IPath Path Inference in Wireless Sensor Networks

Abstract

IPath Path Inference in Wireless Sensor Networks

With the growing network scale and the dynamic nature of wireless communications, recent IPath Path Inference in Wireless Sensor Networks (WSNs) are becoming increasingly complex. For accurate and fine-grained analysis of complex network behaviors, many measurement and diagnostic approaches depend on per-packet routing paths. 

In this paper, we propose iPath, a novel inference path approach to reconstructing the routing paths per packet in dynamic and large-scale networks. iPath’s basic idea is to exploit high path similarity from short paths to iteratively infer long paths. iPath starts with an initial set of known paths and iteratively performs path inference.

IPath includes an inferred path verification novel design of a lightweight hash function. iPath includes a fast bootstrapping algorithm to reconstruct the initial set of paths to further improve the inference capability as well as the execution efficiency. 

We also implement iPath and use traces from large-scale WSN deployments as well as extensive simulations to evaluate its performance. Results show that under different network settings, iPath achieves much higher reconstruction ratios than other state-of – the-art approaches.

Conclusion

In this paper, we propose IPath, a novel path inference approach to reconstruct the routing path for each packet received. IPath exploits the similarity of the path and uses the iterative boosting algorithm to effectively reconstruct the routing path. The fast bootstrapping algorithm also provides an initial set of paths for the iterative algorithm.

We formally analyze IPath’s reconstruction performance as well as two related approaches. The results of the analysis show that when the network setting varies, IPath achieves a higher reconstruction ratio. We also implement IPath and use a trace-driven study and extensive simulations to evaluate its performance.