maximization of the charging efficiency. Then, an offline algorithm is developed to solve the problem. Further, an online charging node insertion algorithm is developed for real-time service. The simulation results show that the proposed algorithm can achieve near 100% charging success rate under periodic and hybrid services, which is significantly superior to the results obtained by the compared algorithms.

Introduction

Wireless sensor networks have been widely used in industry and science [1]. Due to the energy constraints of sensor nodes powered by batteries, the network has limited lifetime while some applications are expected to work indefinitely. For this reason, how to effectively extend the working time of wireless sensor networks has attracted the continuous attention of researchers. Some researchers have proposed energy-saving methods [2], [3], but these methods only extend the limited working time [4]. Recent studies have shown that wireless charging technology may effectively prolong the network lifetime. Wireless sensor networks can be recharged by mobile charging vehicles or by deploying static chargers. With the help of wireless energy transfer technology [5], rechargeable batteries can be supplemented by wireless charging, so that the energy can be updated in time and the network lifetime can be prolonged. By carefully optimizing the charging design, the lifetime of the wireless sensor network can be significantly increased [6].

Generally, wireless sensor networks can be recharged by statically deploying chargers or by mobile wireless charging vehicles(WCV). Static chargers can be deployed in the network to continuously replenish the nodes in the coverage area [7]. However, the coverage of static chargers is limited and multiple chargers need to be deployed, which leads to higher costs. Wireless charging vehicles can replenish the energy of nodes in different positions in the network by mobile vehicles, and achieve the goal of sustainable network lifetime [8]. Obviously, the nodes in the network need to have their energy before they are exhausted. Therefore, how to schedule the charging behavior of the wireless charging vehicles has become an important issue. Recently, some researchers have put forward a variety of approaches, such as maximizing the charging utility [9], minimizing the total length of the charging tour path [10], charger deployment and path planning [11], charging path optimization and stopping point selection [12]. In these studies, the charging scheduling solutions of mobile wireless charging vehicles are proposed from different perspectives. However, the charging time of nodes is seldom considered in the existing scheduling methods. In the case of multiple requires of nodes, it is difficult to satisfy all nodes on time, which leads to the energy exhaustion in the charging cycle and affects the quality of service of the network.

To reduce the number of exhausted nodes in the energy replenishment process, this paper not only considers how to schedule the mobile charging vehicles to minimize the path cost, but also optimizes the supply time of each node. Further, the charging time of each sensor node is optimized to maximize the charging efficiency. The main work of this paper is presented as follows:

(1) To improve the charging efficiency and reduce the number of exhausted nodes, a collaborative model of node charging scheduling and time allocation is established to minimize the charging cost of wireless charging vehicles and maximize the energy replenishment of nodes. The model takes into account the service cost and energy replenishment utility, and optimizes the charging time at the same time, which can effectively reduce the number of exhausted nodes.

(2) For periodic services, an evolutionary teaching-learning base optimization algorithm is proposed to optimize the discrete scheduling and the continuous time allocation problem. Based on hybrid encoding, a novel teaching and learning iteration process is designed. To reduce the occurrence of infeasible solutions, a charging time repair algorithm is designed.

(3) For hybrid services, a dynamic node insertion algorithm is proposed to schedule new nodes for real-time requests in the network. The combination of the dynamic insertion method and periodic scheduling algorithm provides a charging scheduling and time allocation scheme for mixed operation.

Finally, the proposed approaches are employed in experiments to verify their effectiveness. The rest of this paper is summarized as follows: the related works are organized in the second section, the system model is formulated in the third section, the periodic scheduling and time allocation method is developed in fourth section, the real-time scheduling for the urgent tasks model is proposed in fifth section, the sixth section evaluates the performance of the algorithm through experiments, and the last section summarizes the paper.

