Certain investigations on fuzzy induced routing optimization for improving data transmission security and energy of wireless sensor networks

Abstract

A heterogeneous set of wireless devices/ motes called nodes are newlineconnected to form Wireless Sensor Networks (WSNs). The nodes are capable newlineof transmitting, receiving, and routing data and neighbors across different newlinecommunication ranges. Therefore a wireless node acts as a router and hosts newlineindependently or collaboratively with other neighbours. These nodes possess newlinedistinguishable characteristics as they are tiny and restricted to limited newlinehardware designs. Besides, the nodes are resource-constraint by hardware newlinefabrication and hence they rely on a built-in battery for their operations. The newlinenodes rely on radio resources for communication by identifying direct and newlineindirect neighbors. In the routing process, a host/ router node discovers its in newlinerange and out-of-range neighbors using beacons and control messages. The in newlinerange is decided by the maximum communication distance offered by the newlinenode; the direct neighbors are said to be one-hop and the rest as multi-hop. newlineThe nodes rely on Layer-3 and Layer-4 protocols for route discovery and data newlineforwarding. The network possesses exceptional features such as scalability, newlineadaptability, energy harvesting, clustering, etc. These features are optimal for newlineenergy efficiency, security, and load handling for different real-time newlineapplications. Considering these features, this research proposal introduces newlinethree contributions that balance data transmission, energy efficiency, routing, newlineand security aspects of the WSN. newlineThe first contribution is the Query-based Location-Aware newlineEnergy-Efficient Secure Multicast Routing (QLAMSR). This method eyes data newlinetransmission, energy efficiency, and security of the network amid different newlineadversaries. The considered analyzing factors are node location, remaining newlineenergy, and authentication keys. The optimization is enhanced using a fuzzy newlinedecision that synchronizes all three aforementioned factors. First, the location newlineof the nodes is identified for routing the destination depending on their initial newlineiv newlineenergy. In the routing pro