The research challenge of current Wireless Sensor Networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate---consisting of low-power, low-cost all-analog sensors---that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using Analog Joint Source Channel Coding (AJSCC) realized via the characteristics of Metal Oxide Semiconductor Field Effect Transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large scale high density and persistent WSN deployment.

中文翻译：

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用于大规模和高密度持续无线监测的节能模拟传感

当前无线传感器网络 (WSN) 的研究挑战是为环境监测等应用设计节能、低成本、高精度、自愈和可扩展的系统。传统的 WSN 由低密度、耗电的数字微尘组成，这些微尘很昂贵，一次充电后无法长时间保持功能。为了应对这些挑战，提出了一种将传感和计算能力分开的哑传感和智能处理架构。传感完全是模拟基板的责任——由低功耗、低成本的全模拟传感器组成——它位于由数字节点组成的传统 WSN 之下，它对从模拟接收到的传感器数据进行所有处理。传感器。使用模拟联合源通道编码 (AJSCC) 提出了一种用于基板传感器的低功耗和低成本解决方案，该解决方案是通过金属氧化物半导体场效应晶体管 (MOSFET) 的特性实现的。数字节点（接收器）还使用机器学习技术估计模拟传感器（发射器）处的源分布，以便找到 AJSCC 的最佳参数，这些参数会传送回模拟传感器，以根据应用需求调整其传感分辨率。所提出的技术已经通过来自 MATLAB 和 LTSpice 的模拟得到验证，显示出有希望的性能，并确实证明了我们的框架可以支持大规模高密度和持久的 WSN 部署。数字节点（接收器）还使用机器学习技术估计模拟传感器（发射器）处的源分布，以便找到 AJSCC 的最佳参数，这些参数会传送回模拟传感器，以根据应用需求调整其传感分辨率。所提出的技术已经通过来自 MATLAB 和 LTSpice 的模拟得到验证，显示出有希望的性能，并确实证明了我们的框架可以支持大规模高密度和持久的 WSN 部署。数字节点（接收器）还使用机器学习技术估计模拟传感器（发射器）处的源分布，以便找到 AJSCC 的最佳参数，这些参数会传送回模拟传感器，以根据应用需求调整其传感分辨率。所提出的技术已经通过来自 MATLAB 和 LTSpice 的模拟得到验证，显示出有希望的性能，并确实证明了我们的框架可以支持大规模高密度和持久的 WSN 部署。