Energy-Constrained Multidimensional Optimization for UAV-Aided Secure Data Collection With Cooperative Jamming

Due to the agile mobility and line-of-sight transmission capabilities, unmanned aerial vehicles (UAVs) have emerged as a promising technology for facilitating data collection from sensor nodes (SNs) in Internet of Things (IoT) networks. However, the inherent security vulnerabilities of ground-to-air channels and the energy limitation issues of UAVs and wireless devices pose significant challenges to UAV-aided data collection. To address these challenges, this paper develops a secure and effective UAV-aided data collection scheme. In this scheme, each SN operates in either sleep or wake-up mode to conserve energy, and a UAV collects data from wake-up SNs in an eavesdropped environment. To prevent interception by an eavesdropper, other idle IoT devices are strategically chosen to act as friendly jammers to send artificial noise cooperatively. We aim to maximize the worst-case secrecy rate of the considered system by jointly optimizing UAV trajectory design, sensor scheduling, and jammer selection besides ensuring all the data from SNs are successfully collected by the UAV under the triple energy constraints of the UAV, SNs, and jammers and a maximum speed constraint for the UAV. Since the formulated problem is a challenging mixed-integer nonlinear programming problem, we develop an efficient iterative algorithm to tackle this problem by exploiting block coordinate descent (BCD) and successive convex approximation (SCA). Finally, simulation results validate the superior performance of our proposed scheme compared to several baseline schemes in terms of secrecy rate.