PROJECT TITLE :
Towards Reconstructing Routing Paths in Large Scale Sensor Networks - 2016
In wireless sensor networks, sensor nodes are typically self-organized, delivering knowledge to a central sink during a multi-hop manner. Reconstructing the per-packet routing path permits fine-grained diagnostic analysis and performance optimizations of the network. The performances of existing path reconstruction approaches, but, degrade rapidly in large scale networks with lossy links. This paper presents Pathfinder, a strong path reconstruction method against packet losses plus routing dynamics. At the node aspect, Pathfinder exploits temporal correlation between a group of packet paths and efficiently compresses the path data using path difference. At the sink aspect, Pathfinder infers packet paths from the compressed data and employs intelligent path speculation to reconstruct the packet methods with high reconstruction ratio. We have a tendency to propose a unique analytical model to research the performance of Pathfinder. We tend to any evaluate Pathfinder compared with 2 most connected approaches using traces from a massive scale deployment and intensive simulations. Results show that Pathfinder outperforms existing approaches, achieving both high reconstruction ratio and low transmission price.
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