Boundary Recognition by Simulating a Diffusion Process in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSN) are becoming increasingly promising in practice. As the predeployment design and optimization are usually unpractical in random deployment scenarios, the global optimum of the WSN’s performance is achievable only if the topology dependent self-organizing process acquires the overview of the WSN, in which the boundary is the most important. The idea of this paper comes from the fact that contours only break on the geometrical boundary and the WSN are discrete sampling systems of real environments. By simulating a diffusion process in discrete form, the end point of semi-contours suggests the boundary nodes of a WSN. The simulation cases show the algorithm is well worked in WSN with average degree higher than 10. The boundary recognition could be very valuable for other algorithms dedicated to optimize the overall performance of WSN.

A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network

“…In Gu et al, 15 the author proposed distributed algorithm for boundary recognition based on iso-contour property. The algorithm works fine for dense networks with greater than 10 nodes surrounding each node.…”