UAV-assisted connectivity enhancement algorithms for multiple isolated sensor networks in agricultural Internet of Things

Abstract

In recent years, Wireless Sensor Networks (WSNs) are used in agricultural Internet of Things (IoT) to observe crop data, that has improved the output and quality of agricultural product. However, the connectivity of WSNs in agricultural IoT will encounter new challenges when a farmland is segmented into multiple isolated sub-sensor networks (MISN) due to natural environmental factors. In order to tackle this issue, a new definition of connectivity in MISN is proposed, and the Unmanned Aerial Vehicle (UAV) assisted connectivity enhancement algorithms (UCE) are designed. Our target is to minimize energy consumption of the UAV while satisfying MISN connectivity requirements. Firstly, the destination selection ant colony optimization algorithm (DSACO) and the normalized ant colony optimization algorithm (NACO) are proposed to connect all the sub-sensor networks. Through comparative analysis of them, the optimal path for the UAV to solve the above optimization problem is found. Secondly, autonomously generated optimal point ant colony optimization algorithm (AGOP) is proposed to connect non-communicable nodes within each sub-sensor network. Simulation results show that the complexity of the three algorithms is low, and they can complete the connectivity enhancement task of a large outdoor MISN with reduced energy consumption of the UAV, and the connectivity of the MISN has been significantly improved.