QOS and Congestion Aware Routing Protocols with Data Aggregation Technique for WSN’S Assisted IoT

Abstract

A wireless sensor network (WSN) has the potential to be a heterogeneous network, contingent upon its specific application. The energy constraint is a crucial concern within the field of WSN.In a WSN, the use of energy-aware routing protocols is crucial, however, those that just take into account energy parameters often struggle to effectively manage excessive energy consumption.The emergence of congestion in network nodes causes an increase in packet loss and energy usage. The routing algorithms should attempt to balance the load among the sensor nodes and maximize energy efficiency in order to extend the network lifetime.One of the best methods for agglomerating data across sensor nodes effectively is clustering. Frequent CH rotation in clustered networks is the key issue.In this study, we offer an energy-efficient data aggregation & congestion-aware routing method (EDCAR), which takes into account both energy optimization and congestion as two key characteristics during data aggregation in order to maximize network longevity, CH stability, and energy-efficient data aggregation.In this study, a method for choosing the cluster head of a WSN using the grey wolf optimization method is proposed. It takes into account a variety of criteria, including the node's energy level, degree, distance to the sink, distance within the cluster, and priority factor. In order to perform the efficiency requirements of a scalable WSN, collision-aware routing employing the seagull optimization method is also devised.Reduced energy usage at each node is achieved by the suggested collision-aware routing. When choosing congestion-free relays, the proposed seagull optimization method calculates fitness using queue length and network quality.The experimental data demonstrate that the proposed EDCAR scheme increases the network lifetime when compared to the current energy-efficient data aggregation systems.