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 paper, 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.