Data Freshness Performance Analysis and Optimization in Timely and Secure Low Altitude Economics

Abstract

Low altitude economy (LAE) can significantly promote the air-to-ground communication development by providing various data collection, transmission and computing services. However, due to the openness trait of wireless channel, LAE also faces timeliness and security concerns on sensing data’s stale and leakage at ground base station (BS) end. This can result in delayed information delivery, compromised data integrity, and increased vulnerability to malicious attacks. In this regard, to address the above concerns, this paper first establishes the blockchain-based UAV-enabled mobile crowdsensing (MCS) model. Then, after determining the impact of the blockchain on data timeliness, the mathematical expressions of the data freshness metric are derived in closed form. On this basis, a data freshness minimization problem with security premise is formulated, where the UAV’s transmission power, computing rate, and BS’s power allocation ratio are jointly optimized. Furthermore, one deep reinforcement learning-aided multi-objective optimization (MOP) algorithm is proposed to solve the formulated problem. At last, under various parameter settings, numerical results have evaluated the effectiveness of the proposals.