ISSN (Online) 2278-1021 ISSN (Print) 2319 5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 7, July 2015 Copyright to IJARCCE DOI 10.17148/IJARCCE.2015.4775 326 Power Optimization Using Proposed Dijkstra‟s Algorithm in Wireless Sensor Networks Pankaj Chauhan 1 , Arvind Negi 2 , Tarun Kumar 3 M. Tech, CSE, Uttaranchal University, Dehradun (U.K.), India 1,2 CSE Dept., Uttaranchal University, Dehradun (U.K.), India 3 Abstract: WSN refers to a group of spatially spread and dedicated sensors for watching and recording the physical conditions of the surroundings and organizing the collected information at a central location. Wireless device Networks (WSNs) give a brand new paradigm for sensing and scattering info from varied environments, with the potential to serve several and numerous applications. Current WSNs usually communicate directly with a centralized controller or satellite. On the opposite hand, a sensible WSN consists of various sensors spread across a geographical area; every sensor has wireless communication capability and sufficient intelligence for signal process and networking of the information. The structure of WSNs area unit tightly application-dependent and lots of services are smitten by application linguistics (e.g. application-specific processing combined with information routing). Thus, there's no single typical WSN application, and dependency on applications is over in ancient distributed applications. Therefore, besides the well explored power management techniques on the transceiver activity and wireless transmission, there's a necessity to instigate additionally the ability management on the sensing unit that reduces the power consumption of the power-hungry sensors. In this paper, we provide a proposed method for power optimization in WSN. In the proposed method, we are using Dijkstra‟s algorithm to reduce the power consumption and finding the shortest power consumed path between Source to Destination using minimum number no nodes. Keywords: WSN, power optimization, power hungry sensors, distributed sensing, power optimization methods. 1. INTRODUCTION The need to monitor and measure various physical phenomena (e.g. temperature, fluid levels, vibration, strain, humidity, acidity, pumps, generators to manufacturing lines, aviation, building maintenance and so forth) is common to many areas including structural engineering, agriculture and forestry, healthcare, logistics and transportation, and military applications. Wired sensor networks have long been used to support such environments and, until recently, wireless sensors have been used only when a wired infrastructure is infeasible, such as in remote and hostile locations. But the cost of installing, terminating, testing, maintaining, trouble- shooting, and upgrading a wired network makes wireless systems potentially attractive alternatives for general scenarios. Recent advances in technology have made possible the production of intelligent, autonomous, and energy efficient sensors that can be deployed in large numbers to form self-organizing and self healing WSNs in a geographical area. Moreover, the dramatic reduction in the cost of this wireless sensor technology has made its widespread deployment feasible, and the urgent need for research into all aspects of WSNs has become evident. The WSN has great, long- term potential for transforming our daily lives, if we can solve the associated research problems. The sensors that, when distributed in the environment, comprise WSNs include cameras as vision sensors, micro- phones as audio sensors, and those capable of sensing ultrasound, infra-red, temperature, humidity, noise, pressure and vibration. Although the individual sensor‟s sensing range is limited, WSNs can cover a large space by integrating data from many sensors. Diverse and precise in- formation on the environment may thus be obtained. Sensor networks are an emerging computing platform consisting of large numbers of small, low- powered, wireless motes each with limited computation, sensing, and communication abilities. Fig 1: WSN Overview It is still a challenge to realize a distributed WSN comprising: small and cost effective sensor modules; high speed, low latency and reliable network infrastructures; software platforms that support easy and efficient installation of the WSN; and sensor information processing technologies. Unfortunately, little of this software carries over directly from one application to an- other, since it encapsulates application-specific tradeoffs in terms of complexity, resource usage, and communication patterns. No WSN application will therefore be seen as typical, and application-dependency will be higher than in traditional distributed applications. Recent WSN research has focused increasingly on the application layer and an API at an appropriate abstraction level is needed urgently.