A New Mechanism to Detect Selfish Behavior in IEEE 802.11 Ad Hoc Networks

Liu, C.; Shu, Yantai; Li, M.; Yang, O.W.W.

doi:10.1109/icc.2009.5198643

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…A possible Denial of Service (DoS) attack on the signaling/control plane of the 3G wireless networks based on CDMA2000 and UMTS is introduced and studied in [11], [12]. The key objectives of these studies include classifying different MAC misbehaviour techniques, determining their impact upon well-behaved users, and mitigating the selfish MAC behaviour by designing a new MAC layer protocol that discourages misbehaviour [3] or introducing efficient misbehaviour detection algorithms [13]- [17] together with proper penalty schemes [4]. In non-cooperative environments where users behave selfishly and aim at optimizing their own individual benefits, the game theoretic approach is often applied to model network problems as a game and seek for an efficient Nash equilibrium, see [18]- [20] for some interesting models.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Effect of User Misbehaviours on the Reservation-Based MAC Protocols in Wireless Communication Networks

Wattanamongkhol

Srichavengsup

Vanichchanunt

et al. 2012

IEICE Trans. Commun.

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SUMMARYIn a shared medium communication system, mobile users contend for channel access according to a given set of rules to avoid collisions and achieve efficient use of the medium. If one or more users do not comply with the agree rules either due to selfish or malicious behaviours, they will cause some impacts on the system performance, especially to the well-behaved users. In this paper, we consider the problem of user misbehaviours on the performance of a wireless infrastructure-based network using reservation-based MAC protocols. Key misbehaving strategies possible in such a network are identified and explained. To quantify the impact of these misbehaviours upon the network performance, three different misbehaving scenarios are developed to allow a systematic investigation of each misbehaving strategy. For each scenario, we have derived mathematical formulations for evaluating and analyzing the key performance metrics, i.e., probabilities of success of well-behaved and misbehaved users and the fairness index. Numerical results show that the presence of misbehaviours can cause different levels of damage depending on the misbehavior strategy used. The combined multi-token and increasing permission probability strategies where the misbehaved user selfishly accesses the channel more times and with higher probabilities than allowed is shown to cause the most severe impairment of performance and fairness.

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