the wireless sensor network is a largely growing research field in the recent world. This network has a vast area of implementation now and is gradually increasing day by day. The main use of the wireless sensor network technology is in the environment system, the object tracking system, sensing data from the location where human can’t reach etc. A sensor network is a combination of low-cost sensor devices with a limited range of data transmission and battery power. A sensor node is responsible to collect sensed data and send those data to the base station and the base station processes those data. Normally a sensor network requires a fixed amount of energy to collect a bit of data. The battery use of the sensor nodes depends on the data collected and transmitted to the base station and also the data transmission range. So, it is very difficult for a sensor network to send data directly to the base station as some sensor nodes may be placed at a long distance from the base station. Then to send data to the base station will finish all its power and the node will die soon. This is the reason the sensor nodes use the clustering technique where the nodes send data to its cluster head and the cluster head forwards data as a tree structure to the base station. This assures a better lifetime of the sensor devices. Some common well known lifetime optimization algorithms are- LEACH, LEACH-C, PEGASIS, GROUP, Ant Colony etc [1]. In this paper, we have proposed an Updated Particle Gaggle Optimization based Routing protocol (UPGOR) where energy efficiency of the sensor nodes is the major focus for the routing protocol and finding an optimized path for data forwarding to the base station and data processing through the base station. The UPGOR algorithm takes the energy as the fitness and finds an optimized path among several available paths to route data. At the end of this paper, we compared our simulated experimental results with other relevant algorithms which show a better result obtained by the proposed UPGOR algorithm. The simulation is done in the NS2 simulation in Ubuntu environment and the simulated data then placed to generate the tables and charts.

Particle Gaggle Optimization, Routing, Lifetime, Wireless Sensor Network, Energy Efficiency

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