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Abstract:

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

1. Introduction

The Internet of Things (IoT) is envisioned as a network of billions of devices that can sense, communicate and share data which can then be analyzed to unlock a wealth of intelligence useful for planning, management and decision making [1]. IoT promises huge benefits for a variety of domains such as healthcare, manufacturing, agriculture, telecommunication and transportation. Despite the popularity of the IoT concept and its promised benefits, its adoption has been significantly slower than expected [2,3]. Some of the major reasons behind this include: (1) security, privacy, policy and trust issues [3–5]; (2) organisational inertia, long capital cycles and shortage of specialist workforce needed to successfully implement IoT [2,5]; and (3) lack of convincing use cases with clear return on investment (ROI) in some sectors.

COVID-19 has impacted all walks of life so much that we may never return to the old normal. This pandemic is proving to be a catalyst for digital transformation because COVID19 has created or expanded applications and use cases of digital technologies [6–8]. It has also forced governments, organisations and individuals to change/adapt their priorities, their views on societal/ethical issues, and the way they operate. In many cases, this has addressed or mitigated many of the above mentioned reasons behind the slower-thanexpected adoption of IoT across many verticals. For example, governments have invested huge amounts of resources in IoT and other technologies to combat COVID-19. Lifestyle changes brought about by COVID-19 such as work/study from home have also provided new use cases of IoT with clear ROI such as remote asset control, workforce tracking and remote employee collaboration [9]. Consequently, many organisations have increased investment in IoT and the pace of their IoT projects [10,11]. The fight against COVID-19 has resulted in a less strict stance on privacy issues, higher trust in technology and fasttracked approval procedures. This is also paving the way for accelerated adoption of IoT in various verticals. Furthermore, regulatory changes such as stricter cleaning and tracking requirements for businesses are also accelerating the IoT adoption in smart buildings.

1- Introduction

2- Methods

3- Analysis of the evolution of ‘urban-centric resilience’: results

4- The advancement of ‘urban resilience’: discussion

5- Conclusion

References

Abstract

Over the last decades ‘resilience’ has particularly arisen as an attractive perspective with respect to cities. As cities continue to expand, their susceptibility to uncertainties and new challenges, such as climate change, has increased, rendering ‘urban resilience’ an increasingly favoured concept in the realm of Urban Development, Planning and Management (UDPM). Despite recent reviews, an updated analysis of the concept is required to understand whether there is in fact scientific evidence to support the expansion and favouring of ‘urban resilience’ in UDPM. The need to understand how the concept evolved is further emphasised by the need to perceive how the distinct sciences have contributed to its development, and which were the focuses and conceptual underpinnings of such evolution. Thus, the objective of this paper is to provide a broader review of the multidimensional concept of ‘urban resilience’, while understanding how distinct research fields have contributed to its inception and expansion, and how distinct conceptualisations of resilience have influenced its evolution. Supported by a bibliometric analysis of urban-centric publications, this paper highlights the recent extensive growth and expanding application of ‘urban resilience’ to distinct research fields, as well as an apparent theoretical stabilisation of the concept, which reemphasises the idea of a three-dimensional conceptual resilience perspective in scientific literature: (1) ‘engineering’, (2) ‘ecological’, and (3) ‘social-ecological resilience’. Consequently, this research emphasises that, if the related conceptual underpinnings are clear, ‘urban resilience’ can potentially serve as an ‘integrative metaphor’, adapted by diverse stakeholders, to reinforce UDPM initiatives.

