Abstract
Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.
Highlights
Wireless sensor networks (WSNs) are one of the most investigated research topics in the last two decades
We propose a center-biased hybrid energy-efficient distributed (CBHEED) clustering algorithm, in which the CHs are selected based on the central bias of their geolocation
We assume that the unmanned aerial vehicle (UAV) can measure the received signal strength indicator (RSSI) value based on (27), and it can estimate the energy consumption of the sensor nodes in a cluster
Summary
Wireless sensor networks (WSNs) are one of the most investigated research topics in the last two decades. Our proposed EFDC exploits the quadcopters’ 3D movement, steady hovering, and low-altitude flying capability to enable energy-efficient data collection from WSNs in an infrastructure-less scenario. An energy-efficient and fast data collection (EFDC) scheme is proposed for UWSNs deployed in hilly terrains. EFDC exploits 3D positioning capability of the multi-rotor quadcopter and reduces the transmission distance in a cluster by applying the tabu search mechanism. We formulate an optimization problem for fine tuning the data collection position in a cluster and propose a modified tabu search algorithm to find the sub-optimal solution. Based on the derived data collection positions from the aforementioned tabu search mechanism, we apply a modified genetic algorithm (GA) to determine the optimized trajectory to minimize the UAV travel time.
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