Resonant wireless power transfer to ground sensors from a UAV

Griffin, Brent; Detweiler, Carrick

doi:10.1109/icra.2012.6225205

Cited by 110 publications

(66 citation statements)

References 10 publications

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“…It has already been applied to power medical sensors and implantable devices [112], to replenish sensors embedded in concrete in a wireless manner [113] and to power a ground sensor from a UAV [114]. 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.…”

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

“…International Journal of Distributed Sensor Networks ( 0 = S); (4) while ≤ do (5) push 0 into ; (6) for = to do (7) p u s h into ; (8) apply Algorithm 1 to decide the schedulability of ; (9) if is unschedulable then (10) p o p from ; (11) = , = + 1; (12) break; (13) end if (14) end for (15) end while Algorithm 2: Construct a Hamilton tour through S and N; split into schedulable subtours.…”

Section: The Tour Assignment Problemmentioning

confidence: 99%

“…In this way, lifespan of WRSNs can be significantly prolonged, or theoretically speaking, to infinity. Applications include sustainable WRSNs in smart grids [6] and underground sensor networks powered by UAVs [7].…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Number of Mobile Chargers to Keep Large-Scale WRSNs Working Perpetually

Wang

2015

International Journal of Distributed Sensor Networks

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Wireless Rechargeable Sensor Networks, in which mobile chargers (MCs) are employed to recharge the sensor nodes, have attracted wide attention in recent years. Under proper charging schedules, the MCs could keep all the sensor nodes working perpetually. Since MCs can be very expensive, this paper tackles the problem of deciding the minimum number of MCs and their charging schedules to keep every sensor node working continuously. This problem is NP-hard; we divide it into two subproblems and propose a GCHA (Greedily Construct, Heuristically Assign) scheme to solve them. First, the GCHA greedily addresses a Tour Construction Problem to construct a set of tours to 1-cover the WRSN. Energy of the sensor nodes in each of these tours can be timely replenished by one MC according to the decision condition derived from a Greedy Charging Scheme (GCS). Second, the GCHA heuristically solves a Tour Assignment Problem to assign these tours to minimum number of MCs. Then each of the MCs can apply the GCS to charge along its assigned tours. Simulation results show that, on average, the number of MCs obtained by the GCHA scheme is less than 1.1 over a derived lower bound and less than 0.5 over related work.

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“…power cables or solar panels) or periodic maintenance to replace batteries. We have developed a novel solution to this problem by using a UAV equipped with a resonant magnetic wireless power transfer system to charge sensors in hard-to-access locations [4]. The system can transfer over 10 Watts at close distances and a range of about 1 m. The system enables charging of sensors embedded in materials or in hard to access locations where physical charging connectors are impractical.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of a UAV-Based Wireless Power Transfer Localization System

Mittleider

Griffin

Detweiler

2015

Experimental Robotics

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Abstract. Sensors deployed in remote locations provide unprecedented amounts of data, but powering these sensors over long periods remains a challenge. In this paper, we develop and present a UAV-based wireless power transfer system. We discuss design considerations and present our system that allows a UAV to fly to remote locations to charge hard to access sensors. We analyze the impact of different materials on the wireless power transfer system. Since GPS does not provide sufficient accuracy, we develop and experimentally characterize a relative localization algorithm based on sensing the magnetic field of the power transfer system and optical flow that allows the UAV to localize the sensor with an average error of 15 cm to enable the transfer of on average 4.2 W . These results overcome some of the practical challenges associated with wirelessly charging sensors with a UAV and show that UAVs with wireless power transfer systems can greatly extend the life of remotely deployed sensors.

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Resonant wireless power transfer to ground sensors from a UAV

Cited by 110 publications

References 10 publications

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

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

Minimizing the Number of Mobile Chargers to Keep Large-Scale WRSNs Working Perpetually

Experimental Analysis of a UAV-Based Wireless Power Transfer Localization System

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