Introduction

Decades of theoretical research in the empiric and formal sciences have contributed to a better understanding of the dynamics of singleequilibrium, multiple-equilibria, and non-equilibrium behaviours. This knowledge has subsidised the establishment of ‘resilience theory’ (Holling et al., 2001; Redman and Kinzig, 2003; Curtin and Parker, 2014) as a formal approach to understanding how systems respond to, persist under, and adapt to disturbances. Although early literature was conceptual and focused on developing a baseline for ‘resilience theory’ (Bhamra et al., 2011, p. 5380), over time a broad range of practical studies were developed (Redman and Kinzig, 2003; Shaw, 2012; Béné et al., 2014). Resilience-focused research grew from its original formulation in Engineering, to its application in Ecology (Mcaslan, 2010; Bhamra et al., 2011; Martin-Breen and Anderies, 2011), and to its applied development in urban-centric research (Cartalis, 2014; Hassler and Kohler, 2014a). The seminal work of Holling and colleagues (Holling and Goldberg, 1971; Holling, 1973, 1986; 1996, 2001; Folke et al., 2002) formed the foundation for the development of resilience studies and reinterpretations (Mcaslan, 2010; Bhamra et al., 2011; Martin-Breen and Anderies, 2011). Their work, along with continuous processes of social, economic and ecological change (Vale, 2014, p. 192), and increasingly unforeseen disturbances (Walker and Salt, 2006; Hodson and Marvin, 2009; Balaban, 2012), have highlighted the value of resilience research (Redman and Kinzig, 2003; Shaw, 2012; Hassler and Kohler, 2014a).

Introduction

Increasing individual use of connected smart devices, rapid growth of worldwide urban population, gradual aging of society in many countries as well as the rising demand for sustainable energy resources have given momentum to the emergence of smart cities and smart spaces [1]. Smart city services span a wide spectrum of applications ranging from smart utilities, to smart health, smart transportation, smart governance, and smart environment [2], which Utilize real-time sens- ing, knowledge engineering, and presentation of the analyzed data in an interpretable format. To fulfill the requirements of diverse applications and services, a smart city architecture consists of five planes as illustrated in Fig. 1 in a minimalist manner. The application plane provides services to the end users for any relevant application such as smart utilities, energy, transportation, health, environment and safety. Serving mobile, home, and corporate sectors, these applications rely on an underlying substrate, which encompasses sensing, communication, data, and security planes as the core of a smart city architecture [3]. The data acquisition (sensing) networks—implemented by utilizing either hard sensors or soft sensors—form the sensing and actuation plane; whereas processing, analysis, and storage of data shape the main functionality of the data plane. These two planes are bridged by the communication and aggregation plane. Addressing users’ growing cognizance about smart city cybersecurity, each component shown in Fig. 1 calls for security and privacy assurance mechanisms. The challenging task of satisfying these requirements is relegated to the security and privacy plane. As shown in Fig. 1, a comprehensive and effective security and privacy plane must be implemented adjacent to every individual building block of this architecture. The Internet of Things (IoT) is a major enabler for diverse smart applications that involve massive data acquisition and intelligent decision making [4]. Therefore, in the realization of smart cities, the IoT is a bridging component between the sensing devices and the data plane [5, 6]. Indeed, in order for the IoT concept to operate thoroughly, its interface with the local wireless networks, backhaul networks, as well as local wired networks needs to be addressed properly. Furthermore, protocols for reliable and efficient data acquisition methodologies for fusing data are of paramount importance particularly to ensure a robust communication back-end in a smart city infrastructure. In a smart city architecture, communication back-end is one of the most crucial components, which is responsible for pre-processing and aggregation of the sensory data. Reliability, usefulness and trustworthiness of the data acquired by the communication back-end depends on the effectiveness of the sensing plane. As studied in [2], sensors in a smart city setting can be deployed in either a dedicated or non-dedicated manner; each deployment strategy has its own pros and cons. Based on the observations above, in this paper, we present a smart city architecture by briefly introducing its building blocks (termed planes), namely the application, communication, sensing, data, and security planes. Upon brief introduction of the architectural building blocks, we move to our main foci, which are the sensing, communication and security planes. In the study of the sensing plane, we present the dedicated and nondedicated sensing paradigms from the standpoint of various smart city applications. We thoroughly investigate the sensor types, problems experienced by the corresponding smart city applications, existing solutions and the communication technologies used by the sensors. Sensing plane is followed by the communication plane, which is studied in terms of the requirements, implications, and common solutions. We partition the communication plane into several sub-planes to study the communication infrastructure and protocol design for a smart city with fine granularity.

