We consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a source-specific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multi-hop fashion until it reaches a data collector (the IoT gateway). The problem we address is to compute a suitable rate-distortion working point for the compression scheme at the source nodes, while jointly assessing the most energy efficient routing paths for the data they transmit, under channel access, distortion and capacity constraints. This is formulated as a multi-objective optimization problem that is solved through distributed learning algorithms, where source coding and routing configurations emerge as the result of local interactions among the network devices. Our final algorithm is based on the alternating direction method of multipliers (ADMM), which is accelerated using the most recent findings from the literature. As a result, it has faster convergence (up to three times) to the global optimum than standard ADMM. Numerical results are discussed for selected network scenarios, emphasizing the interrelations that exist between signal reconstruction quality at the IoT gateway and total transport energy in the network.

中文翻译：

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物联网网络中最优数据路由的分布式学习算法

我们考虑分布式物联网（IoT）中联合有损数据压缩和数据路由的问题。异构源使用特定于源的有损压缩方案来压缩其数据，其中异构性是指信号类型和/或传输速率。因此，已压缩的数据以多跳的方式传播，直到到达数据收集器（IoT网关）为止。我们要解决的问题是为源节点处的压缩方案计算合适的速率失真工作点，同时在信道访问，失真和容量约束下，针对它们传输的数据共同评估最节能的路由路径。这被表述为通过分布式学习算法解决的多目标优化问题，由于网络设备之间进行本地交互，因此出现了源代码和路由配置。我们的最终算法基于乘法器的交替方向方法（ADMM），该方法使用文献中的最新发现进行了加速。结果，与标准ADMM相比，它具有更快的收敛速度（最多三倍）达到全局最优。讨论了针对特定网络场景的数值结果，强调了物联网网关的信号重建质量与网络中的总传输能量之间的相互关系。与标准ADMM相比，它具有更快的收敛速度（最多三倍）达到全局最优。讨论了针对特定网络场景的数值结果，强调了物联网网关的信号重建质量与网络中的总传输能量之间的相互关系。与标准ADMM相比，它具有更快的收敛速度（最多三倍）达到全局最优。讨论了针对特定网络场景的数值结果，强调了物联网网关的信号重建质量与网络中的总传输能量之间的相互关系。