Autonomous underwater vehicle path planning data aggregation scheme for underwater linear sensor network

Abstract

Linear Sensor Networks (LSNs) are often utilized for monitoring and surveying linear structure material such as pipelines, roads, and demarcation of borders. The Under Water Linear Sensor Network (UWLSN) is facing the challenges of limited capability such as bandwidth due to the acoustic signal. In addition, the way and manner of the sensor nodes that are deployed, and data collection that are conducted contribute to the delay in the data delivery to the sink node. Existing deployment strategies forward data to a higher capacity node in order to forward to an Autonomous Underwater Vehicle (AUV) or the sink node. However, these approaches cause delay and do not guarantee data delivery to the sink. The problem could be due to both the higher capacity node and autonomous vehicle that might deviate due to water currents, hence leading to entrapment or local maxima. In addition, existing path planning algorithms do not consider the network coverage of heterogeneous sensor nodes. Consequently, it is important to employ a path planning strategy that utilizes AUV with a unique path movement to collect data with minimum delay and higher data delivery ratio. This research designed and developed an AUV Path planning Data Aggregation Scheme (APDAS) to handle heterogeneous and long distance pipeline sensors without depleting a large amount of sensor energy in UWLSN. The APDAS includes node distribution and path planning strategies for AUV. The node distribution was performed based on the capability and signal coverage of the heterogeneous nodes. Furthermore, the path planning concept was based on sinusoidal sine wave movement for effective traversal of forwarding nodes at the base of the pipeline. Extensive simulation experiments were performed in order to benchmark the performance of the proposed APDAS scheme against baseline schemes. The results of the simulation were evaluated based on Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), and throughput with performance improvements of 13%, 17.8%, and 14.1%, respectively. APDAS was compared with the existing schemes namely, Minimizing Deep-sea Data Collection Delay with Autonomous (MDD-CDA) underwater vehicles and Scalable Heterogeneous Nodes Deployment (SHND) algorithm for monitoring of long-range underwater pipeline. The results obtained were based on the average of both MDD-CDA and SHND, and the percentage was estimated in order to increase the packet delivery while reducing the E2ED and throughput. Thus, the findings have shown the APDAS scheme significantly improved the packet delivery rate and reduced delay during data collection in UWLSN.