Bio-inspired and Voronoi-based algorithms for self-positioning autonomous mobile nodes

We introduce two new self-positioning techniques for autonomous nodes in a mobile ad hoc network to spread over unknown two-dimensional deployment terrains. In our first node self-spreading algorithm, called NSVA, each node moves according to the Voronoi tessellation of its sensing area. Our second self-positioning technique, called NSVGA, is based on a genetic algorithm that utilizes the area of moving node´s Voronoi cell as a fitness function. To establish a basis for our comparisons, we also include the results for nodes moving to the next positions by means of the distributed self-spreading algorithm, called DSSA. We present formal analysis of NSVA, NSVGA, and DSSA to evaluate the area covered by all nodes (NAC) and the average distance traveled (ADT) by nodes until a desired network topology is reached. Simulation experiments demonstrate that both NSVA and NSVGA perform well with respect to NAC, ADT, and convergence speed. Our NSVGA is able to improve NAC considerably faster in the initial steps of the experiments than NSVA and DSSA. On the other hand, a node running NSVA travels a shorter distance on the average than a NSVGA node before reaching a desired network topology. We show that our NSVA and NSVGA are good candidates for self-spreading autonomous nodes that provide power-efficient solutions for many military and civilian applications.