Efficient, sustainable and secure use of smart city resources

The rapid advancement and increased use of technology has introduced the concept of smart cities, driving cities around the world towards developing a wide variety of smart systems, solutions and services. While these smart components provide improvements to various city operations, from increased resource efficiency and sustainability to general quality of life enhancements, they also introduce many challenges that must be dealt with in order to ensure future success. The purpose of this thesis is to determine the necessary measures both current and future smart cities should take in order to use their resources in an efficient, sustainable and secure manner. This was primarily achieved through extensive theoretical research, using a wide variety of information sources, from various scientific publications to different web-based resources. In addition, a simulated model of a smart waste management system was designed in this thesis in order to aid the development of the Salo smart city project. Resource efficiency and sustainability are integral to successful smart city implementations, as they ensure that smart cities can continue to prosper and develop more and more advanced solutions and services in the future. Consequently, the importance of solutions such as smart energy, smart waste management and smart mobility will only continue to increase in the future. Moreover, smart cities must be prepared to deal with various challenges presented by the use of advanced technologies and smart systems - from security, privacy and service availability to people- and ethics-related challenges. In the final parts of the thesis, the aforementioned topics were discussed from the perspective of the Salo smart city project. The use of different security measures and cheaper smart solutions, such as the smart waste recycling centre designed in the thesis, were given as recommendations to guide Salo towards a more resource efficient, sustainable and secure future

Waste Management in the Smart City: Current Practices and Future Directions

The discourse surrounding sustainability, particularly in the urban environment, has gained considerable momentum in recent years. The concept of a smart city epitomises the integration of innovative technological solutions with community-centred approaches, thereby laying the ground- work for a sustainable lifestyle. One of the crucial components of this integration is the effective and innovative management of waste. The aim of this article was to classify scientific research pertaining to waste management within the context of smart city issues, and to identify emerging directions for future research. A systematic literature review, based on a bibliometric analysis of articles included in the Scopus and Web of Science databases, was conducted for this study. The purpose of such a systematic review is to identify, integrate, and evaluate research on a selected topic, using clearly defined criteria. The research query included: TITLE-ABS-KEY (“smart city” AND (waste OR garbage OR trash OR rubbish)) in the case of Scopus, and TS = (“smart city” AND (waste OR garbage OR trash OR rubbish)) in the case of the Web of Science database. A total of 1768 publication records qualified for the analysis. This study presents an investigation into the current and forthcoming directions of waste management in smart cities, synthesising the latest advancements and methods. The findings outline specific future research directions encompassing technological advancement, special waste challenges, digitisation, energy recovery, transportation, community engagement, pol- icy development, security, novel frameworks, economic and environmental impact assessment, and global implications. These insights reflect a multifaceted approach, advocating a technology-driven perspective that is integral to urban sustainability and quality of life. The study’s findings provide practical avenues for cities to enhance waste management through modern technologies, promoting efficient systems and contributing to sustainable urban living and the circular economy. The insights are vital for policymakers and industry leaders globally, supporting the creation of universal stan- dards and policies, thereby fostering comprehensive waste management systems aligned with global sustainability objectives

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Enabling Energy Smart Cities through Urban Sharing Ecosystems

Abstract In order to build real smart cities, heterogeneous data from different sources has to be properly collected, integrated and shared. In this paper, a real district scale example of urban sharing ecosystem based on coopetition is presented. This digital ecosystem enables data sharing that can be synergically applied to different sectors relevant to the urban context, e.g., energy and transportation, in order to create innovative solutions for energy monitoring, citizen engagement, and evaluation and monitoring at district and city level

Revolutionising the quality of life: the role of real-time sensing in smart cities

To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation.This work was supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086. Rui Miranda was supported by grant no. UMINHO/BID/2021/137; Carlos Alves was supported by grant nos. 2022.12629.BD and UMINHO/BID/2021/134; Regina Sousa was supported by grant no. UMINHO/BID/2021/136; António Chaves was supported by grant no. UMINHO/BID/2021/135; Larissa Montenegro was supported by grant no. UMINHO/BID/2022/53

Innovation Policy Roadmapping for the Future Finnish Smart City Digital Twins : Towards Finland National Digital Twin Programme

Smart City Digital Twins (SCDTs) emerge as a transforming concept with the ability to redefine the future of cities in the fast-paced evolving landscape of urban development. This qualitative futures research explores thoroughly into the complex interaction of socio-technical dynamics in the Finnish setting, investigating the several ways SCDTs might revolutionise urban spaces and create resilience. By utilizing Innovation Policy Roadmapping (IPRM) method for the first time on SCDTs, it reveals the diverse capacities of SCDTs across domains such as urban planning, scenario developing, What-IF analysis, and public involvement through a rigorous examination of academic literature and multi-level analysis of expert interviews. The research emphasises the critical role of policymakers and sectoral actors in building an environment that allows Finnish SCDTs to survive in the face of technological improvements. Furthermore, it emphasises the convergence of SCDTs and Futures Studies approaches, giving a visionary path to adaptable and forward-thinking urban futures. The contributions of this study extend beyond the scope of Finnish SCDTs, giving inspiration for sustainable smart city transformations, potential foundational insights towards Finland National Digital Twin Programme and paving the way for the incorporation of futures studies methodologies and digital twins to mitigate uncertainties and create resilient urban futures. Longitudinal impact assessments, real-time citizen-centric foresight applications via SCDT, and the investigation of SCDTs' role in disaster mitigation and social well-being are among the identified future research directions, providing a comprehensive roadmap for leveraging SCDTs as transformative tools for building sustainable urban futures

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

In smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods