CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN

Wang, Yuhou; Dong, Ying; Li, Shiyuan; Wu, Hui; Cui, Mengyao

doi:10.3390/sym10080319

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…Then they proposed an efficient charging scheduling method based on a gravitational search algorithm presented with a novel agent representation scheme and an efficient fitness function that considers both temporal and spatial priorities of SNs. Wang et al [30] proposed a new model which allows the mobile charger to perform data gathering and energy replenishment simultaneously, consuming energy through short communication distances. Xu et al [31] proposed a novel WRSN model equipped with a MC with separable charger array.…”

Section: A Resolving Energy Problemmentioning

confidence: 99%

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Chen

Ouyang

2020

IEEE Access

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The rapid development of wireless power transfer technology brings forth innovative vehicle energy solutions and breakthroughs utilizing wireless sensor networks (WSNs). In most existing schemes, wireless rechargeable sensor networks (WRSNs) are generally equipped with one or more wireless charging vehicles (vehicles) to serve sensor nodes (SNs). These schemes solve the energy issue to some extent; however, due to off-road and speed limitations of vehicles, some SNs still cannot be charged in time, negatively affecting the lifetime of the networks. Our work proposes a new WRSN model equipped with one wireless charging drone (drone) with a constrained flight distance coupled with several wireless charging pads (pads) deployed to charge the drone when it cannot reach the subsequent stop. Our model solves this charging issues effectively and overcomes energy capacity limitations of the drone. Thus, a wireless charging pad deployment problem is formulated, which aims to apply the minimum number of pads so that at least one feasible routing path can be established for the drone to reach every SN in a given WRSN. Four feasible heuristics, three based on graph theory and one on geometry, are proposed for this problem. In addition, a novel drone scheduling algorithm, the shortest multi-hop path algorithm, is developed for the drone to serve charging requests with the assistance of pads. We examine the proposed schemes through extensive simulations. The results compare and demonstrate the effectiveness of the proposed schemes in terms of network density, region size and maximum flight distance. INDEX TERMS Wireless power transfer, wireless charging drone, wireless charging pad, sensor node, wireless rechargeable sensor networks. WEN OUYANG was born in Hsinchu, Taiwan. She received the Ph.D. degree in computer science from The University of Texas at Dallas, in 1993. From then on, she worked as a Member of Scientific Staff and a Senior Member of Scientific Staff with Bell Northern Research, later became Nortel Networks, where she was involved in large-scale telecommunication systems research and development.

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Section: A Resolving Energy Problemmentioning

confidence: 99%

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Chen

Ouyang

2020

IEEE Access

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“…Wang et al [32] proposed a new model combining ondemand charging and a data collection model (CRCM). The proposed model collects data and simultaneously charges the sensors.…”

Section: Related Workmentioning

confidence: 99%

A Distance-Based Scheduling Algorithm With a Proactive Bottleneck Removal Mechanism for Wireless Rechargeable Sensor Networks

Cheng

et al. 2020

IEEE Access

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The development of wireless charging technology has made research in rechargeable wireless sensing networks (WRSNs) a hot topic. Charge scheduling, which involves allocating mobile charger(s) to replenish energy to the sensors of a network so that the network can remain active for a longer time, is one of the most essential issues in this field. Most previous works addressed the charge-scheduling problem by using proactive or on-demand methods, each with its own advantages and issues. In this work, we propose a novel bottleneck prediction and removal mechanism (BP&R) that seamlessly integrates the two completely different scheduling strategies. The BP&R predicts proactively the potential charging needs that may overwhelm the on-demanding scheduling and eliminate those incidents beforehand. This allows the on-demand scheduling, which usually relies on time and/or distance factors in determining charging order, to concentrate on tackling the problem considering only the distance factor. A simple distance-based near job first (NJF) algorithm is thus sufficient for charge scheduling when time-related factors are considered prior to the determination of the charging order. Use of NJF algorithm also allow us derive a shorter path length in charge scheduling, which results in a tighter charging threshold. A lower charging threshold corresponds to fewer charging requests and less pressure on charge scheduling. In general, the new scheduling strategy relies on the on-demand feature to gather the charging demands of the network, while proactively includes additional sensor nodes that could potentially cause subsequent issues in the scheduling plan. The on-demand and proactive natures of the proposed strategy ensure that it can adapt to the dynamic charging needs of sensors, while at the same time taking precautionary measures to avoid possible burst requests in the future. Extensive simulations show that the proposed strategy can achieve a better network lifetime than other stateof-art scheduling methods.INDEX TERMS Wireless rechargeable sensor networks, charge scheduling, semi-proactive charging algorithm, scheduling bottleneck prediction and removal, sustainable networks.

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“…The radius of the cluster should be within the effective charging distance, and as the corresponding distance is far greater than the charging distance, the mobile charger can achieve both charging and data gathering. In Reference [18], the CRCM was proposed for further charging optimization problems. The present study expanded on this work to develop a data gathering and charging strategy using the CRCM.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, as sensor nodes can be recharged by mobile chargers, the transmission of data can be guaranteed. In the Combined Recharging and Collecting Data Model (CRCM) [18], mobile chargers can be allowed to charge the sensor nodes and gather data at the same time. Multi-hop communication only exists when the sensor nodes send the charging request, but all data gathering must be within a one-hop communication distance.…”

Section: Introductionmentioning

confidence: 99%

A New on-Demand Recharging Strategy Based on Cycle-Limitation in a WRSN

Wang

Dong

et al. 2019

Symmetry

Self Cite

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Recently, Wireless Rechargeable Sensor Networks (WRSNs) have been attracting increasing attention and seen rapid development. However, in previous studies, pioneering work on charging issues and data gathering strategies are always discussed separately. In this paper, we aimed to develop a strategy which can guarantee a short data acquisition cycle and high charging efficiency. We used a new Combined Recharging and Collecting Data Model (CRCM) to set-up a WRSN. In this network, mobile chargers were used to separately collected data and charge sensor nodes. A K-means algorithm was used to group sensor nodes into different clusters, from which mobile chargers collected data from sensor nodes in the center of these clusters. The Nearest-Job-Next with Preemption (NJNP) algorithm was used to determine the charging route. The data acquisition cycle was first discussed in this model in order to ensure all data from the sensor nodes could be gathered within a certain time period. Additionally, the Periodically Restricted Dynamic Mobile Chargers (PRDMCs) algorithm was proposed to determine the number of mobile chargers. Lastly, we used the normal CRCM for comparison with our new CRCM, and the results showed that the new CRCM can effectively safeguard the data acquisition cycle without requiring the addition of more mobile chargers.

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CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN

Cited by 15 publications

References 33 publications

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

A Distance-Based Scheduling Algorithm With a Proactive Bottleneck Removal Mechanism for Wireless Rechargeable Sensor Networks

A New on-Demand Recharging Strategy Based on Cycle-Limitation in a WRSN

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