Abstract A network of wireless sensors is a self‐infrastructure approach with many sensory nodes. The distributed sensory nodes communicate with each other via sensory points. In wireless sensor network (WSN), the sensory nodes collect information for healthcare, military and monitoring systems. Such networks require an exclusive arrangement of the nodes to challenge inherent limitations and energy deficiency. The conventional design of a communication system consumes more energy with high latency causing degraded performance. This study provided a machine learning‐based path optimization mechanism using the least energy resources in designing an effective wireless network system with enhanced three measures of network performance, including throughput, packet delivery efficiency and energy usage. The proposed methodology is validated through network simulation tools.

Access to the internet inside moving vehicles with the proper speed and reliability creates several challenges to avail the service to user's satisfaction, unmanned car operation, traffic control, etc. A Vehicular ad-hoc Network (VANET) is familiar as the appropriate model for broadcasting data among running objects in a certain topographical range and behaves as an intelligent system for road safety and traffic management. A Greedy routing scheme is widely preferred to set a dedicated dynamical path from the source to the destination with the moving bodies that result in several unwanted intermediate nodes as the obstacle to the signal frequencies. The greater number of forwarding nodes in a certain range creates several issues such as process delay, packet loss, etc. leading to a slower and resource-consuming process that is not suitable as the next-generation data dissemination model for ad-hoc network. Hence, a novel Neuro-fuzzy forwarder scheme is proposed for the elimination of undesirable nodes for the achievement of better signal strength with improved performance. Furthermore, the comparative study demonstrates expected performance in path reliability with less latency in end-to-end connection after the node optimization with the proposed system.