JoWUA

Abstract

Wireless sensor networks (WSNs) provide huge potential in various applications such as ecological monitoring, healthcare, smart environment and various applications such as robotics. Nevertheless, developing an effective mac protocol for WSNS is challenging due to high energy consumption, scalability issues, and delays. This task introduces an organized Mac protocol to cross these boundaries. The proportional scaling cuttlefish optimization approach (PS-COA) is used by the protocol in order to accomplish the following goals: grouping nodes into groups; balancing energy consumption; and stabilizing network performance. Hebbian Plasticity Neural Networks (HPNN-NAI) are able to plan node activity because they are able to differentiate between active and passive nodes. This allows them to reduce the amount of time and delay that is spent in passive mode, which in turn reduces the amount of energy that is used. The outcomes of the experiments indicate that this method, in contrast to the protocol that is now in use, results in an improvement in energy efficiency, a speeding up of data transmission, and an extension of the network's lifetime. Through the use of the innovative combination of PS-COA, MPNC, RC-FFO, and HPNN-NAI, it has been possible to accomplish the goal of achieving significance in the design and efficiency of the Mac Protocol for WSNS.