Drawing Conclusions from Linked Data on the Web: The EYE Reasoner

PROJECT TITLE :

Drawing Conclusions from Linked Data on the Web: The EYE Reasoner

ABSTRACT:

Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless circumstantial network. During this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We have a tendency to are especially fascinated by the insider-attack case, whereby malicious nodes that are half of the route exploit their knowledge of the Communication context to selectively drop a small amount of packets important to the network performance. As a result of the packet dropping rate in this case is like the channel error rate, typical algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to use the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we have a tendency to develop a homomorphic linear authenticator (HLA) primarily based public auditing architecture that permits the detector to verify the truthfulness of the packet loss data reported by nodes. This construction is privacy preserving, collusion proof, and incurs low Communication and storage overheads. To reduce the computation overhead of the baseline theme, a packet-block-based mostly mechanism is also proposed, which permits one to trade detection accuracy for lower computation complexity. Through extensive simulations, we have a tendency to verify that the proposed mechanisms achieve significantly higher detection accuracy than conventional methods like a maximum-chance based detection.

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