IoT-Enabled Smart Cities: Evolution and Outlook
- It gives an overview of the current landscape of IoT-enabled smart cities based on a selection of relevant smart cities initiatives around the world. The number of cities analysed and the depth of the analysis has been intentionally non-exhaustive as the scope of this review is mainly to showcase the different visions and approaches that have been adopted for the development of IoT-enabled Smart Cities around the globe. In this sense, it is important to mention that among the diverse conceptions of smart cities, the paper focuses on those that rely on IoT as a key enabler for realising the smart city paradigm.
- It describes and analyses the key IoT technologies that have been developed and how they contribute to the realization of a Smart City. The focus of this analysis is on presenting, in a generic and high-level manner, the pool of technologies and services that, apart from the various specific instantiations that have been rolled-out in the existing IoT-enabled smart cities, sets the basic building blocks that are present in today’s IoT-enabled smart cities.
- It identifies a number of key challenges that are currently being addressed and that are essential for the success of smart cities. This is the key contribution of the paper as it delves into the main issues that have to be addressed in order to overcome the fragmented IoT-enabled smart cities landscape, which can be inferred from the review of cities and technologies made in the paper, and to boost the adoption of a more homogeneous smart city materialization, which eases the roll-out of global smart city solutions.
2. IoT-Enabled Smart Cities Landscape
2.1. Cities Selection and Analysis Considerations
- Environmental Monitoring: IoT devices installed provide measurements of different environmental parameters, such as temperature, CO, noise, or air pollutants. Both static and mobile devices installed on vehicles such as buses and taxis retrieve environmental parameters.
- Outdoor Parking Management and Guidance: Parking sensors buried under the asphalt are installed in order to detect parking site availability in downtown outdoor parking zones. Several panels have been installed at the main streets’ intersections in order to guide drivers towards the available free parking lots.
- Traffic Intensity Monitoring: Devices deployed at the main entrances to the city of Santander measure the main traffic condition parameters, such as traffic volumes, road occupancy, and vehicle speed or queue length.
- Parks and gardens irrigation: In order to make irrigation as efficient as possible, multiple sensors have been deployed in two green zones of the city to monitor irrigation-related parameters and serve this information to gardens’ managers.
- Waste management: Paper and cardboard and plastics waste containers in the city are equipped with sensors which are helping to optimize the organization of collection trucks.
- Participatory sensing: Users can participate by reporting events or incidences occurring in the city, which will subsequently be propagated to other users who are subscribed to the respective type of events.
- Smart street light: A service that contributes to energy saving, enhancing street aesthetics, strengthening public safety and crime prevention functions, and building ‘smart lighting’ with energy-saving LED lighting, CCTV, and wireless Internet relay functions.
- Smart crosswalk: A service that reduces the number of traffic accidents, and loss rates with a pedestrian detection and car stop detection system to prevent traffic accidents near the crosswalks, thereby reducing social and economic losses caused by traffic accidents.
- Smart parking: A service that contributes to the reduction of traffic congestion in the city and the environment by improving the efficiency and convenience of parking so that drivers can check and use empty parking spaces in Busan in real time through mobile app and web service.
- Building energy management: A service that establishes an efficient energy saving plan and energy management system through monitoring and analysis of energy consumption by installing a smart meter and sensor in the Busan City Hall.
- Socially disadvantaged individuals: A service that delivers the location of the weak people to the guardians and operators based on an IoT specialized network for safety.
- Lost child prevention: A service that informs guardians of the location of children in Busan based on an IoT specialized network.
- Smart store management system: A service that enables effective store management by monitoring the usage status of various electronic devices used in the store, store environment (temperature, humidity, illumination, fire detection), and visitor trends in real time.
- Smart Urban Mobility: Uses digital technologies to enhance comfort, convenience and reliability of public transport systems, and support a vision of a car-lite Singapore.
- Smart Nation Sensor Platform: This is one of the anchor initiatives in Singapore that enables everyone and everything, everywhere, to be connected all the time in Singapore.
- National Digital Identity: A digital identity system for Singapore residents and businesses to transact digitally with the Government and private sector in a convenient and secure manner.
- Moments of Life: This is an initiative that aims at providing personalized and pro-active support to citizens at key junctures of their lives.
- E-Payments: With this vision, citizens and businesses in Singapore are able to transact digitally in a hassle-free, seamless and secure manner.
