Optimal penalties for misbehavior deterrence in communication networks

The communication among entities in any network is administered by a set of rules and technical specifications detailed in the communication protocol. All communicating entities adhere to the same protocol to successfully exchange data. Most of the rules are expressed in an algorithm format that computes a decision based on a set of inputs provided by communicating entities or collected by a central controller. Due to the increasing number of communicating entities and large bandwidth required to exchange the set of inputs generated at each entity, distributed implementations have been favorable to reduce the control overhead. In such implementations, each entity self-computes crucial protocol decisions; therefore, can alter these decisions to gain unfair share of the resources managed by the protocol. Misbehaving users degrade the performance of the whole network in-addition to starving well-behaving users. In this work we develop a framework to derive the optimal penalty strategy for penalizing misbehaving users. The proposed framework considers users learning of the detection mechanism techniques and the detection mechanism tracking of the users behavior and history of protocol offenses. Analysis indicate that escalating penalties are optimal for deterring repeat protocol offenses.