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Abstract Wireless sensor networks (WSNs) consist of tinysensor nodes that communicate with each other over wirelesschannels, often in a hostile environment where nodes can becaptured and compromised. Consequently, an adversary maylaunch a clone attack by replicating the captured nodes to enlargethe compromised areas employing clones. Thus, it is critical todetect clone nodes promptly for minimizing their damage toWSNs. Recently; various clone detection schemes were proposedfor WSNs, considering different types of network configurations,such as device types and deployment strategies. In order tochoose an effective clone detection scheme for a given sensornetwork, the selection criteria play an important role. In this system, we first investigate the selection criteria of clone detectionschemes with regard to device types, detection methodologies,deployment strategies, and detection ranges. We then classify theexisting schemes according to the proposed criteria.
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Classification and Experimental Analysis for Clone Detection Approaches in Wireless Sensor Networks
Abstract Wireless sensor networks (WSNs) consist of tiny sensor nodes that communicate
with each other over wireless channels, often in a hostile environment where nodes can
be captured and compromised. Consequently, an adversary may launch a clone attack
by replicating the captured nodes to enlarge the compromised areas employing clones.
Thus, it is critical to detect clone nodes promptly for minimizing their damage to WSNs.
Recently; various clone detection schemes were proposed for WSNs, considering
different types of network configurations, such as device types and deployment
strategies. In order to choose an effective clone detection scheme for a given sensor
network, the selection criteria play an important role. In this system, we first investigate
the selection criteria of clone detection schemes with regard to device types, detection
methodologies, deployment strategies, and detection ranges. We then classify the
existing schemes according to the proposed criteria. Simulation experiments are
conducted to compare their performances. It is concluded that it is beneficial to utilize
the grid deployment knowledge for static sensor networks; the scheme using the grid
deployment knowledge can save energy by up to 94.44% in comparable performance
(specifically in terms of clone detection ratio and the completion time), as compared to
others. On the other hand, for mobile sensor networks, no existing approach works
efficiently in reducing detection error rate.
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Abstract Existing System Disadvantages Proposed System Advantages Objective System Requirements System Architecture
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