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Distributed Optimisation of MRF-Based Sensor Networks via Dual Decomposition

机译:通过双重分解分布基于MRF的传感器网络的优化

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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)中的主要挑战是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制定为Markov随机场(MRF)能量最小化,最近提出了一种用于分布式WSN算法设计的一般MRF的框架。建立在此框架和双重分解理论上,我们开发了一种新颖的WSN优化算法,适用于各种网络问题,如路由和功率控制。我们的算法是完全分布的,其特征在于,每个节点处理简单,确定性和相同。通过网络路由通过网络被避免,因为节点专门从其本地社区处理信息。我们将算法应用于多个访问资源分配问题,并展示了全局最佳的快速收敛性。

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