Message Dissemination Method with Lightweight Learning Based Link Prediction for Hybrid UASN

Abstract

Underwater Acoustic Sensor Network (UASN) comprises two types of nodes: anchored nodes and floating nodes. Due to the complex movement of floating nodes and the sparse deployment of anchored nodes, the message dissemination in hybrid UASN is achieved by utilizing the intermittent communication links between nodes (anchored nodes and floating nodes) to constitute the opportunistic communication paths to the sink node. In this work, the historical communication links are recorded and exploited to predict the future communication links, which can help the message holders to select the appropriate relay nodes. In the proposed Message Dissemination method with Lightweight Learning based Link Prediction for hybrid UASN (MD-LLLP), two kinds of learning models are deployed on floating nodes and anchored nodes to perform the local link prediction, respectively. Particularly, a lightweight learning model with low computational complexity is specially designed for floating nodes. Besides, MD-LLLP adopts a distributed message dissemination manner, and thus significantly reduces the communication overhead by avoiding the global exchanges of historical communication links which are locally recorded by nodes. Finally, extensive simulations developed by Python have demonstrated the superior performance of MD-LLLP. Specifically, compared to baseline methods, MD-LLLP achieves an average increase of 19% in delivery ratio, reduces the average delivery delay by 6.1 seconds, and reduces the number of message copies by 16%. By MD-LLLP, the data messages can be disseminated to the sink node quickly, making the delivery ratio of data messages enhanced and the delivery delay of data messages shortened.