Smart cities and internet of things

ABSTRACT

The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in Information and Communication Technologies to find sustainable solutions to the diverse opportunities and challenges (e.g., waste management). Several researchers have attempted to define and characterize smart cities and then identify opportunities and challenges in building smart cities. This short article also articulates the ongoing movement of Internet of Things and its relationship to smart cities.

Introduction

The “Smart City” concept has become extremely popular in scientific literature and international policies. This concept essentially harnesses a plethora of IT innovations hitting us at breathtaking speed to make cities smarter for the citizens. Cities and urban areas comprise about half of the total world’s population (Bakıcı, Almirall, & Wareham, 2013). The urban population inflation for the last few decades has been adversely affecting quantity and quality of services provided to the citizens. Smart cities aim at providing effective solutions. Various Smart City (SC) initiatives by both government and private sector organizations have resulted in deployment of Information and Communication Technologies (ICT) to find sustainable efficient and effective solutions to the growing list of challenges facing cities (Caragliu, Del Bo, & Nijkamp, 2011; Su, Jie, & Hongbo, 2011). Education, health, traffic, energy, waste, unemployment and crime management are some of these challenges (Chourabi et al., 2012).

This article is organized as follows: Emergence of Smart City concept; Characteristics and Components of a Smart City; Smart City Architecture; Challenges in Construction of a Smart City; Los Angeles as an Example of a Smart City; and finally Internet Of Things and Futuristic Scenario of a Smart City.

The emergence of Smart City concept

The Concept of, “Smart City” first appeared in the 1990s. At that point in time, the focus was on the impact of new Information and Communication Technologies on modern infrastructures within cities. The California Institute for Smart Communities focused on how a city could be planned to implement information technologies and how communities could become smart (Alawadhi et al., 2012). Some years later, the Center of Governance at the University of Ottawa started criticizing the idea of smart cities as being too technically oriented. A few years ago, researchers started asking real smart cities to stand up and illustrate the aspects that are hidden behind the term “smart city” (Hollands, 2008).

The label “smart city” is used interchangeably with similar terms like “intelligent city” or “digital city” (Albino, Berardi, & Dangelico, 2015). These variants are categorized into three dimensions (Harrison et al., 2010; Nam & Pardo, 2011): Technology, People and Community as presented in Table 1. These dimensions give rise to various definitions (Table 2) which share some commonalities. Offered definitions appear to have dual interpretations: (a) smart city is a living solution that integrates different life facilities such as transportation, power, and buildings in an efficient manner to improve the services for its citizens; (b) smart city exemplifies the importance of sustainability of resources and applications for future generations.

Table 1. Conceptual relatives of smart cities.

Dimension	Concept asymptotic
Technology	Digital city Intelligent city Ubiquitous city Hybrid city Wired city Information city
People	Creative city Learning city Humane city Knowledge city
Community	Smart community

Table 2. SC definitions along with their key element.

Reference	Definition	Key element/focus area
Hall, 2000	A city that monitors and integrates conditions of all of its critical infrastructures, including roads, bridges, tunnels, rails, subways, airports, seaports, communications, water, power, even major buildings, can better optimize its resources, plan its preventive maintenance activities, and monitor security aspects while maximizing services to its citizens.	Improving connectivity and information flow among core city infrastructures
Harrison et al., 2010	A city connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city.
Nam & Pardo, 2011	A smart city infuses information into its physical infrastructure to improve conveniences, facilitate mobility, add efficiencies, conserve energy, improve the quality of air and water, identify problems and fix them quickly, recover rapidly from disasters, collect data to make better decisions, deploy resources effectively, and share data to enable collaboration across entities and domains.
Washburn et al., 2009	The use of Smart Computing technologies to make the critical infrastructure components and services of a city – which include city administration, education, healthcare, public safety, real estate, transportation, and utilities – more intelligent, interconnected, and efficient	Utilizing ICT evolution to make the critical infrastructures more intelligent and interconnected.
Marsal-Llacuna, Colomer-Llinàs, & Joaquim, 2015	Smart cities initiatives try to improve urban performance by using data, information and information technologies (IT) to provide more efficient services to citizens, to monitor and optimize existing infrastructure, to increase collaboration among different economic actors, and to encourage innovative business models in both the private and public sectors.

