Number and Operation Time Minimization for Multi-UAV-Enabled Data Collection System With Time Windows

Abstract

In this paper, we investigate multiple unmanned aerial vehicles (UAVs) enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer non-convex problem, we decompose the problem into two subproblems: UAV trajectory and hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed MACO-based algorithm, is given by leveraging MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

“…Adaptive Q-learning allows UAVs to take distributed, autonomous and adaptive routing decisions. In [19], the authors formulated an optimization problem for task scheduling by considering the energy budget and cache capacity of UAVs. They solved the optimization problem using ant colony optimization algorithm.…”

“…We assume collaborative autonomous systems, i.e., networks of connected UAVs or the multi-UAV network [19], [32], which interact with each other via wireless communication to execute one or more tasks. Generally, no central entity is required to coordinate the actions of the individual UAVs in this type of network.…”

“…In [16], Kong et al revealed that the traditional queue-length based MaxWeight task scheduling has failed to deliver optimal throughput. Subsequently, many novel scheduling techniques have been designed recently in the context of routing protocols by leveraging link duration [17], adaptive Q-Learning [18] or independent of routing by leveraging ant colony optimization [19], meta learning [20]. However, none of these works have considered the dynamic traffic flows while designing their scheduling techniques.…”

Integrating unmanned and manned UAVs data network based on combined Bayesian belief network and multi-objective reinforcement learning algorithm

“…In this network, the timeliness of data transmission is critical. Limited local memory of UAVs, requires that the data be uploaded within a certain time, otherwise, it would be overwritten by new data (Shen et al, 2022;Samir et al 2020). To consider this priority, in the proposed model, we define the concept of time windows.…”

“…In a world where time is of the essence, any tool that can accelerate work time becomes a competitive advantage for manufacturers. This is also true in natural resources of military applications and UAVs have attracted significant attention in this field since UAVs are flexible, portable, inexpensive, and convenient to use, UAVs have been applied to perform various tasks in the areas of transportation, agriculture, healthcare, and heavy industry (Wan et al, 2013;Zhang et al, 2016;Zheng et al, 2015;Lazarescu, 2013;Liu, 2011;Mukherjee et al, 2008). In particular, their effective deployment enables them to be useful in the situations where the availability of wireless coverage to ground users is a challenging issue and the traditional cellular networks are sparse or unavailable as for example, in remote or rural areas.…”