Urbanization over the next decade will present many complex challenges to cities. Transportation plays an important role in a city’s ability to address urbanization and create resilient, sustainable, inclusive, and safer cities for all. However, challenges introduced by urbanization will decrease the effectiveness of current transportation methodologies. By utilizing innovations in sensing, computing, and communication along with machine learning and real-time data, intelligent transportation systems (ITS) can advance transportation efficiency and effectiveness. Integrating these technologies into intelligent transportation systems can potentially reduce congestion, pollution, energy consumption, and traffic incidents. This integration is at the forefront of academic and industry research. Although testbeds have been developed for the development, testing, and validation of ITS solutions, existing testbeds focus on narrow aspects of the ITS domain and are unable to support large-scale experimentation and integrations. Additionally, access to resources and data is limited and not remotely accessible to potential researchers. In this paper, we propose a smart city and ITS Testbed-As-A-Service. The testbed is located in downtown Chattanooga, TN, and offers a unified platform for a wide range of ITS applications including connected and autonomous vehicles, wireless communication, cooperative transportation, internet-of-things (IoT), and edge-based AI.

Urbanization over the next decade will present many complex challenges to developing cities. The smart city concept aims to address these challenges by exploiting large scale deployments of Internet of Things (IoT) and communication technologies. These technologies generate data that provide quantifiable insights into the state of the infrastructure within a city. Using these insights, cities can more effectively allocate resources, manage services, and enhance the lives of its citizens. The data generated by smart cities is complex and requires high throughput. Advanced data integration platforms must support city-wide data collection, analysis, and storage. These systems must provide features that allow them to scale alongside the growth of the cities to support high rates of data ingestion in large volumes. Additionally, these systems must support low latency response times which is a critical requirement for time sensitive smart city applications. In this paper, we introduce a smart city testbed that will provide a real-world testing environment for applications in areas such as intelligent transportation, pedestrian safety, and autonomous vehicles. The proposed testbed will act as an open platform for researchers and developers to test new sensors, algorithms and more in a live urban environment, allowing them to test before deploying a product or application. In addition to the physical testbed and its capabilities, we will discuss the data integration system and applications responsible for collecting, analyzing, and storing the data generated by the testbed. Lastly, we will introduce an open data platform where researchers can access datasets generated by the testbed.

In smart cities equipped with cameras, one desirable use-case is to detect and track objects. While object detection has been implemented using various methods, object tracking poses a different problem; to track an object requires object permanence to be established between each frame of video. While many technologies have been proposed as a solution for problem, an implementation with scalability in mind has not been developed and poses many new challenges. This paper proposes e-SORT, a solution for scalable object tracking using an enhanced version of the Simple Online and Realtime Tracking (SORT) algorithm. Beyond its scalability, e-SORT stores a mapping of each objects' locations such that the full path of each object is available and several metrics (such as velocity and acceleration) can be calculated. Both e-SORT's abilities and our proposed solution to scalable object tracking are tested and evaluated on Chattanooga Tennessee's live urban testbed.

Monitoring energy consumption data is essential to the everyday workings of power companies; a single uncaught incident outside the standards of normal use can result in financial loss. To minimize the repercussions of an uncaught error, the utilization of forecasting and machine learning can significantly improve the detection of such anomalies in day-to-day operations. This study covers power anomaly detection with the use of deep learning algorithms that have the capability of removing seasonality and trend from data, yielding residual values that are applied in a comparison to values generated from predictive analysis using recurrent neural networks (RNN). Data for this study is provided by Tennessee Valley Authority (TVA).

Falls are common and dangerous for the elderly or individuals with decreased independence or functional limitations. Fall recognition is extremely important for fallers, healthcare providers, and society. Immediate fall recognition triggers emergency services and potentially decreases individuals time with injury without care. Acute post-fall intervention works to mitigate life threatening fall consequences, decrease fall risk through rehabilitation, and improve quality of life. Extended from our research on real-time fall risk estimation with the functional reach test and Timed Up and Go test built in mStroke, a real-time and automatic mobile health system for post-stroke recovery and rehabilitation, our investigation here is expanded to include fall recognition by taking advantage of wearable technologies and machine learning algorithms. Up to three wearable sensors are employed to acquire raw motion data related to activities of daily living or falls. Feature selection and classification on the basis of machine learning algorithms are explored for fall recognition. The fall recognition performances are presented to justify their accuracy and reliability. Meanwhile, the effects of sensor placement/location and the feature number on the recognition performance are also discussed in this paper.