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1. A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

   https://doi.org/10.3390/smartcities6030068  

   Leal Sobral, Victor Ariel; Nelson, Jacob; Asmare, Loza; Mahmood, Abdullah; Mitchell, Glen; Tenkorang, Kwadwo; Todd, Conor; Campbell, Bradford; Goodall, Jonathan L. (June 2023, Smart Cities)

   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. 

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2. A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities

   https://doi.org/https://doi.org/10.1016/j.wasman.2018.05.019  

   Jatinkumar Shah, Parth; Anagnostopoulos, Theodoros; Zaslavsky, Arkady; Behdad, Sara (August 2018, Waste management)

   The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins. 

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3. A Latency-Defined Edge Node Placement Scheme for Opportunistic Smart Cities

   https://doi.org/10.1109/PerComWorkshops51409.2021.9430977  

   Madamori, Oluwashina; Max-Onakpoya, Esther; Erhardt, Gregory D.; Baker, Corey E. (January 2021, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops))

   null (Ed.)

   Smart city projects have the potential to improve the management of environmental and public infrastructure. However, the operational and capital expenditures of smart cities can prevent cities from becoming smarter. A notable factor that influences the cost is providing cellular Internet connectivity to IoT devices. 5G has been proposed as a possible solution, but projections show that 5G will not be able to support the load of billions of IoT devices coming online. To mitigate this, people, vehicles, and other nodes in transportation networks can be exploited to transmit non-urgent data by leveraging device-to-device communication in order to reduce cellular connectivity costs associated with smart city sensors. Hence, this paper addresses cost-effective edge node placement in smart cities that opportunistically leverage public transit networks. We introduce an algorithm that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithm is evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky and results show that our algorithm outperforms betweeness and in-degree centrality metrics with a reduction in latency of over 20 minutes. 

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4. Harnessing Uncertainty in Photoresistor Sensor for True Random Number Generation in IoT Devices

   https://doi.org/10.1109/ICCE46568.2020.9042967  

   Degada, Amit; Thapliyal, Himanshu (January 2020, 2020 IEEE International Conference on Consumer Electronics (ICCE))

   null (Ed.)

   Internet of Things (IoT) has facilitated the connection of many smart devices via internet. Recent cyberattacks have shown that resource constrained IoT nodes are easy prey that lead towards compromising the secrecy of the data and vulnerabilities could be exploited remotely to take control of safety-critical systems. Photoresistor sensors have applications in IoT systems, such as smart street lighting, intelligent cameras, light activated smart consumer electronics, smart home, smart healthcare, etc. Building hardware security primitives, such as True Random Number Generator (TRNG), based on the intrinsic properties of photoresistor would be a novel direction to develop cost-savvy IoT security primitives. Therefore, this paper proposes a TRNG prototype that is devised from uncertainty presents in photoresistor sensors. The proposed TRNG prototype does not require any complex interfacing for preprocessing the weak signal, thereby reducing the unnecessary delay and the recurring hardware cost. The proposed prototype employs the novel approach of additive scrambling that aids to sample sensors at a higher rate. The proposed TRNG has an average random bit generation rate of 8 kbps that is better than the recent work in the literature. The quality of randomness was validated by 15 test batteries of NIST STS test. 

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5. Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement

   https://doi.org/10.23919/WONS51326.2021.9415579  

   Madamori, Oluwashina; Max-Onakpoya, Esther; Erhardt, Gregory D.; Baker, Corey E. (January 2021, 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS))

   null (Ed.)

   Smart city projects aim to enhance the management of city infrastructure by enabling government entities to monitor, control and maintain infrastructure efficiently through the deployment of Internet-of-things (IoT) devices. However, the financial burden associated with smart city projects is a detriment to prospective smart cities. A noteworthy factor that impacts the cost and sustainability of smart city projects is providing cellular Internet connectivity to IoT devices. In response to this problem, this paper explores the use of public transportation network nodes and mules, such as bus-stops as buses, to facilitate connectivity via device-to-device communication in order to reduce cellular connectivity costs within a smart city. The data mules convey non-urgent data from IoT devices to edge computing hardware, where data can be processed or sent to the cloud. Consequently, this paper focuses on edge node placement in smart cities that opportunistically leverage public transit networks for reducing reliance on and thus costs of cellular connectivity. We introduce an algorithm that selects a set of edge nodes that provides maximal sensor coverage and explore another that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithms are evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky. Results show that our algorithms consistently outperform edge node placement strategies that rely on traditional centrality metrics (betweenness and in-degree centrality) by over 77% reduction in coverage budget and over 20 minutes reduction in latency. 

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