PROJECT TITLE :

Grouping-Enhanced Resilient Probabilistic En-Route Filtering of Injected False Data in WSNs

ABSTRACT:

In wireless sensor networks, the adversary may inject false reports to exhaust network energy or trigger false alarms with compromised sensor nodes. In response to the problems of existing schemes on the security resiliency, applicability and filtering effectiveness, this paper proposes a scheme, referred to as Grouping-enhanced Resilient Probabilistic En-route Filtering (GRPEF). In GRPEF, an efficient distributed algorithm is proposed to group nodes without incurring extra groups, and a multiaxis division based approach for deriving location-aware keys is used to overcome the threshold problem and remove the dependence on the sink immobility and routing protocols. Compared to the existing schemes, GRPEF significantly improves the effectiveness of the en-route filtering and can be applied to the sensor networks with mobile sinks while reserving the resiliency.


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