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phd guidance

phd guidance In this paper, we propose iTrust, a probabilistic
misbehavior detection scheme to achieve efficient trust
establishment in DTNs. Different from existing works that
only consider either of misbehavior detection or incentive
scheme, we jointly consider the misbehavior detection and
incentive scheme in the same framework. The proposed
iTrust scheme is inspired from the inspection game [11], a
game theory model in which an inspector verifies if
another party, called inspectee, adheres to certain legal
rules. In this model, the inspectee has a potential interest
in violating the rules while the inspector may have to
perform the partial verification due to the limited
verification resources. Therefore, the inspector could take
advantage of partial verification and corresponding pun-
ishment to discourage the misbehaviors of inspectees.
Furthermore, the inspector could check the inspectee with
a higher probability than the Nash Equilibrium points to
prevent the offences, as the inspectee must choose to
comply the rules due to its rationality.

phd guidance First of all, we assume that each node in the networks is
rational and a rational node’s goal is to maximize its own
profit. In this work, we mainly consider two kinds of DTN
nodes: selfish nodes and malicious nodes. Due to the selfish
nature and energy consuming, selfish nodes are not willing
to forward bundles for others without sufficient reward. As
an adversary, the malicious nodes arbitrarily drop others’
bundles (black hole or gray hole attack), which often take
place beyond others’ observation in a sparse DTN, leading
to serious performance degradation.

phd guidance Note that any of the
selfish actions above can be further complicated by the
collusion of two or more nodes.

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