Abstract

Considering several fundamental needs of network performance, the Routing and Media Access Control (MAC) layers are crucially checked out for their operations. Sometimes, MAC protocol faces difficulties in modifying the situations over underwater communication which are specifically considered to be dynamic. The nodes in water tend to remain unstable and network fluctuations are made depending upon the number of competitors. The existing hybrid MAC protocol does not possess dynamic network loads. It also involves a lack of modification in access control approaches which provide variations in network loads. Finally, it leads to delayed network performance. The routing layer focus on some difficult parameters like increased packet loss, restricted bandwidth with substantial energy incursion, reduced network lifetime, increased propagation delay, less remaining energy, consistent data transfer, and energy organization which are considered to be the most important research issues in Underwater Acoustic Sensor Networks (UWASNs). In MAC and routing layers, a Cross-layer approach involves some techniques which are discussed and implemented using Ns3 simulation to obtain the performance report. It is also proposed to compare the algorithm with existing techniques to provide accurate results. Reinforcement Learning (RL) in the MAC layer for SINR-based time slot allocation and Integrated Artificial Fish Swarm with Bacterial Foraging Algorithm (IAFSBFA) for energy-aware QoS reliable routing in the network layer are used in this system. In this cross-layer approach, load balancing in the MAC layer and efficient routing using a hybrid algorithm gives out better performance in the underwater network.

Full Text
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