| Abstract |
In recent years, automated (Intelligent) decision support systems have become prevalent in various smart city applications such as healthcare, transportation, energy management, and environmental monitoring. Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV)-based smart sensing and actuation devices in agricultural events can change the sector from static and manual to dynamic and intelligent, resulting in increased production with minimal human efforts. In addition, soil measurements that are time-consuming can be collected using Unmanned Ground Vehicles (UGVs). However, to collect data efficiently from wireless sensors in agricultural fields, UAV and UGV need to follow an optimal path. Thus, in this paper, we formulate utility maximization problem using UAV and UGV by simultaneously minimizing energy consumption and maximizing data collection. To solve the formulated problem, we propose a modified Greedy Randomised Adaptive Search Procedure (GRASP) algorithm to predict an efficient path for UAV and UGV to collect data from the agricultural field. Moreover, the efficacy of the proposed algorithm is showcased theoretically and experimentally on real-world data and compared with other state-of-the-art methods. © 2023 IEEE. |
