Tremendous amounts of data are generated by sensors and connected devices with high velocity in a variety of forms and large volumes. These characteristics, defined as big data, need new models and methods to be processed in near real-time. The nature of decentralized large-scale data sources requires distributed algorithms in which it is assumed that the data sources are capable of processing their own data and collaborating with neighbor sources. The network objective is to make an optimal decision, while the data are processed in a distributed manner. New technologies, like next generation of wireless communication and 5G, introduce practical issues such as imperfect communication that should be addressed. In this paper, we study a generalized form of distributed algorithms for decision-making over decentralized data sources. We propose an optimal algorithm that uses optimal weighting to combine the resource of neighbors. We define an optimization problem and find the solution by applying the proposed algorithm. We evaluate the performance of the developed algorithm by using both mathematical methods and computer simulations. We introduce the conditions in which the convergence of proposed algorithm is guaranteed and prove that the network error decreases considerably in comparison with some of the known modern methods.

中文翻译：

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对通信不完善的分散数据源进行优化的分布式大规模分析

传感器和连接设备以各种形式和大容量高速生成大量数据。这些被定义为大数据的特征需要新的模型和方法来近乎实时地处理。分散的大规模数据源的性质需要分布式算法，其中假设数据源能够处理自己的数据并与邻居源协作。网络的目标是做出最优决策，同时以分布式方式处理数据。新技术，如下一代无线通信和 5G，引入了一些实际问题，例如应该解决的不完善通信。在本文中，我们研究了分布式算法的一种广义形式，用于对分散数据源进行决策。我们提出了一种使用最优权重来组合邻居资源的最优算法。我们定义一个优化问题并通过应用所提出的算法找到解决方案。我们通过使用数学方法和计算机模拟来评估所开发算法的性能。我们介绍了保证所提出算法收敛的条件，并证明与一些已知的现代方法相比，网络误差显着降低。