Qi-ferry: Energy-constrained wireless charging in wireless sensor networks

Li, Ké; Luan, Hao; Shen, Chien-Chung

doi:10.1109/wcnc.2012.6214221

Cited by 69 publications

(55 citation statements)

References 10 publications

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“…The emergence of wireless power charging technology should allow the energy constraint to be overcome, as it is now possible to replenish the network elements in a more controllable manner. In this way, some researchers have already investigated the use of mobile chargers that directly deliver power to deployed nodes [115][116][117][118]. A new challenge raised by wireless charging technologies is energy cooperation, since nodes may now be able to share energy between neighbors.…”

Section: Chargingmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

802

452

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The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

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Section: Chargingmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

802

452

View full text Add to dashboard Cite

show abstract

“…Energy replenishment schemes may utilize a mobile charger to prolong the lifetime and improve the efficiency of wireless networks [8]- [14]. A mobile charger can wirelessly transfer energy to wireless nodes with rechargeable batteries [9], [10]. Therefore, an energy harvesting wireless network with a mobile charger brings a new and promising direction to a large number of applications.…”

Section: Introductionmentioning

confidence: 99%

“…These studies usually consider energy replenishment and data flow routing in wireless nodes. A special node named Qi-ferry [9] is proposed to carry energy and wirelessly charge the wireless nodes. The authors solve a tradeoff problem of how many sensor nodes a Qi-ferry is able to charge, and how far the Qi-ferry must go back to recharge itself.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Replenishment Strategy in Energy Harvesting Wireless Networks with a Mobile Charger

Huang

Yuen

et al. 2015

Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustn

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Abstract-Energy harvesting wireless network is an emerging paradigm where the wireless nodes harvest energy to increase their lifetime. Due to high potential for a large variety of applications, such networks have drawn considerable attention in both industry and academia. In this paper, we consider the energy replenishment problem when the wireless nodes not only harvest energy from the ambient environment but also may receive energy from a mobile charger. We propose an optimal energy replenishment strategy using stochastic inventory theory to derive the optimal amount of energy supplied by the mobile charger. The simulation results show that our proposed scheme not only optimizes the energy utilization, but also improves the economic benefit of the wireless nodes.

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“…In general, the number of the stop points can the length of the tour can influence the tour cost [3]. The paper mainly discuss how to reduce mobile devices' tour cost and its electricity consumption, that is, the mobile devices' mobile electricity consumption can be reduced by choosing the proper deformation traveling salesman problem [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

The Disk Covering Tour Problem in the Wireless Sensor Network

Li¹,

Ke²

2014

TOAUTOCJ

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Abstract:The paper discusses the disk covering tour problem for reducing the mobile devices' mobile electricity costs consumed in the wireless sensor network. The DCTP discusses how to find a tour whose cost is the minimum under the condition of the defined initial points and some planar points. The tour starts from the initial stop point. After passing many stop points, the tour returns to the initial stop point. Although the stop point is unnecessary to be in the defined points, the distance between each defined point and a certain stop point during the tour at least should be within the defined disk radius. The electricity can be consumed much when the mobile devices move in and out the stop points. Therefore, the less the number of the stop points is, the less the tour cost is. How to reduce the number of the stop points is regarded as the disk covering problem. The decreasing k-means algorithm is proposed to try finding out the nearly minimum disk for covering all defined points, and then the Lin-Kernighan heuristic is introduced to find out the approximating shortest tour through the obtained stop points. The experimental simulations compare solution algorithms between the method proposed in the paper and other related methods with the Qi-Ferry method for finding out the shortest tour, and then the results show that the method proposed in the paper has the advantage of having a smaller tour cost.

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Qi-ferry: Energy-constrained wireless charging in wireless sensor networks

Cited by 69 publications

References 10 publications

Energy efficiency in wireless sensor networks: A top-down survey

Energy efficiency in wireless sensor networks: A top-down survey

An Optimal Replenishment Strategy in Energy Harvesting Wireless Networks with a Mobile Charger

The Disk Covering Tour Problem in the Wireless Sensor Network

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