Swarm intelligence Based Route Optimization and Data Aggregation to Enhance Performance in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are communicated as Self-Configured and no fixed topology networks for monitoring and observing physical and natural circumstances viz. temperature, pressure, sound, poisons, and so on., and forward this observed information utilizing a network to a predefined location named as sink node or base station (BS), where this information is dissected. The predetermined sink node or BS fills in as an interface among networks and end clients. Any required information from the WSNs can be recovered by embedding queries and hoarding data through the sink node. An ordinary WSN comprises countless sensor nodes i.e., in hundreds or thousands. The nodes communicate to each other through radio signals. A WSN is apparelled with sensing and processing devices, power components i.e., batteries and radio transceiver. Considering an individual node in WSN, are asset compelled, for example, restricted processing power, storage capacity, and transmission capacity (bandwidth). After deployment of sensor nodes, are liable for organizing themselves in a suitable infrastructure with multi-hop correspondence. The yield of the sensors is generally an electrical sign that is communicated to a regulator for additional handling. As of late, a capable plan of WSNs has become a noteworthy territory of research. newline Whenever requested, the utilization of WSNs over the web, Fault Tolerance (FT) based route optimization is a crucial errand to ameliorate the overall efficiency of utility computing. Consequently, in this thesis, an attempt has been made to perform route optimization dependent on FT and information conglomeration utilizing swarm knowledge way to deal with upgrade performance of WSNs. newline