ABSTRACT
With the advent of Micro-Electrical-Mechanical Systems (MEMSs) technology, complex and ubiquitous control of the physical environment by machines will facilitate the diversity of mechanization and automation, long promised by visionaries. Wireless sensor networks have appeared as the first generation of this revolutionary technology. The small size and low cost of sensor devices will enable deployment of massive numbers, but initially place severe limitations on the computing, communicating, and power capabilities of these devices. With these constraints, research efforts have concentrated on developing techniques for executing simple tasks with minimal energy expense. But, as MEMS evolve, computing and communicating capabilities are expected to improve at an accelerating rate and new techniques for supplying energy will significantly reduce the low power constraint. Increased capabilities will be possible, and it is predicted that societies of machines will evolve to be autonomous, cooperative, fault-tolerant, selfregulating, and self-healing. Improvements in biomimetic software and evolvable hardware will lead to self-sustaining communities of machines with emergent behavior that autonomously operate and adapt to changes in the environment. The main goal of this chapter is to investigate biomimetic models in relation to their potential application to the evolution of these systems, thus providing a framework that guides
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the evolution from the current primitive organizations of sensor nodes to pervasive societies of intelligent electromechanical systems.
