Handbook of Smart Cities

Smart City promises to enhance resource utility, cost-effectiveness, sustainability and living conditions in urban environments by utilizing Internet-of-Things (IoT) infrastructures. This chapter presents a comprehensive survey on the architectural design and key wireless communication technologies that enable Smart City applications. In addition, with the adoption and installation of IoT devices on a city-wide scale, securing these devices and the associated communications networks becomes an important issue. As a result, this chapter then continue with a survey to discuss potential security threats for IoT devices in a Smart-City environment, possible countermeasures and open research issues.

Smart Cities are envisioned to offer its citizens a plethora of services that are aimed at optimizing the use of public assets and improving the quality/efficiency of the regular activities. The performance of these services are reliant on the inter-networking of information between the different and disjointed systems with overlapping purposes. The information exchange process will likely involve the communication of large volumes of data with a range of complex requirements that are application-specific. In this chapter, the role of the 5G systems and Internet of Things (IoT) for meeting such stringent requirements are discussed along with the essential constituents of a successful Smart City infrastructure.

Cloud computing and sensor-based internet of things are two important technologies that are driving the realization of smart cities. This chapter focuses on the use of clouds and sensor-based devices in monitoring and managing smart facilities such as bridges, industrial and aerospace machinery and smart applications. Three different roles of cloud computing are discussed. The first concerns the unification of diverse resources required for collecting and analyzing the data monitored by sensors associated with a smart facility. The second focuses on resource management in platforms executing data analytics applications while the third discusses its use in information dissemination and control in the operation of smart applications. The chapter includes case studies on sensor-based bridges, a cloud-based collaboration platform, popular cloud-based platforms for performing data analytics and two sensor-based smart applications, a museum touring system and a restaurant management system.

Over the past few decades, the population in the urban areas has been increasing in a dramatic manner. Currently, about 80% of the U.S. population and about 50% of the world’s population live in urban areas and the population growth rate for urban areas is estimated to be over one million people per week [1, 2]. By 2050, it has been predicted that 64% of people in the developing nations and 85% of people in the developed world would be living in urban areas [1, 2]. Such a dramatic population growth in urban areas has been placing demands on urban infrastructure like never before [1].

Smart cities aim to improve the quality of citizen's life by infusing technology into every part of operations in the cities. The next generation mobile network, 5G, is considered as a potentially key driver for the emerging global IoT to support smart cities where the various indoor/small cell operations create a new 5G business model—the Micro Operators. This chapter deals with the design and applications of future 5G wireless micro operators for integrated casinos and entertainment (5G ICEMO) in smart cities. We first propose a Concentric Value Circles (CVC) model for analysis of 5G ICEMO and develop the business model. A 5G Cloud-enabled ICEMO system and wireless network architectures are developed. Three illustrating cases of mega jackpot, anti-counterfeiting lottery, and autonomous transport are studied to convey the proposed 5G ICEMO architecture. Benchmarking among Las Vegas, Macao, and Singapore is analyzed to validate how the proposed 5G micro operator framework can be exploited to integrated casinos and entertainment in smart cities.

This chapter reports a prototype implementation of an IoT-based urban infrastructure system for smart cities. More specifically, we propose a real-time sensor network that uses the IEEE 802.15.4 standard for monitoring the available spaces in three car parking areas at City University of Autonomous University of Puebla in Mexico. The first step consists of implementing a local server and a database over a Galileo2 development board from Intel using Linux. Then, a wireless sensor network is implemented using SHARP proximity sensors and XBee modules with the 802.15.4 standard to 2.4 GHz for WSN training. The data, in frame format, are sent to the Intel Galileo2 development board, in which free software is used to install a suitable server and database for the frames of nodes, which represent each car park. Finally, a web page, which is integrated into the same Galileo board, is designed for monitoring the automatic updating of the data from any device that belongs to an external network. In this way, the architecture of the IoT is analyzed by considering 3 layers, namely, Application, Network and Perception, and a 5-layer model: Business, Application, Service Management, Object Abstraction and Objects. This analysis is performed with the purpose of using the compatibility among multiple technologies to fulfill the IoT objective. This research will not only help analyze and validate theoretical results but also enable IoT applications in real-world case studies in developing countries by using affordable hardware and free software.