Section snippets

Related works

Traditional wireless sensor networks mainly powered by batteries, however the limited battery capacity limits network lifetime. The methods of saving sensor energy [13] has been proposed in the past decade; however, energy-saving methods can only extend network lifetime to a limited extent. To solve the problem of energy constraints in wireless sensor networks, many researchers have proposed renewable energy technology methods to obtain the corresponding energy supplement, thus network lifetime

Network model and problem formulation

To achieve the permanent work of a wireless sensor network, it is necessary to effectively replenish energy for sensors in the network so that all sensors in the network can steadily work in the expected period. Further, how to select and schedule the nodes to replenish is a key issue to improve the utilization of charging and network performance. To fill the batteries of nodes, the charging process may consume too much time, such that some nodes might not be replenished in time. Further, to

Joint optimization of charging scheduling and time allocation

To dispatch energy, the working time is divided into multiple continuous work periods. At the beginning of each cycle, all nodes calculate the current energy consumption rate independently, so the energy consumption of nodes can be calculated using formula (1). Therefore, the node can estimate the energy status at the end of this period based on historical information and all nodes whose energy is less then the threshold at the end of this period need to be recharged. By sending requests to the

Dynamic insertion algorithm for real-time charging request

In heterogeneous sensor networks, there are multiple types of data streams. Some data streams may change dynamically in real time, such as tracking targets in a sensor network [40]. When the target appears, the sensor energy consumption of the corresponding monitoring area will change significantly, which is difficult to be predicted in advance. Accordingly, the emergence of new tasks will also change the energy consumption [41]. However, the period scheduling method cannot cope with hybrid

Experimental parameters

To evaluate the performance of the proposed ETLBO, various network topologies are designed to compare the result of different algorithms. Assuming that a base station is located in the center of the area, sensor nodes are randomly distributed in the area, and each sensor establishes a path to sink nodes in advance. Heterogeneous data service applications are considered, including periodic tasks and non-periodic tasks in the network. Specifically, in periodic tasks such as periodic environmental

Conclusion

Wireless energy replenishment significantly enhances the network lifetime of wireless sensor networks through feasible scheduling. However, most existing studies only considered how to optimize the charging scheduling without considering charging time allocation. To effectively prolong network working time and improve the efficiency of the sensor network, this paper proposes to jointly optimize the charging scheduling and charging time allocation. The simulation results show that the proposed

CRediT authorship contribution statement

Chuanxin Zhao: Conceptualization, Writing - original draft. Hengjing Zhang: Validation, Software. Fulong Chen: Investigation, Supervision. Siguang Chen: Validation. Changzhi Wu: Methodology. Taochun Wang: Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61871412, Grant 61971235, Grant 61972438, and Grant 61872194 in part by the Natural Science Foundation of Anhui Province of China under Grant 1908085MF214 and Grant 1808085MF172.

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View full text

Spatiotemporal charging scheduling in wireless rechargeable sensor networks

Abstract

Introduction

Section snippets

Related works

Network model and problem formulation

Joint optimization of charging scheduling and time allocation

Dynamic insertion algorithm for real-time charging request

Experimental parameters

Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgment

Appl. Math. Model.

Ad Hoc Netw.

Comput. Commun.

Comput. Netw.

Comput. Netw.

Comput. Commun.

Inform. Sci.

Appl. Soft Comput.

Appl. Math. Model.

Expert Syst. Appl.

Fog computing for energy-aware load balancing and scheduling in smart factory

IEEE Trans. Ind. Inf.

Cognitive-LPWAN: Towards intelligent wireless services in hybrid low power wide area networks

IEEE Trans. Green Commun. Netw.

Opportunistic task scheduling over co-located clouds in mobile environment

IEEE Trans. Serv. Comput.

Wireless power transfer via strongly coupled mag-netic resonances

Science

Radiation constrained scheduling of wireless charging tasks

IEEE/ACM Trans. Netw.

Charging utility maximization in wireless rechargeable sensor networks

Wirel. Netw.

Efficient scheduling of multiple mobile chargers for wireless sensor networks

IEEE Trans. Veh. Technol.

Joint charging tour planning and depot positioning for wireless sensor networks using mobile chargers

IEEE/ACM Trans. Netw.

A survey of network lifetime maximization techniques in wireless sensor networks

IEEE Commun. Surv. Tutor.