The demand for highly lightweight decentralised self- management of wireless sensor networks has lead to the pursuit of emergent or bio-inspired solutions. However, many of the algorithms produced to manage a WSN focus' on one managerial aspect or parameter limiting their usefulness and consuming already scarce resources. We have identified sets of common structures and elements of many well-known emergent algorithms. In this paper present one example algorithm that exploited this knowledge to efficiently manage more than one managerial parameter. This algorithm was then tested using simulations (a standard practice for the field). However, when implementing the algorithm on actual devices we soon found some unexpected results. We discuss this phenomenon and suggest some causes aiming to illustrate that current WSN bio-inspired research simulations have limited usefulness in the real world.
['Michael Breza', 'Julie A. McCann']
Lessons in Implementing Bio-inspired Algorithms on Wireless Sensor Networks