As smart vehicles begin to roam the streets, new possibilities will emerge for large-scale data acquisition tasks necessary for proactive smart cities applications. Unlike mobile devices, smart vehicles carry powerful sensors and are highly mobile; they can cover large areas and perform high quality sensing. However due to restricted reward structures and limited bandwidths of cellular and VANETs, not all vehicles can participate equally. Thus, we must find a method for selecting promising participants which can efficiently the required collect sensing information. In this chapter, we present ideas for participant selection under varying conditions from large scale crowdsensing to personalized crowdsensing. We present several algorithms using a common framework.

Smart cities is a concept that can be interpreted in many ways. One of them is to consider it as leveraging the wireless and wired Internet to streamline the operation of city-wide infrastructures to maximize their operational efficiencies and offer new services to the citizens. Many existing or ongoing smart city realizations follow this interpretation. Another more futuristic interpretation is to consider it as a large-scale context-sensitive computing infrastructure that hosts heterogeneous programs and enables the programs to interact with each other in a variety of different ways. In this chapter, we pursue such a futuristic interpretation. We are proposing a computing model for smart cities that brings together cloud computing, fogs, and mobiles to support intelligent context-sensitive computing. Our computing model has two components. The first component is a hierarchical abstract machine that spans the cloud, fogs, and devices, which can scale from a single device to many thousands of machines. The second component is an implicit learning module that observes selected data within the abstract machine to learn their characteristics. Because the implicit learning module can provide predictions based on the data in a context-sensitive manner, in certain scenarios applications can get by without explicitly incorporating machine learning into their design. In this chapter, we motivate the need for such an intelligent context-sensitive model, describe the components of the model, and present some early results.

Mobile messaging has become a trend in our daily lives, and is vital in supporting new services in smart cities. The current schema for messaging is to route all the messages between mobile users through a centralized server. This scheme, though reliable, creates very heavy load on the server. It is possible for users to communicate through peer-to-peer (P2P) connection, especially over urban networks characterized by heavy user traffic and dense network connectivity. P2P connections however do not provide the best user experience, as they are sometimes unreliable due to network coverage fluctuation. We propose an intelligent messaging framework based on reinforcement learning to strike a balance between reducing server load and improving user experience. The system learns and adapts in real-time to user mobility and messaging patterns. The adaptive system dynamically chooses between routing through the server and routing via P2P connection. As it does not rely on user location information, user privacy is thus preserved. Performance evaluation through simulation of user movement and messaging patterns demonstrates that the system is able to find the best messaging policy for users, achieves a well balance between heavy server load and unreliable communication, and provides a fine user messaging experience while reduces server load. We believe that this work is significant for future smart cities and urban networking where mobile messaging will be prominent among mobile users as well as mobile smart objects.

Interoperability of software services is one of the main challenges of smart city environments, since there is a huge number of interconnected small devices (Internet of Things) which implement and provide a wide variety of fine-grained software services. Classical approaches, such as Service Oriented Architecture (SOA) and RESTful APIs, in which both interacting services share the same data schema, usually lead to a coupling problem, since a service cannot change the schema of its messages without changing it as well in the services with which it interacts. This chapter proposes an asymmetric interoperability approach, in which the schema used to produce a message does not have to be identical to the schema of the messages expected by the receiver. This asymmetry in interoperability is based on the concepts of structural compliance and conformance, which state that schemas need only be compatible in the message components that are actually used and not in the full message schema. This reduces service coupling and allows a service to interact with others, which send or receive messages with different schemas, and to replace another one with a new schema without impairing existing interactions. Simple models of interoperability, coupling, adaptability, and changeability are proposed to justify the usefulness of the compliance and conformance concepts. A few implementation examples, using JSON, are also presented.

