iPath: Path Inference in Wireless Sensor Networks

ABSTRACT

Recent wireless sensor networks (WSNs) are becomingincreasingly complex with the growing network scale andthe dynamic nature of wireless communications. Many measurementand diagnostic approaches depend on per-packet routingpaths for accurate and fine-grained analysis of the complex networkbehaviors. In this paper, we propose iPath, a novel pathinference approach to reconstructing the per-packet routing pathsin dynamic and large-scale networks.

EXISTING SYSTEM

Recent years have witnessed a rapid growth of sensor networkscale. Some sensor networks include hundreds even thousands of sensor nodes . These networks often employ dynamic routing protocols to achieve fast adaptation tothe dynamic wireless channel conditions. The growing networkscale and the dynamic nature of wireless channel make WSNs become increasingly complex and hard to manage.Reconstructing the routing path of each received packet at thesink side is an effective way to understand the network's complexinternal behaviors . With the routing path of each packet, many measurement and diagnostic approaches are able to conduct effective management and protocol optimizationsfor deployed WSNs consisting of a large number ofunattended sensor nodes.

DISADVANTAGES

  • These networks often employ dynamic routing protocols to achieve fast adaptation the dynamic wireless channel conditions. The growing network scale and the dynamic nature of wireless channel make WSNs become increasingly complex and hard to manage.
  • depends on therouting path information to build a Bayesian network for inferringthe root causes of abnormal phenomena.

PROPOSED SYSTEM

we propose iPath, a novel pathinference approach to reconstructing the per-packet routing paths

in dynamic and large-scale networks. The basic idea of iPath isto exploit high path similarity to iteratively infer long paths fromshort ones. iPath starts with an initial known set of paths andperforms path inference iteratively. iPath includes a novel designof a lightweight hash function for verification of the inferred paths.In order to further improve the inference capability as well as theexecution efficiency, iPath includes a fast bootstrapping algorithmto reconstruct the initial set of paths. We also implement iPathand evaluate its performance using traces from large-scale WSNdeployments as well as extensive simulations. Results show thatiPath achieves much higher reconstruction ratios under differentnetwork settings compared to other state-of-the-art approaches.

ADVANTAGES

  • we propose iPath, a novel path inference approach to reconstruct routing paths at the sink side.
  • The basic idea of iPath is to exploit high path similarity to iteratively infer long paths from short ones. iPath starts with a known set of paths (e.g., the one-hop paths are already known) and performs path inference iteratively.

MODULES

  • network model
  • ipath design
  • analysis

SYSTEM CONFIGURATION

HARDWARE CONFIGURATION

Processor-Pentium –IV

Speed- 1.1 Ghz

RAM- 256 MB(min)

Hard Disk- 20 GB

Key Board- Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor- SVGA

SOFTWARE CONFIGURATION

Operating System - Windows Family

Programming Language - JAVA

Java Version - JDK 1.6 & above.