Characteristics and components of a Smart City

Dense environment, like that of cities and capitals, requires its subsystems to work as one system with intelligence being infused into each subsystem. Researchers who support this integrated view stress the importance of the organic integration of a city’s various subsystems (transportation, energy, education, healthcare, buildings, physical infrastructure, and public safety) into one unified system to create a smart city (Gurdgiev & Keeling, 2010; Kanter & Litow, 2009). According to (Giffinger et al., 2007; Perera, Zaslavsky, Christen, & Georgakopoulos, 2014), the authors, in their attempts to delineate the features of a smart city, indicated that the smart city has six possible characteristics: smart economy, smart people, smart governance, smart mobility, smart environment, and smart living as presented in Figure 1.

Figure 1. Characteristics of a Smart City.

Lombardi et al. (Lombardi et al., 2012) associated the above six characteristics with different aspects of urban life, as shown in Table 3.

Table 3. SC components and related aspects.

Smart City Characteristic	Related aspect of urban life
Smart economy	Industry
Smart people	Education
Smart governance	E-democracy
Smart mobility	Logistics and infrastructures
Smart environment	Efficiency and sustainability
Smart living	Security and quality

A framework by Nam and Pardo (Nam & Pardo, 2011), states that there are three factors (components) of a Smart City: technology, people and institutions. Given the connection between the factors, the city will be smart when investments in human social capital and IT infrastructure fuel sustainable growth and enhance the quality of life through participatory governance (institutional factor). Figure 2 shows the sub-components of and connections between these three factors.

Figure 2. Fundamental components of an SC.

ICT infrastructure and smart and mobile technologies are prerequisites for a city to be smart but without real engagement to the other factors there is no smart city.

Human Category highlights creativity, social learning, and education. Lombardi (Lombardi et al., 2012) and Nam and Pardo (Nam & Pardo, 2011) surmise sub-components of this factor as affinity to lifelong learning, social and ethnic plurality, flexibility, creativity, cosmopolitanism or open-mindedness, and most importantly participation in public life.

Governance factor is a basic element in institutional factor. Smarter government besides the prescribed policies will interact dynamically with citizens, communities, and businesses in real time to spark growth, innovation, and progress.

Our review recommends Giffinger && Rudolf model as each component include not a few number of indicators/factors, categorizing these indicators into only three categories as Nam &&Pardo led us to complex measures and assessments. Various components and a number of their major indicators as mentioned in (Albino et al., 2015) are listed in Table 4.

Table 4. SC component and its indicators.

SC Component	Some Indicators
Smart economy	Public expenditure on education, joblessness rate, GDP per head of city population, public expenditure on R&D.
Smart people	Foreign language skills, personal computer skills, secondary – level education ratio, patent applications per inhabitant, participation in life-long learning and others.
Smart governance	Number of universities and research centers, percentage of households with Internet access, e-government on-line availability and e-government use by individuals.
Smart environment	Optimal use of electricity, CO2 emission reduction strategy, green spaces, efficient water utilization, policies to contain urban sprawl, proportion of recycled waste and others.
Smart living	Museum visits, percentage of the area for sports space and leisure activities, theater and cinema attendance, number of public libraries, total book loans and other media and others.

Smart city architecture

A few researchers have developed a vision for the construction of a smart city. Kehua, Li and Fu (Su et al., 2011) visualize the smart city construction in three layers,; the first layer being the “perception layer”, in which different data are collected from various data sources such as cameras, GPS, Sensor network; second layer, “Network Layer” is responsible for transmitting data, collected from layer 1 to data storage center, and layer 3 is dubbed, ”Application Layer” containing applications for analyzing and processing the massive data residing in the data storage center as shown in Figure 3.

Figure 3. Architecture diagram of Smart City.

Another point of view by Balakrishna (Balakrishna, 2012) shows that a smart city mainly consists of three building blocks shown in Figure 4. The most basic one is the large-scale instrumentation of the city’s infrastructure which includes utility, transport, environmental and government infrastructures with sensors, actuators, readers and other sensing devices. Logically, a high-speed network infrastructure is needed to be coupled with the underlying sensor networks to support the expected growth in the number of connected devices and facilitate mobility, connection, and information sharing and this is what the second layer does. The last critical requirement of the construction is the efficient management of the massive aggregated data collected from the underlying sensor fabric which thereby facilitate the development of smart applications and services that are created on the top of the three building blocks.

Figure 4. Building blocks of Smart City architecture.