Video surveillance system is a crucial component in the development of Smart City. Video data can be used for a myriad of applications, enabling many key services of Smart City such as smart traffic management and enhanced public security. This chapter provides an overview of video management system for Smart City and its challenges. A small-scale testbed with assorted video managing services is used to demonstrate and compare performance of on-premise and cloud-based infrastructures. In addition, we present several camera deployment scenarios to illustrate the connectivity and data volume that would emerge in a city-scale implementation.

In the future, Electric Vehicles (EVs) are expected to be widely adopted as personal, commercial, and public fleets in modern cities. The popularity of EVs will have a significant impact on the sustainable and economic development of urban city. However, compared to traditional fossil fuel vehicles, EVs have limited range and inevitably necessitate regular recharging. Thus, the provisioning of assured service quality is necessary for realizing E-mobility solution using EVs.

The design of an efficient charging management system for EVs has become an emerging research problem in future connected vehicles applications, given their mobility uncertainties. Major technical challenges involve decision-making intelligence for the selection of Charging Stations (CSs), as well as the corresponding communication infrastructure for information dissemination between the power grid and mobile EVs. This chapter introduces a number of information enabling technologies that been applied for EV charging, viewed from a transportation planning angle.

Transportation and traffic have become a serious issue around the world. Congestion has severe negative consequences for public health, productivity and the environment, and less reliance on private cars can help solve many interconnected problems in these areas. Increased use of green transportation choices can also limit the need for expensive investment in infrastructure and minimize unpopular traffic regulations. One approach to achieve this is to introduce new kinds of services that provide members of the community with situational aware information of different forms. This includes information to travellers about which environmentally friendly transportation options that are available and match their needs on any given occasion, and how they can find and use these options. Thus, we should motivate people to make green transportation choices and provide them with situationally relevant details about when and how to do so. Nudging is a term from economics and political theory for influencing decisions and behaviour using suggestions, positive reinforcement and other non-coercive means, so as to achieve socially desirable outcomes. In our context, the people to influence are members of a community using various modes of transportation. The main components in such an approach are (i) Sense, (ii) Analyse and (iii) Inform and Nudge. With the combination of Sense, Analyse and Inform and Nudge we can provide smart nudging.

Modern “smart” buildings require a plethora of sensors to be installed at various locations during the construction phase. Wiring costs and limited flexibility of installation make wired installations less attractive. An alternative, flexible, approach is to introduce wireless sensors and endow them with ways to harvest energy from the environment such that they attain the same “zero cost” of maintenance as their wired counterparts. The chapter reviews the sensing needs of smart buildings, and the related merits of energy harvesting to power embedded wireless sensor nodes. A proof-of-concept device exploiting thermoelectric harvesting is designed, built and tested to demonstrate how todays wireless sensing devices enable sustained continuous operation with minor energy harvesting requirements. In multi-hop environments, the underlying optimization problems are described and simple strategies that forego the solution of the hard computation problems but appear effective are outlined.

Urban data streams (UDS) originate from various sensors and Internet of Things (IoT) devices deployed in smart cities as well as social media sources such as Twitter and Facebook. The large volumes of urban data need to be harnessed to help smart city stakeholders and applications make informed decisions on the fly. Furthermore, effective management and governance of smart city components relies on the ability to integrate and federate their data, process urban data streams locally, and use big data analytics. Data integration and interoperability is a challenging problem that smart cities are facing today. Successful data integration is crucial for improved services and governance. This chapter describes a framework that aims to serve in building a data pipeline for the acquisition and processing of urban data streams, urban data analytics, and creation of value-added services. The framework relies on latest technologies for data processing including IoT, edge computing, data integration techniques, cloud computing, and data analytics. The proposed platform will facilitate real-time event detection, notification of alerts, mining the opinions of citizens regarding the governance of their city, and building monitoring dashboards. A prototype of the platform is being implemented using the Kafka messaging platform.