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Distributed Optimisation of MRF-Based Sensor Networks via Dual Decomposition

机译:通过双重分解的基于MRF的传感器网络分布式优化

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A major challenge in wireless sensor networks (WSN) is that spatially distributed nodes need to achieve a global task in a completely decentralised manner. Motivated by the fact that optimisation problems in WSNs can often be formulated as Markov random field (MRF) energy minimisation, a general MRF-based framework for the design of distributed WSN algorithms was proposed recently. Building upon this framework and the theory of dual decomposition, we develop a novel WSN optimisation algorithm that is applicable to a wide variety of network problems such as routing and power control. Our algorithm is completely distributed and is characterised by simple, deterministic and identical per-node processing. Routing of control information through the network is avoided as nodes exclusively process information from their local neighbourhoods. We apply our algorithm to a multiple access resource allocation problem and demonstrate rapid convergence to the global optimum.
机译:无线传感器网络(WSN)的主要挑战在于,空间分布的节点需要以完全分散的方式实现全局任务。由于WSN中的优化问题通常可以表述为Markov随机场(MRF)能量最小化的事实,最近提出了一种基于MRF的通用框架,用于设计分布式WSN算法。在此框架和对偶分解理论的基础上,我们开发了一种新颖的WSN优化算法,该算法适用于各种网络问题,例如路由和功率控制。我们的算法是完全分布式的,并且具有简单,确定性和相同的每个节点处理的特点。由于节点专门处理来自其本地邻居的信息,因此避免了通过网络路由控制信息。我们将我们的算法应用于多路访问资源分配问题,并证明了快速收敛到全局最优。

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