Energy Minimization Oriented Resource Allocation for Integrated Sensing and Communication in Marine IoT Networks

A layered optimization framework for UAV-assisted ISAC in marine IoT, achieving 19.71% energy savings over OFDMA and an 8.72% gap from the LINGO optimum.

Qianru Wang; Li Ping Qian; Chenglong Dou; Haijun Zhang; Yuan Wu· 2026· DOI 10.48550/arXiv.2607.13462

The core problem

Integrated sensing and communication (ISAC) has emerged as a promising technical framework for Marine Internet of Things (MIoT) systems, enabling simultaneous target sensing and data collection over shared spectrum. However, all devices in such networks rely on battery power, making energy efficiency a core bottleneck that limits practical deployment. This paper investigates the energy consumption minimization problem for MIoT-oriented ISAC systems. In the considered architecture, an uncrewed aerial vehicle (UAV) employs non-orthogonal multiple access (NOMA) to concurrently perform target sensing and collect data from uncrewed surface vehicles (USVs). The UAV then forwards processed sensing information and USV data to a shore-based base station (SBS). The challenge is to minimize total system energy consumption subject to latency limits and sensing performance requirements, via joint optimization of multiple variables: UAV transmit beamforming, dedicated sensing signal, USV transmit power, UAV computation power, and time resource allocation for sensing and communication phases. The problem is non-convex, motivating a layered solution architecture that decomposes it into independent

Simulation results verify the validity and accuracy of the proposed algorithm in reducing system energy consumption. Compared with orthogonal frequency division multiple access (OFDMA) and a genetic algorithm benchmark, the proposed scheme lowers system energy consumption by 19.71% and 8%, respectively. Furthermore, the optimized energy value exhibits only an 8.72% gap from the optimum solved by the LINGO solver, demonstrating near-optimal performance. These results confirm that the layered optimization framework effectively balances sensing and communication requirements while minimizing energy consumption in marine IoT networks. The comparison highlights the benefits of NOMA-based ISAC and joint resource allocation over conventional orthogonal multiple access and heuristic optimization.

Integrated sensing and communication (ISAC) has emerged as a promising technical framework for Marine Internet of Things (MIoT) systems, enabling simultaneous target sensing and data collection over shared spectrum. However, all devices in such networks rely on battery power, making energy efficiency a core bottleneck that limits practical deployment. This paper investigates the energy consumption minimization problem for MIoT-oriented ISAC systems. In the considered architecture, an uncrewed aerial vehicle (UAV) employs non-orthogonal multiple access (NOMA) to concurrently perform target sensing and collect data from uncrewed surface vehicles (USVs). The UAV then forwards processed sensing information and USV data to a shore-based base station (SBS). The challenge is to minimize total system energy consumption subject to latency limits and sensing performance requirements, via joint optimization of multiple variables: UAV transmit beamforming, dedicated sensing signal, USV transmit power, UAV computation power, and time resource allocation for sensing and communication phases. The problem is non-convex, motivating a layered solution architecture that decomposes it into independent subproblems and optimizes each alternately according to its mathematical features.

The authors formulate the energy minimization problem as a non-convex optimization that couples UAV beamforming, sensing signal design, USV power control, UAV computation power, and time allocation. To solve it, they build a layered solution architecture that divides the original problem into independent subproblems and optimizes each alternately. First, they derive closed-form solutions for USV transmit power and perform variable substitution to simplify the problem. The remaining non-convex subproblems are then converted into convex forms using the successive convex approximation (SCA) method. Efficient iterative algorithms are designed based on these convex approximations. The overall procedure alternates between subproblems until convergence. The mathematical structure can be summarized as follows: the original problem is

The paper's key contribution lies in its layered solution architecture that decomposes a complex non-convex problem into manageable subproblems, each solved with tailored mathematical techniques. The closed-form USV transmit power solution and variable substitution reduce dimensionality, while SCA handles the remaining non-convexity. The alternating optimization ensures convergence to a near-optimal solution, as evidenced by the small gap to the LINGO solver. The 19.71% energy reduction over OFDMA underscores the efficiency gains from NOMA and joint sensing-communication design. The 8% improvement over a genetic algorithm shows that the proposed analytical approach outperforms heuristic search. However, the reliance on UAV mobility and NOMA may introduce interference management challenges in dense deployments. Future work could extend the framework to multi-UAV scenarios and consider channel uncertainties. Overall, the study provides a solid foundation for energy-efficient ISAC in marine IoT, with potential applications in environmental monitoring, maritime surveillance, and disaster response.

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