Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. To efficiently round the obtained relaxed solution, we propose a randomized rounding algorithm which generalizes the existing algorithm.

本文研究了能量收集物联网（IoT）网络中用于参数估计的最优传感器选择。具体地，重点在于传感器位置的选择，该位置最小化融合中心处的估计误差，并针对每个选定的传感器在规定的频谱和能量预算的情况下最佳地分配功率和带宽。为此，必须考虑测量精度，通信链路质量和能量收集量。传感器的选择在模拟和数字传输方案下都进行了研究，从选定的传感器到融合中心。在数字传输情况下，信息理论方法用于对传输速率，观察量化和编码进行建模。我们通过数值证明，在足够的系统带宽下，数字系统的传感器选择可能会比模拟系统好。感兴趣的设计问题是布尔非凸优化问题，可以通过放宽布尔约束来解决。为了有效地舍入获得的松弛解，我们提出了一种随机舍入算法，该算法对现有算法进行了概括。