The emergence of the Internet-of-Things and cyber-physical systems necessitates the coordination of access to limited communication resources in an autonomous and distributed fashion. Herein, the optimal design of a wireless sensing system with n sensors communicating with a fusion center via a collision channel of limited capacity k (k < n) is considered. In particular, it is shown that the problem of minimizing the mean-squared error subject to a threshold-based strategy at the transmitters is quasi-convex. As such, low complexity, numerical optimization methods can be applied. When coordination among sensors is not possible, the performance of the optimal threshold strategy is close to that of a centralized lower bound. The loss due to decentralization is thoroughly characterized. Local communication among sensors (using a sparsely connected graph), enables the on-line learning of unknown parameters of the statistical model. These learned parameters are employed to compute the desired thresholds locally and autonomously. Consensus-based strategies are investigated and analyzed for parameter estimation. One strategy approaches the performance of the decentralized approach with fast convergence and a second strategy approaches the performance of the centralized approach, albeit with slower convergence. A hybrid scheme that combines the best of both approaches is proposed offering a fast convergence and excellent convergent performance.

中文翻译：

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有和没有本地通信的冲突信道上的分布式远程估计

物联网和网络物理系统的出现需要以自主和分布式的方式协调对有限通信资源的访问。在此，考虑了具有通过有限容量 k (k < n) 的碰撞通道与融合中心通信的 n 个传感器的无线传感系统的优化设计。特别是，它表明在发射机处采用基于阈值的策略来最小化均方误差的问题是准凸的。因此，可以应用低复杂度的数值优化方法。当传感器之间无法协调时，最优阈值策略的性能接近集中下限的性能。权力下放造成的损失得到了彻底的表征。传感器之间的本地通信（使用稀疏连接图）可以在线学习统计模型的未知参数。这些学习到的参数用于本地和自主地计算所需的阈值。研究和分析了基于共识的策略以进行参数估计。一种策略以快速收敛接近分散方法的性能，第二种策略接近集中式方法的性能，尽管收敛速度较慢。提出了一种结合了两种方法的优点的混合方案，可提供快速收敛和出色的收敛性能。研究和分析了基于共识的策略以进行参数估计。一种策略以快速收敛接近分散方法的性能，第二种策略接近集中式方法的性能，尽管收敛速度较慢。提出了一种结合了两种方法的优点的混合方案，可提供快速收敛和出色的收敛性能。研究和分析了基于共识的策略以进行参数估计。一种策略以快速收敛接近分散方法的性能，第二种策略接近集中式方法的性能，尽管收敛速度较慢。提出了一种结合了两种方法的优点的混合方案，可提供快速收敛和出色的收敛性能。