Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks

Yi, Jun; Yoon, Ikjune

doi:10.3390/s19122679

Cited by 8 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this technique, the high energy requirement of the cluster head, which was not satisfied by WPT, was supplemented using the energy harvesting method. Yi et al [48] proposed a scheme for determining the number of clusters that increases energy utilization in an environment where nodes use solar energy and energy transmitted from UAVs as energy sources. In their scheme, when a UAV traversing a given path arrives at the current cluster heads, it transmits energy to them and receives data.…”

Section: Related Work a Wpt For Wsnsmentioning

confidence: 99%

“…In addition, a cluster head is selected for the configuration of each cluster. The mobile sink node, which is a UAV such as a quadcopter, periodically traverses a predefined path and collects data by visiting the cluster heads, as in the method proposed by previous studies [48].…”

Section: Clustering and Determining The Data Collection Ratementioning

confidence: 99%

“…There, the user retrieves the data collected by the UAV and recharges it or replaces its battery for the next round. The proposed scheme targets an environment similar to the scheme proposed by Yi et al [48], but there are differences in the method used to determine the number of clusters and the selection of heads. An additional difference is that the proposed scheme adjusts the amount of data collected, whereas the scheme of Yi et al collects a fixed amount of data.…”

Section: Clustering and Determining The Data Collection Ratementioning

confidence: 99%

“…If there are m cluster head nodes, data must be collected, and energy must be delivered while taking off and landing m times. Therefore, the energy model of the sink node is expressed as follows [48]:…”

Section: B Energy Model Of a Sink Nodementioning

confidence: 99%

See 3 more Smart Citations

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Yoon

Noh

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes.To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

show abstract

Section: Related Work a Wpt For Wsnsmentioning

confidence: 99%

Section: Clustering and Determining The Data Collection Ratementioning

confidence: 99%

Section: Clustering and Determining The Data Collection Ratementioning

confidence: 99%

Section: B Energy Model Of a Sink Nodementioning

confidence: 99%

See 2 more Smart Citations

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Yoon

Noh

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“… Assisting in locating the WSN nodes [ 25 , 26 , 27 ]. Performing wireless charging for the WSN nodes [ 28 , 29 , 30 ]. Dispersing the WSN nodes in large areas [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Tuan

Yang

et al. 2020

Sensors

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

show abstract

Study on IoT networks with the combined use of wireless power transmission and solar energy harvesting

Chinipardaz

Amraee

2022

Sādhanā

View full text Add to dashboard Cite

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks

Cited by 8 publications

References 42 publications

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Study on IoT networks with the combined use of wireless power transmission and solar energy harvesting

Contact Info

Product

Resources

About