A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Cheng, Gong; Guo, Cuiping; Xu, Haitao; Zhou, Chang; Yuan, Xiaotao

doi:10.3390/pr8101324

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The first stage uses a particle swarm optimization algorithm to optimize the area coverage. In the second stage, a queuing game-based energy supply algorithm is designed to optimize the energy distribution [33]. In these works, little consideration has been given to the impact of sensor deployment on the overall network.…”

Section: Related Workmentioning

confidence: 99%

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Lian,

Yao

2024

Energies

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As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

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Section: Related Workmentioning

confidence: 99%

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Lian,

Yao

2024

Energies

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“…The technology of unmanned aerial vehicles was leveraged in wireless sensor networks in which the sensor nodes offloaded the data to the UAVs which improved the network coverage and reduced the consumption of energy [11]. The combination of coverage optimization and energy regulation processes increased the QoSensing of the transmission and extended the lifetime of the sensor nodes [12]. The implementation of the mobile sink comparatively increased the network coverage, and further, the usage of the long-range wireless interface improved the QoSensing of the network [13].…”

Section: Introductionmentioning

confidence: 99%

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

2021

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Coverage is an important factor for the effective transmission of data in the wireless sensor networks. Normally, the formation of coverage holes in the network deprives its performance and reduces the lifetime of the network. In this paper, a multi-intelligent agent enabled reinforcement learning-based coverage hole detection and recovery (MiA-CODER) is proposed in order to overcome the existing challenges related to coverage of the network. Initially, the formation of coverage holes is prevented by optimizing the energy consumption in the network. This is performed by constructing the unequal Sierpinski cluster-tree topology (USCT) and the cluster head is selected by implementing multi-objective black widow optimization (MoBWo) to facilitate the effective transmission of data. Further, the energy consumption of the nodes is minimized by performing dynamic sleep scheduling in which Tsallis entropy enabled Bayesian probability (TE2BP) is implemented to switch the nodes between active and sleep mode. Then, the coverage hole detection and repair are carried out in which the detection of coverage holes if any, both inside the cluster and between the clusters, is completed by using the virtual sector-based hole detection (ViSHD) protocol. Once the detection is over, the BS starts the hole repair process by using a multi-agent SARSA algorithm which selects the optimal mobile node and replaces it to cover the hole. By doing so, the coverage of the network is enhanced and better QoSensing is achieved. The proposed approach is simulated in NS 3.26 and evaluated in terms of coverage rate, number of dead nodes, average energy consumption and throughput.

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“…However, wireless sensor nodes have limited energy. It is difficult to charge and replace the batteries [4]. Finite energy would affect network lifetime and communication quality.…”

Section: Introductionmentioning

confidence: 99%

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

Han

Zhu

et al. 2021

Processes

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The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.

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A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Cited by 8 publications

References 26 publications

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

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