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The rise of the Internet of Things (IoT), marked by unprecedented growth in connected devices, has created an insatiable demand for supplementary computational and communication resources. The integration of Unmanned aerial vehicles (UAVs) within IoT ecosystems presents a promising avenue to surmount these obstacles, offering enhanced network coverage, agile deployment capabilities, and efficient data gathering from geographically challenging locales. UAVs have been recognized as a compelling solution, offering extended coverage, flexibility, and reachability for IoT networks. Despite these benefits, UAV technology faces significant challenges, including limited energy resources, the necessity for adaptive responses to dynamic environments, and the imperative for autonomous operation to fulfill the evolving demands of IoT networks. In light of this, we introduce an innovative UAV-assisted data dissemination framework that aims to minimize the total energy expenditure, considering both the UAV and all spatially-distributed IoT devices. Our framework addresses three interconnected subproblems: device classification, device association, and path planning. For device classification, we employ two distinct types of deep reinforcement learning (DRL) agents—Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO)—to classify devices into two tiers. To tackle device association, we propose an approach based on the nearest-neighbor heuristic to associate Tier 2 devices with a Tier 1 device. For path planning, we propose an approach that utilizes the Lin-Kernighan heuristic to plan the UAV’s path among the Tier 1 devices. We compare our method with three baseline approaches and demonstrate through simulation results that our approach significantly reduces energy consumption and offers a near-optimal solution in a fraction of the time required by brute force methods and ant colony heuristics. Consequently, our framework presents an efficient and practical alternative for energy-efficient data dissemination in UAV-assisted IoT networks.

Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars, with the exception of a few. These implementations have also tended to be partial-band and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low-priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated in an additive white Gaussian noise channel to obtain a lower bound performance. Then, we validated the finding obtained by the analysis via realistic end-to-end simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to further validate the analysis and simulation findings. The obtained results demonstrated that the Gaussian noise waveform outperformed the digitally modulated waveforms. Furthermore, in terms of jamming attack strategies, experimental results showed that to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing a random channel hopping jamming strategy achieves a packet loss ratio above 90%.