An asymmetrical nash bargaining for adaptive and automated context negotiation in pervasive environments

To cope with the energy performance concern of pervasive and Internet-of-Thing (IoT) devices, current pervasive systems require intelligent algorithms that can change the behavior of the devices and the overall network. Making the devices aware of their states and able to adjust their operative modes using context information has the potential to achieve better energy performance. Context information is typically obtained from environmental sensors, device sensors, and from external sources. In this work, we study the marketing of context-aware services through an adaptive context negotiation model. The negotiation process between context consumers and one or several context providers aims to satisfy the preferences of each the negotiating party concerning the quality-of-context (QoC) levels required by the context consumer. The proposed negotiation model uses an asymmetrical 'Power Bargaining' model in which each negotiating party can influence the other party. It implements a learning algorithm for a symmetrical bargaining model. Numerical evaluation of the model shows the convergence of the Nash Bargaining Solution (NBS) by using a learning algorithm.