Received signal strength indication based clustering and aggregating data using Q-learning in mobile Ad Hoc network

Clustering <span>is a significant idea for extending the scalability and enhancing energy in mobile ad-hoc network (MANET). In addition, clustering concept is used to diminishes cost of communication. The re-clustering procedure makes expensive, frequent extra routing overhead utilization. To solve these issues, received signal strength indication (RSSI) based aggregating data (RCAD) using Q-learning MANET proposed. this approach, we build clusters by node RSSI. fuzzy logic system (FLS) select cluster head (CH) mobility utilization energy. Q-learning-based data-aggregation improving efficiency MANET. Here, can find an optimum next-hop utilizing their Q-values established on rewards (RD). Since RD rule decide best solution technique. This computed present bandwidth (PB), (PE), packet delivery (PDD), hop count (HC) parameter selecting aggregator from sender receiver. experimental outcomes illustrate that RCAD approach increases 155 CH round raises 24% lifetime MANET.</span>