Smart infrastructure design for Smart Cities

Intelligent Transportation Systems (ITS) is one of the keywords to describe smart cities, aiming at efficient public transport, smart parking, enhanced road safety, intelligent traffic management, onvehicle entertainment, and so on. In ITS, Roadside Unit (RSU) deployment should be well-designed due to it serves as a service provider and a gateway to the Internet for vehicular users. In this article, we propose an RSU deployment strategy which maximizes the communication coverage and reduces the energy consumption of RSUs, simultaneously. We first formulate a multi-objective optimization RSU deployment problem and solve it by an evolutionary algorithm. Then we conduct extensive simulations and simulation results demonstrate that our proposed strategy significantly improves both the energy efficiency and the network connectivity

DESIGNING NEXT GENERATION SMART CITY INITIATIVES - HARNESSING FINDINGS AND LESSONS FROM A STUDY OF TEN SMART CITY PROGRAMS

The proliferation of Smart Cities initiatives around the world is part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. As a framework for urban transformation, Smart City initiatives aim to harness Information and Communication Technologies and Knowledge Infrastructures for economic regeneration, social cohesion, better city administration and infrastructure management. However, experiences from earlier Smart City initiatives have revealed several technical, management and governance challenges arising from the inherent nature of a Smart City as a complex Socio-technical System of Systems . While these early lessons are informing modest objectives for planned Smart Cities programs, no rigorous developed framework based on careful analysis of existing initiatives is available to guide policymakers, practitioners, and other Smart City stakeholders. In response to this need, this paper presents a Smart City Initiative Design (SCID) Framework grounded in the findings from the analysis of ten major Smart Cities programs from Netherlands, Sweden, Malta, United Arab Emirates, Portugal, Singapore, Brazil, South Korea, China and Japan. The findings provide a design space for the objectives, implementation options, strategies, and the enabling institutional and governance mechanisms for Smart City initiatives

Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully designed with respect to the city infrastructure and utilization of resources. Recently, the idea of 'smart' environment, which takes advantage of the infrastructure for better performance of wireless networks, has been proposed. This idea is aligned with the recent advances in design of reconfigurable intelligent surfaces (RISs), which are planar structures with the capability to reflect impinging electromagnetic waves toward preferred directions. Thus, RISs are expected to provide the necessary flexibility for the design of the 'smart' communication environment, which can be optimally shaped to enable cost- and energy-efficient signal transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart Cities would become even more controllable and adaptable, which would subsequently ease the implementation of future communication networks in urban areas and boost the interconnection among private households and public services. In this paper, we describe our vision of the application of RISs in future Smart Cities. In particular, the research challenges and opportunities are addressed. The contribution paves the road to a systematic design of RIS-assisted communication networks for Smart Cities in the years to come.Comment: Submitted for possible publication in IEEE Open Journal of the Communications Societ

Creating sustainability through Smart City Projects

Smart Cities are a key mechanism for facilitating sustainability – be that in the use of resources (e.g. energy, water), the running of city infrastructure (e.g. transport) or in terms of social policy (e.g. politics). Using our experience of a Smart City project, MK:Smart, we describe what role citizen-led innovation could have in promoting long-term sustainable change. Beyond this we detail some of the barriers to success we have identified in the hope that design patterns might help us address these challenges

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges

Smart infrastructure technologies: Crowdsourcing future development and benefits for Australian communities

© 2018 Elsevier Inc. Smart Information and Communications Technology (ICT) is envisaged to provide the capabilities to plan, design, construct, operate and manage Australia\u27s key infrastructure. With over 75% of Australia\u27s population living in cities and accessing public and private goods and services, ICT is positioned as a strategic resource for smart infrastructure developments. In this study, international and domestic stakeholder inputs on the future role of smart ICT in advancing Australia\u27s infrastructure development and operations were crowdsourced for analysis. The study identifies several forms of smart ICT (e.g. building information modelling software) enabled infrastructure that possesses potential to deliver over A$9 billion per annum in domestic economic improvements, with commensurate advancement of communities, regions and urban environments. However, to be effective these smart ICT require enablement through open and interoperable data, sound governance and policy, and government leadership and coordination using dedicated resources. While smart infrastructure development is presently slow and lumbering, the identified smart ICT present as valuable strategic technologies for change and development in domestic communities

Smart Decision-Making via Edge Intelligence for Smart Cities

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and providing these AI applications is non-trivial and will require sufficient computing resources. Traditionally, cloud computing infrastructure have been used to process computationally intensive tasks; however, due to the time-sensitivity of many of these smart city applications, novel computing hardware/technologies are required. The recent advent of edge computing provides a promising computing infrastructure to support the needs of the smart cities of tomorrow. Edge computing pushes compute resources close to end users to provide reduced latency and improved scalability — making it a viable candidate to support smart cities. However, it comes with hardware limitations that are necessary to consider. This thesis explores the use of the edge computing paradigm for smart city applications and how to make efficient, smart decisions related to their available resources. This is done while considering the quality-of-service provided to end users. This work can be seen as four parts. First, this work touches on how to optimally place and serve AI-based applications on edge computing infrastructure to maximize quality-of-service to end users. This is cast as an optimization problem and solved with efficient algorithms that approximate the optimal solution. Second, this work investigates the applicability of compression techniques to reduce offloading costs for AI-based applications in edge computing systems. Finally, this thesis then demonstrate how edge computing can support AI-based solutions for smart city applications, namely, smart energy and smart traffic. These applications are approached using the recent paradigm of federated learning. The contributions of this thesis include the design of novel algorithms and system design strategies for placement and scheduling of AI-based services on edge computing systems, formal formulation for trade-offs between delivered AI model performance and latency, compression for offloading decisions for communication reductions, and evaluation of federated learning-based approaches for smart city applications

