Mac layer misbehavior effectiveness and collective aggressive reaction approach
Current wireless MAC protocols are designed to provide an equal share of throughput to all nodes in the network. However, the presence of misbehaving nodes (selfish nodes that deviate from standard protocol behavior in order to obtain higher bandwidth) poses severe threats to the fairness aspects of MAC protocols. In this thesis, investigation of various types of MAC layer misbehaviors is done, and their effectiveness is evaluated in terms of their impact on important performance aspects including throughput, and fairness to other users. Observations obtained from the simulation of misbehaviors show that the effects of misbehavior are prominent only when the network traffic is sufficiently large and the extent of misbehavior is reasonably aggressive. In addition, it is also observed that the performance gains achieved using misbehavior exhibit diminishing returns with respect to its aggressiveness, for all types of misbehaviors considered. Crucial common characteristics among such misbehaviors are identified, and these learnings are used to design an effective measure to react towards such misbehaviors. Employing two of the most effective misbehaviors, it is shown that collective aggressiveness of non-selfish nodes is a possible strategy to react towards selfish misbehavior. Particularly, a dynamic collective aggressive reaction approach is demonstrated to ensure fairness in the network, however at the expense of overall network throughput degradation. In addition, the proposed adaptive reaction strategy provides the necessary disincentive to prevent selfish misbehavior in the network.