Biblio

Many complex dynamical systems consist of a large number of interacting subsystems that operate harmoniously and make decisions that are designed for the benefit of the entire enterprise. If, in an attempt to disrupt the operation of the entire system, one subsystem gets attacked and is made to operate in a manner that is adversarial with the others, then the entire system suffers, resulting in an adversarial decision-making environment among its subsystems. Such an environment may affect not only the decision-making process of the attacked subsystem but also possibly the other remaining subsystems as well. The disruption caused by the attacked subsystem may cause the remaining subsystems to either coalesce as a unified team making team-based decisions, or disintegrate and act as independent decision-making entities. The decision-making process in these types of complex systems of systems is best analyzed within the general framework of cooperative and non-cooperative game theory. In this paper, we will develop an analysis that provides a theoretical basis for modeling the decision-making process in such complex systems. We show how cooperation among the subsystems can produce Noninferior Nash Strategies (NNS) that are fair and acceptable to all subsystems within the team while at the same time provide the subsystems in the team with the security of the Nash equilibrium against the opposing attacked subsystem. We contrast these strategies with the all Nash Strategies (NS) that would result if the operation of the entire system disintegrated and became adversarial among all subsystems as a result of the attack. An example of a system consisting of three subsystems with one opposing subsystem as a result of an attack is included to illustrate the results.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.