Age-optimal trajectory planning for UAV-assisted data collection

Abstract

Unmanned aerial vehicle (UAV)-aided data collection is a new and promising application in many practical scenarios. In this work, we study the age-optimal trajectory planning problem in UAV-enabled wireless sensor networks, where a UAV is dispatched to collect data from the ground sensor nodes (SNs). The age of information (AoI) collected from each SN is characterized by the data uploading time and the time elapsed since the UAV leaves this SN. We attempt to design two age-optimal trajectories, referred to as the Max-AoI-optimal and Ave-AoI-optimal trajectories, respectively. The Max-AoI-optimal trajectory planning is to minimize the age of the 'oldest' sensed information among the SNs. The Ave-AoI-optimal trajectory planning is to minimize the average AoI of all the SNs. Then, we show that each age-optimal flight trajectory corresponds to a shortest Hamiltonian path in the wireless sensor network where the distance between any two SNs represents the amount of intervisit time. The dynamic programming (DP) method and genetic algorithm (GA) are adopted to find the two different age-optimal trajectories. Simulation results validate the effectiveness of the proposed methods, and show how the UAV's trajectory is affected by the two AoI metrics.

3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

“…Assuming that the UAV has prior information of all SNs' locations, we jointly design the UAV communication scheduling and 3D trajectory for maximizing the minimum average data collection rate from all SNs, under the constraints on the UAV communication scheduling and 3D trajectory, while ensuring data being reliably received by the UAV under a given tolerable outage probability. 6 Assume that the UAV can independently control the horizontal and vertical flying speeds with the maximum speeds denoted by V xy and V z in meter/second (m/s), respectively [33]. Then the maximum horizontal and vertical flying distances within each time slot are S xy = V xy δ and S z = V z δ, respectively, leading to the following UAV flying speed constraints:…”

Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-Assisted Networks

“…Then, the average AoI and some other age-related metrics were investigated in the literature for variations of the queueing model considered in [12] (refer to [13] for a comprehensive survey). Another line of research [14]- [23] employed AoI as a performance metric for different communication systems that deal with time critical information. The main focus of these works was on applying tools from optimization theory to characterize age-optimal transmission policies.…”

“…Note that the destination node was commonly assumed to be a static node in [12]- [23]. More recently, in [22] and [23], the authors considered the optimization of AoI in UAV-assisted wireless networks. However, the analyses in these works were limited to scenarios where UAVs acted as relay nodes and are hence not broadly applicable.…”

Probability Analysis of Age of Information in Multi-Hop Networks

“…While AoI is applicable to almost any cyber-physical system scenario, some of the most prominent applications are vehicular networks [2], unmanned aerial vehicles [3] and networked control systems [4] where periodic status updates are being sent over a wireless network. In such a setting, packets may need to traverse multiple hops towards the destination where each link is prone to delay and packet loss.…”