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1. A Blockchain-based Traceability System for Waste Management in Smart Cities

   Gopalakrishnan, Praveen Kumare; Hall, John; Behdad, Sara (August 2020, the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2020, August 16 – 19, 2020, St. Louis, MO)

   Waste tracking is becoming an important concern for developed countries as well as developing regions, where municipalities aim to assure proper waste management considering environmental and economic objectives. Waste tracking is important not only for a transparent reporting system compatible with environmental regulations but also for economically viable waste collection and recovery solutions. In this paper, a waste tracking system based on the blockchain technology is introduced where different entities involved in the system will be able to retrieve required data from the platform and decide on their level of contributions. The conventional technologies do not provide a sufficient level of transparency and coordination among different entities. With the introduction of blockchain as a tamper-proof technology, municipalities can enhance the efficiency of their waste management efforts. The proposed blockchain technology can connect proper stakeholders towards collaboration and sharing information. The concept of a smart contract for waste management is discussed and further, a decision-making framework is developed to guide users of the system select proper services available to them, depending on the level of data sharing, cost, reliability, and the security level that they expect from the system. 

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2. Disaster reconnaissance framework for sustainable post-disaster materials management

   https://doi.org/10.1016/j.wasman.2023.07.010  

   Yeşiller, Nazli; Hanson, James L.; Wartman, Joseph; Turner, Benjamin; Gardiner, Andrea; Manheim, Derek C.; Choi, Juyeong (September 2023, Waste Management)

   A first foundational assessment is provided for disaster debris reconnaissance that includes identifying tools and techniques for reconnaissance activities, identifying challenges in field reconnaissance, and identifying and developing preliminary guidelines and standards based on advancements from a workshop held in 2022. In this workshop, reconnaissance activities were analyzed in twofold: in relation to post-disaster debris and waste materials and in relation to waste management infrastructure. A four-phase timeline was included to capture the full lifecycle of management activities ranging from collection to temporary storage to final management route: pre-disaster or pre-reconnaissance, post-disaster response (days/weeks), short-term recovery (weeks/months), and long-term recovery (months/years). For successful reconnaissance, objectives of field activities and data collection needs; data types and metrics; and measurement and determination methods need to be identified. A reconnaissance framework, represented using a 3x2x2x4 matrix, is proposed to incorporate data attributes (tools, challenges, guides), reconnaissance attributes (debris, infrastructure; factors, actions), and time attributes (pre-event, response, short-term, long-term). This framework supports field reconnaissance missions and protocols that are longitudinally based and focused on post-disaster waste material and infrastructure metrics that advance sustainable materials management practices. To properly frame and develop effective reconnaissance activities, actions for all data attributes (tools, challenges, guides) are proposed to integrate sustainability and resilience considerations. While existing metrics, tools, methods, standards, and protocols can be adapted for sustainable post-disaster materials management reconnaissance, development of new approaches are needed for addressing unique aspects of disaster debris management. 

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3. 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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4. Moving towards Real-time Data-driven Quality Monitoring: A Case Study of Hard Disk Drives

   Raihanian Mashhadi, Ardeshir; Cade, Willie; Behdad, Sara (August 2018, Procedia Manufacturing, SME North American Manufacturing Research Conference)

   Since its emergence, the cloud manufacturing concept has been transforming the manufacturing and remanufacturing industry into a big data and service-oriented environment. The aggressive push toward data collection in cloud-based and cyber-physical systems provides both challenges and opportunities for predictive analytics. One of the key applications of predictive analytics in such domains is predictive quality management that aims to fully exploit the potentials provided by the enormous data collected via cloud-based systems. As a case study, a data set of hard disk drives’ Self-Monitoring, Analysis and Reporting Technology (SMART) attributes from a cloud-storage service provider has been analyzed to derive some insights about the challenges and opportunities of using product lifecycle data. An analysis of time-to-failure monitoring of hard disk drives in real-time has been carried out and the corresponding challenges have been discussed. 

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5. GUIDES: Geospatial Urban Infrastructure Data Engineering Solutions

   https://doi.org/10.1145/3139958.3139968  

   Balasubramani, B. S.; Belingheri, O.; Boria, E. S.; Cruz, I. F.; Derrible, S.; Siciliano, M. D. (October 2017, 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems)

   The digitization of legacy infrastructure constitutes an important component of smart cities. While most cities worldwide possess digital maps of their transportation infrastructure, few have accurate digital information on their electric, natural gas, telecom, water, wastewater, and district heating and cooling systems. Digitizing data on legacy infrastructure systems comes with several challenges such as missing data, data conversion issues, data inconsistency, differences in the data format, spatio-temporal resolutions, structure, semantics and syntax, difficulty in providing controlled access to the datasets, and so on. Therefore, we introduce GUIDES, a new data conversion and management framework for urban infrastructure systems, which is comprised of big data analytics, efficient data management techniques, semantic web technologies, methods to ensure information security, and tools that aid visual analytics. The proposed framework facilitates: (i) mapping of urban infrastructure systems; (ii) integration of heterogeneous geospatial data; (iii) a secured way of storing, analyzing and querying data while preserving the semantics; (iv) qualitative and quantitative analysis over several spatio-temporal resolutions; and (v) visualization of static (e.g., land use) and dynamic (e.g., road traffic) information. 

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