Energy provisioning in wireless rechargeable sensor networks with limited knowledge

Sheikhi, Marzieh; Kashi, Saeed Sedighian; Samaee, Zahra

doi:10.1007/s11276-019-01948-1

Cited by 17 publications

(7 citation statements)

References 45 publications

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“…Details on wireless chargers' deployment when the locations of the sensors are known can be found in Sheikhi et al, 46 Zhang et al, 47 and Dai et al 48 Adaptive directional WPT system model…”

Section: Wpt System Model Employing Power Chargers and An Fcmentioning

confidence: 99%

Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges

Ijemaru

Ang

Seng

2022

International Journal of Distributed Sensor Networks

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Distributed sensor networks have emerged as part of the advancements in sensing and wireless technologies and currently support several applications, including continuous environmental monitoring, surveillance, tracking, and so on which are running in wireless sensor network environments, and large-scale wireless sensor network multimedia applications that require large amounts of data transmission to an access point. However, these applications are often hampered because sensor nodes are energy-constrained, low-powered, with limited operational lifetime and low processing and limited power-storage capabilities. Current research shows that sensors deployed for distributed sensor network applications are low-power and low-cost devices characterized with multifunctional abilities. This contributes to their quick battery drainage, if they are to operate for long time durations. Owing to the associated cost implications and mode of deployments of the sensor nodes, battery recharging/replacements have significant disadvantages. Energy harvesting and wireless power transfer have therefore become very critical for applications running for longer time durations. This survey focuses on presenting a comprehensive review of the current literature on several wireless power transfer and energy harvesting technologies and highlights their opportunities and challenges in distributed sensor networks. This review highlights updated studies which are specific to wireless power transfer and energy harvesting technologies, including their opportunities, potential applications, limitations and challenges, classifications and comparisons. The final section presents some practical considerations and real-time implementation of a radio frequency–based energy harvesting wireless power transfer technique using Powercast™ power harvesters, and performance analysis of the two radio frequency–based power harvesters is discussed. Experimental results show both short-range and long-range applications of the two radio frequency–based energy harvesters with high power transfer efficiency.

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Section: Wpt System Model Employing Power Chargers and An Fcmentioning

confidence: 99%

Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges

Ijemaru

Ang

Seng

2022

International Journal of Distributed Sensor Networks

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“…Requiring only local residual energy information, M2C refreshes the cluster-based network topology to ensure no sensor nodes will run out of energy before getting charged [ 39 ]. Sheikhi et al put forward an approach named Limited Knowledge Charging (LKC) to prolong the network lifetime and reduce the movements of mobile chargers [ 40 ]. Ai et al proposed a novel distributed mobile charging algorithm, to optimize the charging time and chargers’ quantity, and an improved algorithm, named adaptive dynamic energy transfer, to further promote the network capacity in 5G networks [ 41 ].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Cyber Petri net Modelling, Simulation and Analysis of Master-Slave Charging for Wireless Rechargeable Sensor Networks

Qin

Zhao

et al. 2021

Sensors

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Wireless charging provides continuous energy for wireless sensor networks. However, it is difficult to replenish enough energy for all sensor nodes with fixed charging alone, and even more unrealistic to charge a large number of nodes within a short time via mobile charging. In order to overcome the above weaknesses, this paper firstly puts forward a Master-Slave Charging mode for the WRSN (Wireless Rechargeable Sensor Network), where fixed charging is the master mode and mobile charging is the slave mode, respectively. However, Master-Slave Charging is a typical hybrid system involving discrete event decision and continuous energy transfer. Therefore, the Hybrid Cyber Petri net system is proposed to build a visual specification with mathematical expression of Master-Slave Charging. Moreover, wireless charging in the WRSN is modeled and evaluated from the perspective of a hybrid system for the first time. Furthermore, a greedy-genetic algorithm is proposed to obtain the deployment of fixed chargers and the path planning of a mobile charger, by maximizing the actual electric quantity of the master charging problem and minimizing the mobile charger’s travelling path of the slave charging problem. Finally, the simulation results confirm and verify the Hybrid Cyber Petri net model for Master-Slave Charging. It is worth noting that the proposed model in this paper is highly adaptable to various charging modes in the WRSN.

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“…Pal et al [18] suggested a new way to manage the energy demand in every cluster with the use of Travelling Salesman Problem. Sheikhi et al [20] proposed a limited knowledge charging approach in which each charging station formulates a virtual region using grid cells. The aim of limited knowledge charging is to extend the network lifetime using distinct methods.…”

Section: A Recharging Approachmentioning

confidence: 99%

Performance Analysis of Modified AODV Routing Protocol With Lifetime Extension of Wireless Sensor Networks

2020

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Amalgamation of ReCharging and Load Balancing are introduced here to overcome the problem of quick battery energy depletion and jamming in Wireless Sensor Networks. The primary approach (Re-Charging) is to maintain the desired energy level at each node in the network. The second method guarantees jamming-free communication in the network. To maintain the required energy level of the nodes, the SenCar visits the chosen anchor point along the pre-defined trajectory. At the chosen point, SenCar tests all the nodes within its coverage and re-energize all the nodes that have the energy level below the threshold value (3 Joules). Then, the SenCar moves on to the next anchor point and so on until the pre-defined trajectory completes. Finally, the implementation of the proposed mechanism proves the performance enhancement of Wireless Sensor Networks in terms of Throughput, Energy Level, and Packet Delivery Ratio. INDEX TERMS Battery recharging , load balancing, packet delivery ratio, wireless sensor networks.

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Energy provisioning in wireless rechargeable sensor networks with limited knowledge

Cited by 17 publications

References 45 publications

Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges

Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges

Hybrid Cyber Petri net Modelling, Simulation and Analysis of Master-Slave Charging for Wireless Rechargeable Sensor Networks

Performance Analysis of Modified AODV Routing Protocol With Lifetime Extension of Wireless Sensor Networks

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