Introduction

Nowadays, smart city services are becoming more widespread than ever as cities are growing and becoming increasingly crowded as a result of urbanization and world population growth[1]. The term “smart city” in [2] refers to the use of information and communication technologies to sense, analyze and integrate 5 key information from core systems in operating cities. At the same time, smart city services can make intelligent responses to different kinds of needs in terms of daily livelihood, environmental protection, and public safety, as well as the city’s facilities and industrial and commercial activities. As smart city — related technologies develop rapidly (for example, the spread of IoT devices), more things become measurable. As a result, we have more and more usable data about the ecosystem of our cities. 10 Among the various notable goals of smart cities, construction of smart transportation systems and smart urban management systems are two of the key aims, which could significantly influence the lives of residents in future cities. Advanced Traffic Management Systems (ATMSs) and Intelligent Transportation Systems (ITSs) integrate information, communication, and other technologies and apply them in the field of transportation to build an integrated system of people, roads, and vehicles. These systems constitute 15 a large, fully-functioning, real-time, accurate, and efficient transportation management framework [3]. In ATMSs and ITSs, it is a fundamental challenge to predict the next possible states of traffic with high precision, because this information helps to prevent unfortunate events like traffic jams or other anomalies on roads. The literature often refers to traffic as a flow, because it has similar properties to fluids. Thus when we speak about traffic flow prediction, we wish to predict the next state (it can be the 20 volume, speed, density, or behavior) of the traffic flow based on historic and real-time data. The rapid progress of urbanization has modernized many people’s lives, but also brought remarkable challenges like traffic congestion [4] that can lead to increased energy/fuel consumption [5] and enormous emission of pollutants [6]. These phenomena have a great impact on the health and quality of life of citydwellers. According to [7], laboratory studies indicate that transport-related air pollution may increase 25 the risk of developing allergies and can exacerbate symptoms, particularly in susceptible subgroups; cohereas [8] showed that traffic jams increase the risk of heart attack. Intelligent management systems (such as ATMS and ITS) can help overcome or significantly reduce the impact of such negative effects on city-dwellers. Forecasts can also support traffic control centers in managing the road network and allocating resources systematically, such as opening/closing lanes, 30 dynamic parking pricing [9], or adaptive traffic lights [10] with a high level of automation [11]. When driving, knowledge of traffic forecasts for different routes is advantageous [12], and devices will be able to calculate more efficient routes and reduce travel time. Insight into vehicular flows within the smart city could make searching for parking spaces [13] much easier and faster. Moreover, it could be a useful source of information for emerging V2X-based traffic control systems [14], which could play an 35 important role in the route planning of self-driving cars.

Abstract

By leveraging advancements in information and communications technology (ICT), Smart Cities offer many potential benefits like improved energy efficiency, management and personal security. However, this dependence on ICT also makes smart cities prone to cyber attacks. In this paper, we investigate the topic of cybersecurity for smart cities. We show how the specific characteristics of smart cities give rise to cybersecurity challenges, and review the different threats faced. Finally, we review some of the more important cybersecurity solutions for smart cities that have been proposed.

Introduction

Rapid advances in ICT have been exploited to streamline the design, operation and management of urban environments in a variety of ways. For example, it is now possible to monitor and manage energy consumption patterns in real time with smart meters, use this information to coordinate generation and distribution resources via the smart grid, continuously track traffic congestion and road hazards and communicate this automatically to vehicles and commuters. Progress in these areas have helped to cut costs, increase efficiency, bring about greater safety and convenience, and reduce pollution and greenhouse gas emissions.