A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.
Wireless sensor networks are very feasible to monitor where human reach is impossible. But conserving the limited resource and maintaining Quality of service is the challenging task. There is most need to design an algorithm to maintain quality of services. In this paper we have designed node layered division MAC with adaptive listening based on the queue size at the transmitting cluster node. The proposed protocol attenuates the problem of collision and unfair distribution of power among the nodes. When compared with the existing algorithms, IQMAC outperforms in terms of network lifetime, delay, PDR and collision probability.
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