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Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia. 

While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location. 

Real-time control of stormwater systems can reduce flooding and improve water quality. Current industry real-time control strategies use simple rules based on water quantity parameters at a local scale. However, system-level control methods that also incorporate observations of water quality could provide improved control and performance. Therefore, the objective of this research is to evaluate the impact of local and system-level control approaches on flooding and sediment-related water quality in a stormwater system within the flood-prone coastal city of Norfolk, Virginia, USA. Deep reinforcement learning (RL), an emerging machine learning technique, is used to learn system-level control policies that attempt to balance flood mitigation and treatment of sediment. RL is compared to the conventional stormwater system and two methods of local-scale rule-based control: (i) industry standard predictive rule-based control with a fixed detention time and (ii) rules based on water quality observations. For the studied system, both methods of rule-based control improved water quality compared to the passive system, but increased total system flooding due to uncoordinated releases of stormwater. An RL agent learned controls that maintained target pond levels while reducing total system flooding by 4% compared to the passive system. When pre-trained from the RL agent that learned to reduce flooding, another RL agent was able to learn to decrease TSS export by an average of 52% compared to the passive system and with an average of 5% less flooding than the rule-based control methods. As the complexity of stormwater RTC implementations grows and climate change continues, system-level control approaches such as the RL used here will be needed to help mitigate flooding and protect water quality.