Internet of things and challenges in construction of a Smart City

Evolution in ICT and information sharing technology are the drivers of smart city scope and scale. This rapid evolution is revolutionizing smart city construction with the dawn of Internet of Things (IoT). This revolution also represents challenges in constructing Kehua, Li, and Fu (Su et al., 2011).

Management, integration and release of massive spatial–temporal urban data

At present, existing information outputs of digital urban information systems are still too static and simple and often appear in the form of answers to simple queries and are devoid of analysis of data from multiple sources across time to assist the process of decision-making in urban management. Modeling temporal data in current urban information systems is still weak. The data structure and organization of temporal data from multiple sources cannot meet the special needs of digital real-time updates, historical reconstruction, and future prediction. Therefore, one of the keys to building a smart city for the current times is the integration of multi-source heterogeneous urban data. Additionally, there is a need for management of urban infrastructure and components, and capability for quick update and visualization of multi-dimensional spatial and temporal data.

Model of heterogeneous sensor data and emergence of internet of things

Due to the advances in sensor and cloud technology, processing and storage capability, and decreased sensor production cost, the growth of sensor deployments has increased over the last few years. Unlike smart city, IoT has originated basically from the advances in technology and not as a result of user or application needs. One of the most popular and widely used definitions of IoT is that it allows people and things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service. Despite their differentiation, both the IoT and SC are moving towards each other to achieve a common goal as summarized in Figure 5. It should be noted that the process of building models describing sensor information in terms of location attributes, observed objects, time and status is complex due to the variety of sensor platforms, observation mechanisms, sensor processes, location information, and technical requirements.

Figure 5. Relationship between IOT and SC.

Large-scale space-time information management

Spatial information of a smart city is generated from different types of sensors, controllers and computing terminals which are all maintained by computers and storage devices equipped in various departments and locations. Managing and coordinating these devices with different structures and wide-area distribution is not a trivial issue. Smart cities generate not only structured data such as temperature values, geographical coordinates but also a lot of unstructured data such as pictures, audios, and videos. Storing and managing this vast amount of diverse data in several formats is a monumental task. Smart cities are responsible for thorough analysis of urban information, public affairs, decision support, real-time tasks and responding to users’ requests on time.

Sound information sharing mechanisms and legal protection

One of the definitions of smart city is “the use of Smart Computing technologies to make the critical infrastructure components and services of a city more intelligent, interconnected, and efficient.” by Washburn (Washburn et al., 2009). So we need to overcome all possible bottlenecks and challenges to achieve efficient and effective information sharing between several city agencies: traffic, public security, media, utility, weather, etc. Besides, there is a need to learn from developed countries on how to establish coalition mechanisms of sharing spatial information. It is also important to achieve sound information services and share policy mechanisms and legal protection among all city departments.

Los Angeles as an example of a Smart City

Many countries around the world face the challenges of enhancing their citizen’s quality of life in the context of rapid population growth in large cities. Many of them have launched smart city initiatives to provide quality services to its citizens. These initiatives include renewal of already existing urban infrastructure of energy provision, public parks, traffic management, mass transport services, healthcare services, educational facilities at all levels. There is no “universal solution “to ensure the success of a city on its way towards smartness. To illustrate changes that have been undertaken in cities across the world, the case of Los Angeles (Pick, 2017) is described below in summary form.

Los Angeles has a technologically proactive city government. Since 2013, Los Angeles has progressed from a moderate digital status to a leading digital city. The new mayor Eric Garcetti issued Executive Directive 3 on Open Data (Garcetti, 2013), which mandated that the city supply raw data to the public in easily accessible formats, leverage public information as a civic asset, promote innovation from entrepreneurs and businesses, and that each city department be required to implement open data. Accordingly, many systems have been developed to raise the efficiency of services provided to the public including but not limited to the following:-

• My311 Services. Web-based system to inform Los Angeles citizens (Angelenos) about city services and enable them to make online service requests, e.g., graffiti and large waste removal.

• GeoHub. Advanced spatial analytics system that allows employees and citizens to understand multiple dimensions of the city’s services through its mapping interface.

• City-Link LA. Inventory of the city’s telecommunication assets to encourage the private sector to deploy advanced wireline and Wi-Fi digital communications networks for better planning and efficiencies.

• Mayor’s Dashboard. Sustainability plan and performance dashboard totally progress and keep tabs. With green panes to indicate achieved goals and red panes to convey that a goal not yet achieved. There are panes that include seasonally adjusted unemployment rate, lane miles paved in the fiscal year to date, crime percentage change and other panes.

