Reduced UAVs' Deployment and Trajectory Optimization With Balanced Traveling Distance

초록

Unmanned aerial vehicles (UAVs) are employed for collecting information received by Internet-of-Things (IoT) devices for an operation time restricted by their battery. Due to the energy constraint in the UAVs, it is challenging to gather data from widely spread IoT devices. Accordingly, the cooperation of multiple UAVs is required; however, finding the optimal quantity of UAVs is one of the crucial problems to reduce the overall operational cost. To address this issue, an algorithm, termed as edge deployment successive convex approximation (ED-SCA), is proposed to optimize the required quantity of UAVs while guaranteeing the minimum average transmission rate of IoT devices. ED-SCA solves two subproblems: finding the minimum quantity of UAVs and determining the trajectory of those UAVs. The first subproblem is solved using a heuristic method of optimizing the UAVs' deployment, assigning similar number of IoT devices to each UAV, and then initializing every UAV's trajectory. The second subproblem is solved by maximizing the total transmission rate in IoT devices with the constraint of guaranteeing a minimum average transmission rate for all IoT devices through convex optimization. Through simulations under various conditions, the proposed algorithm achieves a fewer UAVs being deployed, higher average throughput than conventional algorithms, and a balanced traveling distance among the UAVs.

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