Researchers crack wireless security problem that's plagued IoT networks

<p>Broadcast channels are fundamental components of wireless communication systems, extensively utilized in various applications such as the Internet of Things (IoT). However, security poses a significant concern in such networks due to their broadcast nature, making them vulnerable to unauthorized access and eavesdropping. Physical layer security, among other security measures, is crucial for enhancing the security posture of broadcast channels and is often integrated into cross-layer design approaches. Analyzing the performance of secure broadcast channels is a complex endeavor, compounded by several critical factors. Issues such as fading, interference from nearby transmissions, and path loss significantly influence key performance metrics like average packet delay and throughput. The consideration of bursty traffic remains largely unexplored in these systems. The utilization of traffic characteristics such as random arrivals can further improve the performance of those systems without sacrificing security.</p><p>The main contribution of this thesis is addressing these factors in a holistic way for ensuring the efficient operation of broadcast channels. More specifically, proposing and studying congestion control policies as an effective strategy to manage packet transmission flow can mitigate the adverse effects of factors like interference and path loss on average packet delay. By maintaining a balanced transmission flow, congestion control policies help alleviate delays caused by channel impairments. Decoding schemes also play a pivotal role in optimizing network performance. Successive decoding, in particular, offers superior performance compared to treating interference as noise, leveraging higher-quality communication channels more effectively to improve overall network performance. Furthermore, adjusting packet transmission power based on channel conditions can help mitigate interference issues. Utilizing reinforcement learning techniques enables dynamic adjustment of transmission power, allowing the system to adapt to changing channel conditions and optimize performance accordingly. Addressing issues like interference, path loss, and packet delay is crucial for optimizing the performance of secure broadcast channels. By employing congestion control policies, optimizing decoding schemes, and leveraging reinforcement learning for power control, broadcasters can enhance network efficiency and security in various wireless communication applications.</p><p>This thesis proposes an integrated approach to enhancing the performance and security of broadcast channels by addressing key performance bottlenecks. Unlike existing studies that focus separately on congestion control, decoding strategies, or power adaptation, we present a holistic framework that combines these techniques to mitigate interference, reduce packet delay, and optimize throughput. Specifically, we propose the use of congestion control policies to regulate packet transmission flow, thereby minimizing the impact of interference and path loss on latency. Additionally, we explore the benefits of successive decoding over conventional interference-as-noise approaches, demonstrating its ability to leverage high-quality communication channels for improved network efficiency. Finally, we introduce a reinforcement learning-based power control mechanism that dynamically adjusts transmission power in response to channel conditions, enabling adaptive interference mitigation and enhanced system performance.</p>