Multistep Prediction-Based Adaptive Dynamic Programming Sensor Scheduling Approach for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Liu, Fen; Jiang, Chao; Xiao, Wendong

doi:10.1109/tase.2020.3019567

Cited by 35 publications

(9 citation statements)

References 50 publications

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“…Thereafter, the remaining candidate nodes in constitute the actual forwarding set . Similar to [12], we take the packet acceptance rate ( ), as shown in (7), to measure the link quality. The larger the , the better the link quality.…”

Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

“…)) , (7) where is the distance between nodes and , , is the noise bandwidth, is the packet sending rate, denotes the current signal-to-noise ratio and denotes the size of the forwarding packet. We use the model proposed in [35] to estimate by (8).…”

Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

“…. Thereafter, the link quality for the link between each node in and node is calculated using (7). Nodes 4 and 5 are filtered out because their link quality is lower than the predefined threshold α.…”

Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

“…The limited battery power capacity is one of the major constraints in the application of traditional wireless sensor networks [1], [2], [3]. Energy harvesting (EH) technology has become a promising solution to this problem [4], [5], [6], [7]. By equipping energy harvesting modules, the sensor nodes in energy-harvesting wireless sensor networks (EH-WSNs) can harvest energy from ambient energy, such as solar, wind, and vibrations.…”

Section: Introductionmentioning

confidence: 99%

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An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Ren

Yao

2022

IEEE Access

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Opportunistic routing (OR) is widely used in energy-harvesting wireless sensor networks (EH-WSNs). The transmission power of sensor nodes in EH-WSNs is usually adjusted dynamically to make full use of the harvested energy. Many OR algorithms with dynamic transmission power adjustment have been proposed for EH-WSNs. However, fewer studies consider the non-bidirectional communication and the increase in packet retransmission and delay caused by the heterogeneous transmission power/radii. To this end, we propose an opportunistic routing algorithm for EH-WSNs with dynamic transmission power and duty cycle (ORDPD). It adjusts the transmission power of the sensor nodes at the end of each time slot, according to the predicted available energy. It also adjusts the transmission power and duty cycle of the sensor nodes in time slots if the nodes receive packets from other nodes outside their current transmission range. ORDPD also adopts an improved transmission model and information exchange mechanism to dynamically update relay sets and forwarding paths. A series of experiments were conducted to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed ORDPD reduces non-bidirectional communication and retransmission significantly and performs better than its corresponding competitors.

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Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

Section: B the Relay Set (Re)selection Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Ren

Yao

2022

IEEE Access

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“…In our previous work (Liu et al, 2020;Chen et al, 2021Chen et al, , 2022Jiang et al, 2022), reinforcement learning methods have been successfully applied to the charging scheduling problem of microgrids, energy harvesting WSNs, and WRSNs. For example, we proposed a multistep adaptive dynamic programming algorithm, one type of reinforcement learning, for cooperative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon (Liu et al, 2020). We also proposed an improved deep Q-network approach for user-side battery energy storage charging and discharging strategy to reduce the costs and energy consumptions of charging and discharging actions (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Q-network for Mobile Charging Sequence Scheduling with the Minimal Mobile Charger Energy Capacity in Wireless Rechargeable Sensor Network

Jiang

Chen

et al. 2023

Preprint

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Wireless rechargeable sensor network (WRSN) is a new type of wireless sensor networks (WSNs) with rechargeable sensor nodes, which provides a feasible way to overcome the energy constraint prob- lem in WSN. Mobile charging is promising for WRSN by charging sensors using the mobile charger (MC), usually via wireless energy transfer (WET). However, it is challenging to maintain the network working sustainably under the limited capacity of the MC, and most existing works assume that the capacity of MC is infinite or with a very large value, which may be impossible in many real applica- tions. This paper will study the mobile charging sequence scheduling problem to maintain the network working sustainably and minimize the energy capacity of the MC (CSSEC). Specifically, the problem of minimizing the energy capacity of MC is coupled with mobile charging sequence scheduling, and the charging sequence will directly affect the energy cost of MC during a charging tour. Therefore, we propose an improved deep Q-network for CSSEC (IDQN-CSSEC) to address this issue. It employs the MC as the agent to explore the WRSN, and determines a charging policy for MC based on the charging demand of sensors, which selects the next sensor for charging. IDQN-CSSEC uses the Q learning and deep Q-network (DQN) comprehensively, which trains the Q table of the Q learning and the networks of DQN in the early stage of the training, and updates them simultaneously. The action determined by Q learning is selected with a greater probability, which decreases as the iteration step increases. Simu- lation experiments verify that the charging policy determined by IDQN-CSSEC can make the network work sustainably with the minimal energy cost of the MC by comparing it with the baseline methods.

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Distributed Multi-target Tracking Based on Wireless Sensor Networks

Chen

Liu

2023

Lecture Notes in Electrical Engineering

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Multistep Prediction-Based Adaptive Dynamic Programming Sensor Scheduling Approach for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Cited by 35 publications

References 50 publications

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

An Opportunistic Routing for Energy-Harvesting Wireless Sensor Networks With Dynamic Transmission Power and Duty Cycle

Improved Deep Q-network for Mobile Charging Sequence Scheduling with the Minimal Mobile Charger Energy Capacity in Wireless Rechargeable Sensor Network

Distributed Multi-target Tracking Based on Wireless Sensor Networks

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