Edge Computing Underwater Optical Wireless Sensor Networks

Yang Chi, Chi Lin, Jing Deng, Kaiwen Ning, Xin Fan, Guowei Wu

Abstract: Underwater Optical Wireless Sensor Networks (UOWSNs) play important roles in resource exploration and maritime rescue. However, they face significant challenges in real-time data transmission due to the limited propagation range of optical signals (typically 10–100 m), frequent link disconnections caused by node mobility, and the extended distances to onshore servers. Traditional cloud computing solutions, designed for stable terrestrial networks with stationary edge servers and continuous connectivity, experience high latency (3-15 s) in UOWSNs, rendering them unsuitable for real-time applications in underwater environments. To address this issue, we propose a cloud-edge-end architecture tailored for UOWSNs, which can not only combat unique underwater environmental interference on link connection and topological changes but also guarantee robust and real-time communication. We develop a dynamic link-stability-based task offloading path selection (DLS-TOPS) algorithm for maximizing network resource profits. Afterward, we propose an online primal-dual task offloading (OPD-TO) algorithm for minimizing task completion time. Simulation results indicate that the proposed method significantly improves the real-time performance and resource profits of the network, reducing the total task completion time by more than 50% compared to baseline algorithms. We implemented a UOWSN with a cloud-edge-end architecture using commercial off-the-shelf and verified the applicability and effectiveness of the proposed scheme in emergency detection through testbed experiments.

External IDs:dblp:journals/tmc/ChiLDNFW25