Secure UAV-assisted Mobile Edge Computing for IoT with Backscatter Communication in the Presence of a Moving Eavesdropper

The perception layer security (PLS) is crucial for ensuring that the data collected by Internet of Things (IoT) devices is accurate, reliable, and protected against various security threats. It helps maintain the overall integrity of the IoT ecosystem and builds trust in its applications. Our work explores the integration of network and PLS in a UAV-enabled mobile edge computing (MEC) system for IoT. This system supports multiple users with a combined non-orthogonal and time-division multiple access (NOTDMA) scheme and is based on backscatter communication (BC). In this system, the UAV-mounted server functions as a hybrid access point (HAP) and hovers over a cluster of energy-constrained IoT devices to transmit RF energy and assist them in performing tasks by employing BC. The IoT devices apply the combined NOTDMA scheme to offload their tasks to the HAP. A mobile passive eavesdropper attempts to intercept information from IoT devices without actively launching any attacks. A partial offloading scheme with various encryption algorithms is proposed to improve the system’s secrecy, which adapts to the users’ non-linear harvested energy levels. In addition, considering the network and physical security, we derive a approximation expression for the secrecy successful computation probability (SSCP). This expression incorporates factors such as harvested energy, local computing and encryption latency, edge offloading latency, processing, decryption, and the associated secrecy costs. The optimization problem for maximizing SSCP is formulated and solved using an Immune algorithm to find the optimal set of device parameters and UAV altitude. Key parameters affecting secrecy and latency performance are analyzed to better understand the system’s behavior. Numerical simulations are provided to validate the accuracy of our analysis.