The future Internet of Things (IoT) will consist of energy harvesting devices and Unmanned Aerial Vehicles (UAVs) to support applications in remote areas. However, as UAVs communicate with IoT devices using broadcast channels, information leakage emerges as a critical security threat. This paper considers the problem of maximizing the minimum secrecy rate in an energy harvesting IoT network supported by two UAVs, where one acts as a server to collect data from devices, and the other is an eavesdropper to intercept data transmission. It presents a novel Mixed-Integer Nonlinear Program (MINLP), which we then linearize into a Mixed-Integer Linear Program (MILP) problem. It also proposes a heuristic solution called Fly Nearest Location (FNL). Both solutions determine (i) the UAV server’s flight routing, flight time, and computation time, as well as (ii) the energy usage and operation mode of IoT devices. Our results show that FNL achieves on average 78.15% of MILP’s performance.

A key issue when operating an Internet of things (IoT) network is that devices may be infected by malware. Consequently, when collecting data from these devices, e.g., using an uncrewed aerial vehicle (UAV), malware data may be transmitted to a gateway that is then used to attack servers. To address this issue, we equip a UAV with a virtual network function, and study a novel problem involving the allocation of computation and communication resource to identify malware traffic from solar-powered devices. To solve this problem, we first outline a mixed integer linear program (MILP) that jointly optimizes i) UAV placement, ii) UAV trajectory, iii) data processing, iv) channel allocation, and v) energy usage of devices. To facilitate online decision making, we design a neural network-based solution called neural network mapping (NNM) to store integer valued decision variables offline. During flight, the UAV then uses the neural network to retrieve the corresponding integer values for a given scenario, which allows the UAV to solve the said MILP as a linear program that can be computed quickly. Our simulation results show that NNM is able to effectively reduce the amount of data from malware that arrives at a gateway.

Vehicle platoons require low-latency, reliable V2V/V2I links, yet authentication, edge processing and active attacks consume scarce resources and introduce delay and uncertainty; prior work mostly treats control, communication and security separately. In this letter, we close this gap with a risk-aware joint control–communication–security framework and a practical solver, the Distributionally Robust Primal–Dual Alternating (DR–PDA) algorithm, which couples LQR-based control updates with SCA-based convex surrogates and Wasserstein–CVaR robustness for integer resource allocation. Numerical results show DR–PDA achieves low total cost, reduced leakage and attack success, modest per-epoch runtime and a small optimality gap; ablations confirm the Wasserstein–CVaR correction is critical for tail safety, while removing modeled authentication delay lowers cost but markedly increases leakage, illustrating the latency–security trade-off.