A Cross-Layer Adaptive TDMA and Energy-Aware Clustering Protocol for Underwater Acoustic IoT Networks

Underwater Acoustic Internet of Things Networks (UAIoTNs) play vital roles in underwater monitoring, environmental observation and underwater surveillance.In underwater acoustic sensors networks, however, energy efficiency and reliability of communication is difficult since of limited bandwidth, propagation delay and limited battery capacity of sensor nodes.The current TDMA-based MAC and clustering schemes use either fixed scheduling or infrequent reclustering mechanism, resulting in high energy consumption and communication overhead.In order to overcome the above mentioned shortcomings, the present research propose an adaptive Time Division Multiple Access (TDMA) scheduling method coupled with Improved Fast Local Clustering (IFLC) scheme in this work.The adaptive TDMA algorithm dynamically schedules the time slots based on fluctuations in node traffic load and energy, which minimizes idle listening and minimizes packet collisions.The clustering algorithm adopts local parameters like residual energy, node connectivity and link quality to enhance the stability of clusters and prolong the network lifetime.The proposed protocol is tested with extensive simulations under various densities of nodes and underwater scenarios.The performance results show that underwater routing protocols can be enhanced significantly with respect to energy consumption, network lifetime, packet delivery ratio, end-to-end delay, and throughput compared with the existing underwater routing protocols.The results show that the proposed framework is an energy-saving and efficient solution for scalable UAIoTN systems.