An optimization method to improve the performance of unmanned aerial vehicle wireless sensor networks

Abstract

Wireless sensor networks have made great progress in recent years in every aspect of our life. To extend their range of application and provide a further effective option for remote surveillance, unmanned aerial vehicles have been gradually introduced into sensor networks, due to their advantages of flexibility, mobility, and ease of realization. Despite the success of various applications and studies in this new field, unmanned aerial vehicle-wireless sensor network still faces many open challenges, such as the unmanned aerial vehicle capable system framework, land-wireless sensor network management, and unmanned aerial vehicle mission planning strategies. In the article, we propose a cooperative framework for unmanned aerial vehicle-wireless sensor network, which is composed of sensor nodes, fixed-group leaders, and a unmanned aerial vehicle-Sink, in which a three-layer hierarchical network is formed. A land-wireless sensor network k-means driven grouping approach is then presented, which considers the communication performance, the position, and other factors. Additionally, a simulated annealing algorithm is employed to detect the optimal flight trajectory according the ground wireless sensor network architecture. Finally, the proposed approach is compared to other related approaches, and the results have shown better performance of our proposal in terms of energy consumption, flying time, and other relevant evaluation criteria.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…Here, drones collect data only from a subset of sensors (so‐called sink nodes or cluster heads), which in turn aggregate data from their neighboring sensors. Most articles propose a hierarchical planning approach: Stationary sensors are clustered first and a node in each cluster is nominated to be the sink node of this cluster, then drone routes over the sink nodes are planned . Liang et al propose a WSN architecture with a mobile sink node.…”

“…Overall, because data collection and transmission consume the limited energy of sensors and drones, a widely used objective function is to maximize the WSN lifetime, that is, the time interval before the first sensor failure due to energy expiration , or maximal energy consumption among sensors to transmit the collected information . Some articles minimize makespan , total travel distance of the drone and the energy required for the drone's operations .…”

Adversarial Impacts on Autonomous Decentralized Lightweight Swarms

“…Survey on UAV swarms with inspiration from bird flocks -+ + --- [16] Optimization data collection technique for drone swarms and sensor networks + + + + -- [17] A homomorphic encryption scheme to protect drone controllers -…”

Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality

“…Here, only the head nodes of the presently configured clusters directly send their data to the sink node in a passing UAV, and the changing position of the UAV is accommodated by reconfiguring the individual clusters of the sensor nodes in the WSN and reselecting the respective head nodes after each data transmission in order to avoid overtaxing the battery resources of the selected head nodes [29][30][31][32][33]. Research focused on the development of UAV-WSN systems based on the clustering routing protocol has featured a number of methods for configuring sensor node clusters, such as particle swarm optimization (POS) or the weighted K-means clustering algorithm [34][35][36][37][38], and the head nodes are selected by other methods, such as the low-energy adaptive clustering hierarchy (LEACH) protocol or the hybrid energy-efficient distributed (HEED) clustering approach [39][40][41]. Finally, the optimal flight route of the UAV over the head nodes is typically planned using a number of approaches, such as the traveling salesman problem (TSP) or simulated annealing (SA) [29,30,[42][43][44][45][46].…”

“…This method has also been employed to good effect in a number of UAV-WSN applications. For example, Huiru et al [37] deployed a UAV-WSN system based on the clustering routing protocol over a large area for the purpose of environmental monitoring. The weighted K-means algorithm was employed for sensor node clustering, the LEACH protocol was employed to select the head node of each cluster, and SA was employed to plan the flight route.…”