Abstract
With the burgeoning of underwater Internet of things, the amount of data generated by sensor nodes increases dramatically in UASNs, requiring efficient data collection schemes. The data collection process mainly considers MAC and routing design to ensure the link efficiency of sensing data to the sink, which refers to collision avoidance and high bandwidth utilization at low signaling overhead. In recent underwater MAC designs, multi-channel schemes have been adopted as an effective way to eliminate collisions. However, existing multi-channel schemes face the problem of low bandwidth utilization, resulting in a reduction in the performance of network throughput and end-to-end delay. Besides, in the routing design, the unbalanced transfer load may lead to partial transmission congestion, which also decreases the overall bandwidth utilization. To solve the above problems, in this paper, we propose a Link-Efficiency Transmission Protocol (LETP). In the routing layer, we propose a forwarding node probabilistic selection approach to solve the unbalanced transfer load problem at low signaling overhead. In the MAC layer, with the assistance of routing information, a Link Efficiency Channel Allocation (LECA) algorithm with low signaling overhead is applied on receiver sides to allocate dedicated communication channels to senders and optimize the allocation decision based on channel characteristics. Simulation results verify that LETP achieves a better network performance in comparison with existing protocols.
This work is supported by the National Natural Science Foundation of China (NSFC) (Grants No. U19A2061, No. 61772228, No. 61902143), the Natural Science Foundation of Jilin Province, China under Grant No. YDZJ202101ZYTS191, Jilin Scientific and Technological Development Program (No. 2020122208JC), Research Project by the Education Department of Jilin Province (No. JJKH20211105KJ).
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Wei, X., Wang, X., Xu, H., Wang, X., Guo, H. (2022). Link-Efficiency Multi-channel Transmission Protocol for Data Collection in UASNs. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_5
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