Bioinspired Cooperative Wireless Energy Transfer for Lifetime Maximization in Multihop Networks

Choi, Hyun-Ho; Lee, Kisong

doi:10.1109/jiot.2020.3015953

“…Energy cooperation can also be facilitated through Simultaneous Wireless Information and Power Transfer (SWIPT) technology, which enables concurrent wireless transfer of both information and power. It allows nodes to receive data and harvest energy simultaneously from a single wireless radio signal, prompting numerous related research endeavors [25]- [26].…”

Section: Energy Cooperationmentioning

confidence: 99%

Joint Optimization of Data Routing and Energy Cooperation for Multicastive WPT assisted Data Collection in WSNs

Jiang,

Gao,

Wang

et al. 2024

Preprint

0

View full text Add to dashboard Cite

In Energy Harvesting assisted Wireless Sensor Networks (EH-WSNs), Wireless Power Transfer (WPT) technology plays a vital role in extending network lifespan by enabling energy cooperation among nodes. Nevertheless, existing energy cooperation assisted data collection methods mostly focused solely on Point-to-Point (P2P) style WPT, overlooking the broadcasting nature of wireless radio signals, which offers the potential for multiple nodes to harvest energy simultaneously from a single radio signal. To best exploit the multicastive radio signals, this paper concentrates on harnessing multicastive WPT-based energy cooperation for optimizing data collection performance in WSNs, and inspects the underlying joint optimization of Data Routing and Energy Cooperation for Data Collection (DREC-DC) problem. Here the term \textit{multicastive WPT} is used to distinguish from the P2P-style WPT considered in previous works. To overcome the scalability limitations of centralized solutions, we proposed an efficient Distributed method based on Projected Subgradient Algorithm (DPSA) by formulizing and transforming the single-slot version DREC-DC problem into a strict convex form. For situations where relevant information in some upcoming time slots are available ahead through predictions, DPSA is extended to a sliding window style multi-slot version. Our DPSA is compared with a P2P style energy cooperation approach and some classic routing algorithms not involving energy cooperation for assessing its effectiveness. Simulation results demonstrate that our distributive multicastive-WPT based method leads to considerably higher data collection ability than others.

show abstract

“…Therefore, we adopt (R, ϕ 1 ) for finding the value of r that maximizes the minimum average receive power. Then, let us plug this result into (6) to get the optimum position r by solving…”

Section: A Equally-far-from-center Approach (Ec)mentioning

confidence: 99%

“…where E[ξ RF e ] is the contribution at the worst sensor on the edge with ϕ = ϕ 1 , thus given by (6), and E[ξ RF c ] = |B|P Kr −γ is the contribution at the center. Meanwhile, for the deployment in Fig.…”

Section: A Equally-far-from-center Approach (Ec)mentioning

confidence: 99%

See 1 more Smart Citation

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Rosabal

¹

,

López

²

,

Alves

³

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

show abstract

“…is the contribution at the worst sensor on the edge with ϕ = ϕ 1 , thus given by (6), and E[ξ RF c ] = |B|P Kr −γ is the contribution at the center. Meanwhile, for the deployment in Fig.…”

Section: A Equally-far-from-center Approach (Ec)mentioning

confidence: 99%

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Rosabal,

López,

Alves

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

show abstract

Bioinspired Cooperative Wireless Energy Transfer for Lifetime Maximization in Multihop Networks

Cited by 8 publications

References 50 publications

Joint Optimization of Data Routing and Energy Cooperation for Multicastive WPT assisted Data Collection in WSNs

Joint Optimization of Data Routing and Energy Cooperation for Multicastive WPT assisted Data Collection in WSNs

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Contact Info

Product

Resources

About