By-Passing Infected Areas in Wireless Sensor Networks using BPR

Abstract

Abnormalities in sensed data streams indicate the spread of malicious attacks, hardware failure and software corruption among the different nodes in a wireless sensor network. These factors of node infection can affect generated and incoming data streams resulting in high chances of inaccurate data, misleading packet translation, wrong decision making and severe communication disruption. This problem is detrimental to real-time applications having stringent quality-of-service (QoS) requirements. The sensed data from other uninfected regions might also get stuck in an infected region should no prior alternative arrangements are made. Although several existing methods (BOUNDHOLE and GAR) can be used to mitigate these issues, their performance is bounded by some limitations, mainly the high risk of falling into routing loops and involvement in unnecessary transmissions. This paper provides a solution to by-pass the infected nodes dynamically using a twin rolling balls technique and also divert the packets that are trapped inside the identified area. The identification of infected nodes is done by adapting a Fuzzy data clustering approach which classifies the nodes based on the fraction of anomalous data that is detected in individual data streams. This information is then used in the proposed by-passed routing (BPR) which rotates two balls in two directions simultaneously: clockwise and counter-clockwise. The first node that hits any ball in any direction and is uninfected, is selected as the next hop. We are also concerned with the incoming packets or the packets-on-the-fly that may be affected when this problem occurs. Besides solving both of the problems in the existing methods, the proposed BPR technique has greatly improved the studied QoS parameters as shown by almost 40 percent increase in the overall performance.