Futuristic scenario of a waste management in a Smart City

In this section, we articulate the use of IoT for waste management challenges in smart cities. Waste management is a vital process consists of different sub-processes such as collection, transportation, processing, disposal, managing, and monitoring of waste materials. Each one consumes a significant amount of time, money and labor. Modern smart cities should work on optimizing the waste management processes to save these resources which could be utilized in addressing other challenges. Optimization could be achieved through cooperation among the different parties who are interested in waste management such as city council, recycling companies, manufacturing plants, and authorities related to health and safety. Instead of deploying sensors and collecting information independently, the interested parties can have a collaborative sensing infrastructure and bear related costs collectively as suggested in (Perera et al., 2014). Each party can retrieve sensor data in real time to achieve its own objective. For example, the manufacturing plants can use sensor data to determine the amount of incoming waste so as to optimize their internal processes. Additionally, a city council may use the collected data to efficiently optimize the garbage collection strategies.

Figure 6 portrays waste management by utilizing IoT. Different types of sensors, as part of the IOT infrastructure, need to be deployed at different locations for things like garbage cans and truck. The main functions of these sensors is to sense necessary data such as amount of garbage, types of garbage, and upload it to the cloud either directly as case (1) or indirectly through the nearby infrastructure such as the communication devices attached to street lights as case (2) or through garbage trucks as case (3).

Figure 6. Efficient waste management with a shared infrastructure (Perera et al., 2014).

Such a network of sensors and data collection would help in effectively conducting a variety of waste management processes such as determination of the time at which the collection should be carried-out, type of truck that should be sent at different collections areas, and appropriate locations where the garbage cans must be placed to guarantee environmental safety.

Conclusions and implications for researchers and practitioners

In this short article, we presented a short review of various initiatives in the domain of smart cities. As a result of the migration from rural areas to urban centers throughout the world, education, health, traffic, energy, waste, unemployment and crime management are some of the critical challenges facing cities. We have articulated the critical role of ICT in transforming traditional cities into smart cities. We have also constructed a connection between Internet of Things and functions of Smart Cities. We took this connection one step further by presenting an IOT-based futuristic scenario for optimizing the waste management processes in smart cities.

There is an unprecedented increase in the amount of data collected in data warehouses through interaction by citizens in a Smart City. Extracting meaning and knowledge from these data is crucial for governments and businesses to support their strategic and tactical decision-making. Furthermore, artificial intelligence (AI) and machine learning (ML) makes it possible for computers to process large amounts of such data, to learn and execute tasks never before accomplished.

Advances in big data-related technologies are increasing rapidly. For example, virtual assistants, smart cars, and smart home devices in the emerging Internet of Things world can, we think, make our lives easier. But despite the perceived benefits of these technologies/methodologies, there are many challenges ahead in the context of smart cities and IoT.

With the increasing potential of machines that learn, old jobs composed of simple tasks are at risk. New jobs will be created with new skills but numbers are most likely not equal (Cuquet, Vega-Gorgojo, Lammerant, & Finn, 2017). This should be a concern for politicians and governments as unemployment can increase. Also in smart cities, information on individuals is open to analysis and sharing which gives rise to concerns about profiling, stealing and loss of control (Hashem & Targio et al., 2016). To address these concerns researchers in (Bello-Orgaz et al., 2016; Chang, Ramachandran, Yao, Kuo, & Li, 2016) have identified different privacy issues that require further research in the future such as data communication, graph matching, awareness, and evaluation of privacy-preserving services.

In addition to other confidence-related challenges, trust in computing methods for big data may be harder to establish if their rationale cannot be easily explained. Different malicious decisions may result if the data are incorrect, missing, use the wrong format, and/or are incomplete (Gouveia, Seixas, & Giannakidis, 2016).

Acknowledgments

Editor-in-Chief Dr. Shailendra Palvia was gracious to help improve language and grammar and organization of this article so that it helps the readers in quick comprehension of the contents and lessons for implementation in various scenarios of research and practice in private or public enterprises. My heartfelt thanks to him.

Additional information

Notes on contributors

H. Samih

H. Samih received his B.SC in Information Technology from Assiut University, Assiut Egypt in 2011. He works as a Teaching Assistant for Egyptian E-Learning University, Cairo Egypt since 2014. He is currently pursuing Master’s degree at Ain Shams University, Cairo Egypt. His research interests include Smart Cities and its related technologies as well as Semantic Web technologies and its applications in the field of image retrieval and annotation.