Adaptive bio-inspired wireless network routing for planetary surface exploration

Abstract

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc networks at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: In addition the agents are provided with some capabilities not present in real ants, but likely to help solving the problem at hand. For example each ant is able to determine how far away nodes are, what the RF coverage index is, topology favorable index and they all have a memory of which nodes they have already visited. Furthermore, we add the estimated values for next node by tracking the speed of current mobile units. The simulation shows that the method is feasible and more reliable. It is a feasible way to avoid node congestion and network interruptions without much decrease of network performance