An analytical justification is proposed for the design and global routing performance of three pheromone update methods proposed for use in Termite, a swarm intelligent routing algorithm for mobile wireless ad-hoc networks. A simple model is used in order to determine the average amount of pheromone present on a link, as well as some basic aspects of the pheromone dynamics. This includes a tendency towards a one-zero pheromone distribution favoring the better link. The pheromone update methods are investigated with the perspective that link pheromone is more an estimate of link utility than simply a routing heuristic. This allows the routing solution to be rephrased from a biological analogy to a more traditional best-metric routing terminology. A signal estimation perspective is suggested. IntroductionRecent applications of biologically inspired algorithms to routing in mobile wireless ad-hoc networks (MANETs) have shown increased performance over traditional approaches in many critical metrics [1] [2] [3]. It remains unclear as to exactly why they work as well as they do, and how to best take advantage of their positive and negative feedback mechanisms. This paper presents a simple analytical model of Termite, a swarm intelligent MANET routing algorithm [1]. The purpose of this model is to discover * http://wisl.ece.comell.edu/
An analytical framework is presented to study the self-adaptive behavior of probabilistic routing protocols for computer networks. Such soft routing protocols have attracted attention for delivering packets more reliably, robustly, and efficiently than conventional deterministic approaches. Efficient global operating parameters can be estimated without resorting to expensive Monte-Carlo simulation of the whole system. Key model parameters are routing sensitivity and routing threshold/noise, which control the "randomness" of packet routes between source and destination, and a metric estimator. Global network characteristics are estimated, including steady state routing probabilities, average path length, and path robustness.The framework is based on a Markov chain analysis. Individual network nodes are represented as states. Standard techniques are used to find primary statistics of the steady state global routing pattern, given a set of link costs. The use of packets to collect information about, or "sample," the network for new path information is also reviewed. How the network sample rate influences performance is investigated.
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