An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

Nguyen, Phi Le; La, Van Quan; Nguyen, Anh Duy; Nguyen, Thanh Hung; Nguyen, Kien

doi:10.3390/s21165520

Cited by 17 publications

(13 citation statements)

References 46 publications

(83 reference statements)

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“…In [ 23 ], Xu, Liang, and Jia discussed the use of a mobile charger to wirelessly charge sensors in a rechargeable sensor network so that the sum of sensor lifetimes is maximized while the travel distance of the mobile charger is minimized. In [ 24 , 25 , 26 , 27 ], the research problem of on-demand TSP mobile charging was addressed while sensors running out of energy were changed dynamically.…”

Section: Related Workmentioning

confidence: 99%

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

Chen

Gao

et al. 2022

Sensors

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In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

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Section: Related Workmentioning

confidence: 99%

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

Chen

Gao

et al. 2022

Sensors

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“…This scheme has been developed to evaluate the oil and gas subsurface. Nguyen et al [44] presented the Fuzzy Q-Charging method to determine the optimal amount of energy charging for sensor nodes using fuzzy logic.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…In this step, each sub-complex evolves based on the local exploration phase presented in SFLA, and thus the position of the worst frog (x w , y w ) in each sub-complex is improved according to Equation (44).…”

Section: Step 6-evolution Of Each Sub-memeplexmentioning

confidence: 99%

“…x new = x w + S ix y new = y w + S iy (44) where (x new , y new ) is the new position of the frog. Moreover, S ix and S iy indicate the step size in the direction of the x and y axes, respectively; so that, S min ≤ S ix ≤ S max and S min ≤ S iy ≤ S max .…”

Section: Step 6-evolution Of Each Sub-memeplexmentioning

confidence: 99%

“…Improve (x w , y w ) based on Equation(44) and Equation (45); 14: if ((x new , y new ) is not better than (x w , y w )) then Remove (x new , y new ); Calculate a new position (x new , y new ) based on Equation (44) and Equation (46); 17: if ((x new , y new ) is not better than (x w , y w )) then Remove (x new , y new ); Evaluate frogs in Set Solution using the proposed fitness function in Equation (35); 26: BS: Extract the best frog in Set Solution (P G ); 27: BS: Send a Coverage message based on P G to the mobile nodes; End…”

mentioning

confidence: 99%

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An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

et al. 2021

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Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

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