A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

Chen, Hongbin; Li, Xueyan; Zhao, Feng

doi:10.1109/jsen.2016.2517084

Cited by 89 publications

(39 citation statements)

References 34 publications

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“…In [25], they also presented a distributed maximum energy protection algorithm to address the maximum network lifetime coverage for EH-WSNs. Chen et al [26] proposed the reinforcement learning-based sleep scheduling for coverage (RLSSC) algorithm for solar-powered WSNs. e RLSSC composed of two-stage scheduling algorithms can effectively prolong the network lifetime by adjusting the working modes of the sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…A sensor node in an energy harvesting WSN (EH-WSN) can be powered perpetually. erefore, the coverage problems in EH-WSNs tend to concentrate on quality-aware target coverage, but not the extension of network lifetime for the coverage problem [15][16][17][18][19][20][21][22][23][24][25][26][27]. In these algorithms, the optimization objective mainly focuses on coverage utility, including maximizing the number of covered targets or time slots of sensor nodes covering targets.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Bao

Han

et al. 2019

Mobile Information Systems

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Improving the quality of monitoring and guaranteeing target coverage and connectivity in energy harvesting wireless sensor networks (EH-WSNs) are important issues in near-perpetual environmental monitoring. Existing solutions only focus on the utility of coverage or energy efficient coverage by considering target connectivity for battery-powered WSNs. This paper focuses on optimizing the maximum monitoring frequency with guaranteed target coverage and connectivity in EH-WSNs. We first analyzed the factors affecting monitoring quality and the energy harvesting model. Thereafter, we presented the problem formulation and proposed the algorithm for maximizing monitoring frequency and guaranteeing target coverage and connectivity (MFTCC) that is based on graph theory. Furthermore, we presented the corresponding distributed implementation approach. On the basis of the existing energy harvesting prediction model, expensive simulations show that the proposed MFTCC algorithm achieves high average maximum monitoring frequency and energy usage ratio. Moreover, it obtains a higher throughput than existing target monitoring methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Bao

Han

et al. 2019

Mobile Information Systems

View full text Add to dashboard Cite

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“…An efficient greedy hill-climbing algorithm was developed to orderly switch sensors between recharging and active state, which aimed to maximize the overall surveillance quality of targets. Study [16] presented a reinforcement learningbased sleep scheduling for the solar-powered WSN. Based on the reinforcement learning, the proposed algorithm scheduled the sensor nodes in sleep or wakeup states, aiming to satisfy the desired area coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Different from studies [15] and [16], study [17] developed the barrier coverage mechanism in solar-powered wireless sensor networks. It proposed a barrier coverage algorithm, called MSQ which allocated the sensor having the largest contribution to monitor the space-time point with the minimal surveillance quality.…”

Section: Introductionmentioning

confidence: 99%

Barrier Coverage Mechanism Using Adaptive Sensing Range for Renewable WSNs

et al. 2020

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Barrier coverage aims at constructing defense barriers to detect the intruder crossing the predefined boundary in a given wireless sensor network. In literature, most studies considered the battery-powered sensors and applied the Boolean Sensing Model (BSM). The battery-powered sensors have the constraint of limited lifetime while the BSM will affect the actual surveillance quality evaluation because it cannot reflect the physical features of sensing. This paper applies the Probabilistic Sensing Model (PSM) and proposes an algorithm, called BSAS, which considers the solar-powered sensors with adjustable sensing radius to construct the defense barriers. Two main challenges should be overcome. The first one is the cooperative working between sensors to achieve the highest intruder detection probability for a given boundary curve. The BSAS identifies the bottleneck segment with the minimal surveillance quality and schedules as many as possible sensors to improve the bottleneck segment. In addition, a space-time transformation scheme which further adjusts the sensing radius of some sensors is proposed, aiming at improving the detection probability of the bottleneck segment. Consequently, the minimal surveillance quality of the barrier can be maximized. The second challenge is to maintaining the perpetual lifetime of WSNs. The BSAS takes into account the recharging and discharging ratio and time length of daytime in a day in its energy management which guarantees that the sensor energy can always satisfy the energy consumption for sensor working even in the nighttime. Experimental studies reveal that the proposed algorithm outperforms the existing studies in terms of surveillance quality and stability. INDEX TERMS Solar-powered sensor, probabilistic sensing model, adjustable sensing radius, barrier coverage, wireless sensor networks.

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“…The closed-loop MPPT algorithm mainly includes disturbance observation method and conductance incremental method [6]. The intelligent control algorithm mainly includes MPPT control algorithm based on neural network, Fuzzy theory MPPT control algorithm [7][8][9]. In the research of MPPT algorithm in recent years, the literature [10] adopted the method of moving average conductance increment to control the voltage change.…”

Section: Introductionmentioning

confidence: 99%

Design of Photovoltaic Power Supply MPPT Circuit for WSN Node Based on Current Observation

Zhang

et al. 2018

Int. J. Onl. Eng.

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Abstract-In view of the problem of low energy collection efficiency and low efficiency of photovoltaic power supply modules in wireless sensor networks, a maximum power point tracking algorithm suitable for photovoltaic power supply of wireless sensor nodes is proposed. Firstly, the photovoltaic cell model is analyzed. Based on this, the traditional maximum power point tracking algorithm is analyzed. Combining with the advantages of disturbance observing method and load current maximization method, the problem of low working efficiency and low energy collection efficiency of functional modules is solved. Current observation method maximum power point tracking algorithm, and complete the relevant hardware circuit design. Experimental results show that the power consumption of MPPT circuit is low, and the efficiency caused by environmental factors is very small. The efficiency is kept above 90% and the overall system efficiency is about 87%, which provides a stable and reliable photovoltaic power supply for WSN nodes.Keywords-WSN Node, MPPT, Photovoltaic Power Supply, Current Observation IntroductionAs a new information acquisition and processing technology, wireless sensor network(WSN) can monitor, perceive and collect the relevant information of various monitored objects in the network distribution area in real time and has a bright future. However, WSN nodes are generally powered by common chemical batteries, and their limited service life directly affects the life of wireless sensor networks [1][2]. Taking the solar energy with higher energy density as the energy source of the wireless sensor node can greatly increase the life span of the wireless sensor node. Due to the low photoelectric conversion efficiency of photovoltaic cells, and the output power is greatly affected by external parameters such as light intensity and temperature, if it is necessary to ensure that the node obtains more energy to facilitate the storage of the energy storage element and improve the service quality of the node, To ensure that iJOE

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A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

Cited by 89 publications

References 34 publications

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Barrier Coverage Mechanism Using Adaptive Sensing Range for Renewable WSNs

Design of Photovoltaic Power Supply MPPT Circuit for WSN Node Based on Current Observation

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