The sustainable development of smart cities through digital innovation

The ‘smart city’ concept has been wrought from distinctive theoretical underpinnings. Initially, this term was used to describe those cities that utilized advanced computerized systems to provide a safe, secure, green, and efficient transportation services and utilities to meet the demands of their citizens (Caragliu, Del Bo & Nijkamp, 2011; Hall, Bowerman and Braverman, Taylor, Todosow and Von Wimmersperg, 2000). A thorough literature review suggests that several cities are already using disruptive technologies, including advanced, integrated materials, sensors, electronics, and networks, among others, which are interfaced with computerized systems to improve their economic, social and environmental sustainability (Camilleri, 2015, 2017; Deakin and Al Waer, 2011; Hall et al., 2000). These cities are increasingly relying on data-driven technologies, as they gather and analyze data from urban services including transportation and utilities (Ramaswami, Russell, Culligan, Sharma and Kumar, 2016; Gretzel, Sigala, Xiang and Koo, 2015). Their underlying objective is to improve the quality of life of their citizens (Ratten, 2017; Buhalis and Amaranggana, 2015). Hence, ‘smart cities’ have introduced technological innovations to address contingent issues like traffic congestion; air pollution; waste management; loss of biodiversity and natural habitat; energy generation, conservation and consumption; water leakages and security, among other matters (Camilleri, 2019; 2014; Ahvenniemi, Huovila, Pinto-Seppä and Airaksinen, 2017; Ratten and Dana, 2017; Ratten, 2017). Ecologically-advanced local governments and municipalities are formulating long-term sustainable policies and strategies. Some of them are already capturing data through multisensor technologies via wireless communication networks in real time (Bibri, 2018; Bibri and Krogstie, 2017). Very often, they use the Internet’s infrastructure and a wide range of smart data-sensing devices, including radio frquency identification (RFID), near-field communication (NFC), global positioning systems (GPS), infrared sensors, accelerometers, and laser scanners (Bibri, 2018). A few cities have already started to benefit from the Internet of Things (IoT) technology and its sophisticated network that consists of sensor devices and physical objects including infrastructure and natural resources (Zanella, Bui, Castellani, Vangelista and Zorzi, 2014). Several cities are crunching big data to better understand how to make their cities smarter, more efficient, and responsive to today’s realities (Mohanty, Choppali and Kougianos, 2016; Ramaswami et al., 2016). They gather and analyze a vast amount of data and intelligence on urban aspects, including transportation issues, citizen mobility, traffic management, accessibility and protection of cultural heritage and/or environmental domains, among other areas (Angelidou, Psaltoglou, Komninos, Kakderi, Tsarchopoulos and Panori, 2018; Ahvenniemi et al., 2017). The latest advances in technologies like big data analytics and decision-making algorithms can support local governments and muncipalities to implement the circular economy in smart cities (Camilleri, 2019). The data-driven technologies enable them them to reduce their externalities. They can monitor and control the negative emissions, waste, habitat destruction, extinction of wildlife, etc. Therefore, the digital innovations ought to be used to inform the relevant stakeholders in their strategic planning and development of urban environments (Camilleri, 2019; Allam & Newman, 2018; Yigitcanlar and Kamruzzaman, 2018; Angelidou et al. ,2018; Caragliu et al., 2011). In this light, we are calling for theoretical and empirical contributions that are focused on the creation, diffusion, as well as on the utilization of technological innovations and information within the context of smart, sustainable cities. This Special Issue will include but is not limited to the following topics: • Advancing the circular economy agenda in smart cities; • Artificial intelligence and machine learning in smart cities; • Blockchain technologies in smart cities; • Green economy of smart cities; • Green infrastructure in smart cities; • Green living environments in smart cities; • Smart cities and the sustainable environment; • Smart cities and the use of data-driven technologies; • Smart cities and the use of the Internet of Things (IoT); • Sustainable energy of smart cities; • Sustainable financing for infrastructural development in smart cities; • Sustainable housing in smart cities; • Sustainable transportation in smart cities; • Sustainable tourism in smart cities; • Technological innovation and climate change for smart cities; • Technological innovation and the green economy of smart cities; • Technological innovation and the renewable energy in smart cities; • Technological innovation and urban resilience of smart cities; • Technological innovation for the infrastructural development of smart cities; • The accessibility and protection of the cultural heritage in smart cities; • The planning and design of smart cities; • The quality of life of the citizens and communities living in smart cities; • Urban innovation in smart cities; • Urban planning that integrates the smart city development with the greening of the environment; • Urban planning and data driven technologies of smart cities.peer-reviewe

Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

Fog computing scheduling algorithm for smart city

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used