Energy-efficient Caching and Task offloading for Timely Status Updates in UAV-assisted VANETs

TL;DR

This paper proposes an energy-efficient caching and task offloading strategy in UAV-assisted vehicular networks to ensure timely status updates, using a two-stage metric and a DDPG-based decision method.

Contribution

It introduces a novel two-stage performance metric and a deep reinforcement learning approach for joint cache refreshing and offloading in UAV-assisted VANETs.

Findings

The proposed method reduces energy consumption while maintaining data freshness.

Simulation results demonstrate the effectiveness of the DDPG-based solution.

The approach outperforms baseline strategies in timely status update delivery.

Abstract

Intelligent edge network is maturing to enable smart and efficient transportation systems. In this letter, we consider unmanned aerial vehicle (UAV)-assisted vehicular networks where UAVs provide caching and computing services in complement with base station (BS). One major challenge is that vehicles need to obtain timely situational awareness via orchestration of ubiquitous caching and computing resources. Note that cached data for vehicles' perception tasks contains time-varying context information, thus freshness of cached data should be considered in conjunction with task execution to guarantee timeliness of obtained status updates. To this end, we propose a two-stage performance metric to quantify the impact of cache refreshing and computation offloading decisions on the age of status updates. We formulate an energy minimization problem by jointly considering cache refreshing, computation offloading and aging of status updates. To facilitate online decision making, we propose a deep deterministic policy gradient(DDPG)-based solution procedure and incorporate differentiated experience replay mechanism to accelerate convergence. Simulation results show that the performance of proposed solution is competitive in terms of energy consumption for obtaining fresh status updates.