Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery

The reliability of ad-hoc networks is gaining popularity in two areas: as a topic of academic interest and as a key performance parameter for defense systems employing this type of network. The ad-hoc network is dynamic and scalable and these descriptions are what attract its users. However, these descriptions are also synonymous for undefined and unpredictable when considering the impacts to the reliability of the system. The configuration of an ad-hoc network changes continuously and this fact implies that no single mathematical expression or graphical depiction can describe the system reliability-wise. Previous research has used mobility and stochastic models to address this challenge successfully. In this paper, the authors leverage the stochastic approach and build upon it a probabilistic solution discovery (PSD) algorithm to optimize the topology for a cluster-based mobile ad-hoc wireless network (MAWN). Specifically, the membership of nodes within the back-bone network or networks will be assigned in such as way as to maximize reliability subject to a constraint on cost. The constraint may also be considered as a non-monetary cost, such as weight, volume, power, or the like. When a cost is assigned to each component, a maximum cost threshold is assigned to the network, and the method is run; the result is an optimized allocation of the radios enabling back-bone network(s) to provide the most reliable network possible without exceeding the allowable cost. The method is intended for use directly as part of the architectural design process of a cluster-based MAWN to efficiently determine an optimal or near-optimal design solution. It is capable of optimizing the topology based upon all-terminal reliability (ATR), all-operating terminal reliability (AoTR), or two-terminal reliability (2TR).