Data Acquisition Control for UAV-Enabled Wireless Rechargeable Sensor Networks

Abstract

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively.

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

“…Charger types may include charging vehicles, unmanned aerial vehicles (UAVs), or a combination of both. Previous research on wireless charging for wireless rechargeable sensor networks (WRSNs) has predominantly focused on utilizing WCVs [20][21][22][23][24] or UAVs [6,11,16,[25][26][27][28].…”

“…Yoon [11] developed multiple Minimum Depth Trees (MDTs) for all nodes, considering both drone and sensor node energy. The parent node dynamically controls data transmission to prevent its own energy depletion, ensuring balanced data collection for all nodes and preventing energy drain in hotspot areas.…”

“…However, since the battery capacity of drones is limited, the flight distance of the drone is limited when compared to vehicles. To reduce the need for frequent return trips to the base station for drones, the development of wireless charging pads (pads) for automatic drone landing, combined with high-power and efficient wireless power transmission systems, enables drones to be automatically charged within a short timeframe [6,11,12].…”