Energy-Efficient Resource Allocation for Mission-Critical Applications in Underwater Acoustic Networks

The Internet of Underwater Things (IoUT) has emerged as a vital technological domain, significantly advancing underwater exploration and communication capabilities. At the core of this paradigm is Underwater Acoustic Communication (UAC), which acts as the primary medium for data transmission in underwater environments, including environmental monitoring, security and surveillance, and underwater exploration. Despite its significance, UAC systems face inherent challenges such as limited bandwidth, high signal attenuation, and long propagation delays. These limitations become particularly significant in mission-critical maritime operations, such as warning systems, underwater navigation and control, and diver safety or emergency communications, where timely and reliable data transmission is crucial. Existing resource allocation solutions often fail to adequately optimize limited UAC bandwidth to address the distinct needs of mission-critical applications, making it difficult to ensure reliability and low delays for critical tasks without compromising non-critical performance. This paper introduces a novel resource management scheme to support mission-critical applications in UAC systems, optimizing the acoustic spectrum using Resource Blocks (RBs) with frequency and time division multiplexing. This approach prioritizes mission-critical applications to ensure high reliability and low latency, while also accommodating non-critical applications. We formulate a resource allocation optimization problem based on application type and present a solution. We also introduce a Meta-Heuristic approach designed to efficiently achieve near-optimal solutions while minimizing computational complexity. Simulation results obtained with the DESERT underwater network simulator show that, compared to existing resource management schemes, our approach significantly reduces end-to-end delay and improves throughput for mission critical applications while optimizes transmission power, thus validating the effectiveness of the proposed solution.