© 2016, IJCSE All Rights Reserved 140                               Review Paper Volume-4, Issue-5 E-ISSN: 2347-2693 Efficient Path Reconstruction for Wireless Sensor Network Payel Ray 1* , Ranjan Kumar Mondal 2 , Debabrata Sarddar 3 1 Department of Computer Science & Engineering, University of Kalyani, WB, India 2 Department of Computer Science & Engineering, University of Kalyani, Kalyani, India 3 Assistant Professor, Department of Computer Science & Engineering, University of Kalyani, Kalyani, India Available online at: www.ijcseonline.org Received: Apr/21/2016 Revised: May/04/2016 Accepted: May/18/2016 Published: May/31/2016 Abstract— Recent wireless sensor networks (WSNs) are becoming increasingly complex with the growing network scale and the dynamic nature of wireless communications. Many measurement and diagnostic approaches depend on per-packet routing paths for accurate and fine-grained analysis of the complex network behaviors. In this paper, we propose a Path, a novel path inference approach to reconstructing the per-packet routing paths in dynamic and large-scale networks. The basic idea of the Path is to exploit high path similarity to iteratively infer long paths from short ones. The Path starts with an initial known set of paths and performs path inference iteratively. In order to further improve the inference capability as well as the execution efficiency, it includes a fast bootstrapping algorithm to reconstruct the initial set of paths. We also implement the Path and evaluate its performance using traces from large-scale WSN deployments as well as extensive simulations. Results show that it achieves much higher reconstruction ratios under different network settings compared to other state-of- the-art approaches. Keywords—Measurement, path reconstruction, wireless sensor Network. I. Introduction A Wireless Sensor Network [1] is a dense wireless network of small low cost sensors, which collect and disseminate environmental data. WSNs facilitate monitoring and controlling of physical environments from remote locations with better accuracy [2]. Wireless Sensor Network has applications in a variety of fields such as environmental monitoring, military purpose and gathering sensing information in hospital locations. Sensor nodes have various energy and computational constrains due to their inexpensive nature and ad hoc method of deployment [3]. A wireless sensor network basically does- Sensing: Senses the dada from environment. Computation: each Sensor node made some computation based on sensed data. Communication: the sensed data communicates with other sensor nodes and/or base stations to make a result based computed data. WSN contains large number of tiny nodes that can assemble and configure themselves and are deployed to create a mesh network. The node uses advanced mesh networking protocols. The protocol searches every possible communication path by hopping dada from node to node in search of the destination. Unlike traditional wireless devices, wireless sensor nodes communicate only with its local peers, not with the high Power control tower or base station. These WSNs are able to support very different kind of applications. But working with WSNs leads to many challenges due to their many inherent constraints (for instance coverage and connectivity, power efficiency, localization) [4]. These challenges have emerged many research issues. There is not any common solution which can solve all problems associated with WSN. So many research initiatives have been taken on this field to optimize the performance and overcome the constraints of WSN. II. Applications of Wireless Sensor Network: Sensor network may consist of many different kind of sensors, such as- Seismic sensor, Thermal Sensor, Visual Sensors, Infrared Sensor, Acoustic Sensor, Rader [5]. These sensors are able to monitor a wide variety of environment, such as- Temperature, Humidity, Noise level,