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Register a new userIntegrated System for Malicious Node Discovery and Self-destruction in Wireless Sensor Networks
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Added by
Ovidiu Banias
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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With the tremendous advances of the wireless devices technology, securing wireless sensor networks became more and more a vital but also a challenging task. In this paper we propose an integrated strategy that is meant to discover malicious nodes within a sensor network and to expel them from the network using a node self-destruction procedure. Basically, we will compare every sensor reading with its estimated values provided by two predictors: an autoregressive predictor [1] that uses past values provided by the sensor under investigation and a neural predictor that uses past values provided by adjacent nodes. In case the absolute difference between the measured and the estimated values are greater then a chosen threshold, the sensor node becomes suspicious and a decision block is activated. If this block decides that the node is malicious, a self-destruction procedure will be started.
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In past years there has been increasing interest in field of Wireless Sensor Networks (WSNs). One of the major issue of WSNs is development of energy efficient routing protocols. Clustering is an effective way to increase energy efficiency. Mostly, heterogenous protocols consider two or three energy level of nodes. In reality, heterogonous WSNs contain large range of energy levels. By analyzing communication energy consumption of the clusters and large range of energy levels in heterogenous WSN, we propose BEENISH (Balanced Energy Efficient Network Integrated Super Heterogenous) Protocol. It assumes WSN containing four energy levels of nodes. Here, Cluster Heads (CHs) are elected on the bases of residual energy level of nodes. Simulation results show that it performs better than existing clustering protocols in heterogeneous WSNs. Our protocol achieve longer stability, lifetime and more effective messages than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC).
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