Artificial Intelligence for Wireless Sensor Networks Enhancement

Whereas the main objective of Artificial Intelligence is to develop systems that emulate the intellectual and interaction abilities of a human being the Distributed Artificial Intelligence pursues the same objective but focusing on human being societies (O’Hare et al., 2006). A paradigm in current use for the development of Distributed Artificial Intelligence is based on the notion of multi-agent systems. A multi-agent system is formed by a number of interacting intelligent systems called agents, and can be implemented as a software program, as a dedicated computer, or as a robot (Russell & Norving, 2003). Intelligent agents in a multi-agent system interact among each other to organize their structure, assign tasks, and interchange knowledge. Concepts related to multi-agent systems, artificial societies, and simulated organizations, create a new and rising paradigm in computingwhich involves issues as cooperation and competition, coordination, collaboration, communication and language protocols, negotiation, consensus development, conflict detection and resolution, collective intelligence activities conducted by agents (e.g. problem resolution, planning, learning, and decision making in a distributed manner), cognitive multiple intelligence activities, social and dynamic structuring, decentralized administration and control, safety, reliability, and robustness (service quality parameters). Distributed intelligent sensor networks can be seen from the perspective of a system composed by multiple agents (sensor nodes), with sensors working among themselves and forming a collective system which function is to collect data from physical variables of systems. Thus, sensor networks can be seen as multi-agent systems or as artificial organized societies that can perceive their environment through sensors. But, the question is how to implement Artificial Intelligence mechanisms withinWireless Sensor Networks (WSNs)? There are two possible approaches to the problem: according to the first approach, designers have in mind the global objective to be accomplished and design both, the agents and the interaction mechanism of the multi-agent system. In the second approach, the designer conceives and constructs a set of self-interested agents whose then evolve and interact in a stable manner, in their structure, through evolutionary techniques for learning. The same difficulty applies when working with a WSN perspective seen from the 4