- Core Operations Development and eXchange (CODEX): This is a digital platform that enables the Government to deliver better digital services to citizens faster and more cost efficiently.
- Water quality management: An IoT platform called Smart Sponge collects and manages water quality-related data on a real-time basis to provide city-wide water management services (water volume, quality information, and location information of water pipe networks).
- Smart industrial complexes: This service connects a series of smart factories to share various technologies and provide effective smart factory services. To achieve this, the city established ultra-high-speed broadband Internet, next-generation wireless network, and free Wi-Fi infrastructure, in addition to connecting and managing various IoT devices in industrial complexes via a common cloud platform.
- Public safety: The smart public safety service in Shenzhen deals with public safety, production safety, food and pharmaceutical safety, and geological risks in the city. This is achieved through a surveillance camera infrastructure and a network that covers the entire city. This IoT public safety platform was designed to be used all-year-round, and it is used to provide a variety of services including social safety, anti-terrorism and crime prevention efforts.
- IoT: Video cameras with network features, smart street lights, smart traffic lights, smart water quality management, utility management, environmental pollution management, smart meter, smart factory, etc.
- Big data and AI: Social credit platform, intelligent transportation system, fire incident analysis system, smart surveillance camera, intelligent parking service, and smart maintenance service.
- Cloud computing: Cloud-based IoT platform and city data management centre
- North Avenue Smart Corridor: Designed to act as a living lab for mobility- and safety- related IoT and data analytics, as well as connected and autonomous vehicles. The 2.3-mile North Avenue Smart Corridor, which is a key east-west arterial connection in Atlanta, incorporates many smart city technologies, including adaptive traffic signals which adjust to traffic conditions in real-time and can prioritise emergency vehicles. The City of Atlanta will take key learnings from this Corridor but is now looking to take a much broader approach to mobility.
- ShotSpotter: This service can pinpoint the exact location where a gunshot has been fired. Alerts will notify police of possible gunfire within 30–45 s, and give the precise location, normally within 20 feet. The necessary infrastructure was deployed at 200 streetlights in five different locations across Atlanta.
- Smart Neighbourhood: The project, run in association of an energy utility and a home construction company, planned the creation of more than forty technology-enhanced houses aiming at drastically reducing their Home Energy Rating System (HERS) score. The data-fuelled home energy optimisation platform intelligently scheduled each home’s major appliances, in coordination with solar and batteries, to minimise cost while maximising each homeowner’s comfort at a neighbourhood level.
- ATL311 and NotifyATL app: These two apps enabled more fluent interaction with citizens which could report non-critical problems such as potholes, graffiti or waste issues and track the status of service requests, as well as provide citizens with critical information about events such as severe weather, unexpected road closures, missing persons and evacuations of buildings or neighbourhoods as well as community events and crime alerts.
- Open Data: Availability of open data from about 30 city departments, including topographical and address data, land value and ownership information, healthcare data, traffic data and more. In most cases the open data is provided as Excel sheets accessible from a web site, i.e., easily accessible to humans, but not directly linkable and automatically accessible to machines.
- MyNeighbour App (MijnBuur): provides a direct connection between neighbours, e.g., for the purpose of alerting about dangers and identifying something that needs to be done. The idea is to strengthen social responsibility and direct interactions between citizens without having to involve the municipality.
- Wyzer App: implements the idea of keeping people off the main track, thus reducing congestion and improving their city experience at the same time. With fuzzy navigation, they are directed in the right direction, but not on the main route. The application highlights “hidden gems” on the way.
- Plastic free rivers: is a pilot for stopping plastic floating on rivers. A tube with holes is placed at the bottom of a river and air is pumped through it. This creates a “bubble barrier” that stops floating plastic and guides it to the river banks for collection.
- Social Glass: does big data analysis on social media and, on this basis, determines the emotions of the public. This information is cross-referenced with geolocation and other data, which enables establishing patterns and mapping the mood of the city.
- IoT Living Lab: An IoT Living Lab has been set up, covering a stretch of more than 3 km with iBeacons using LoRaWan technology. Users can use it to send small data packets to the cloud, which can then be accessed and used for smart city apps.
2.8. Sunshine Coast Region
- Sunshine Coast Council (SCC) App: provides information about facilities, events, guided tours, but also serves as a disaster hub with emergency contacts.
- Smart Street: with free public WiFi collecting data to understand how public spaces are used. Overall, more than 200 access points have been deployed.
- Pedestrian and cyclist counters: monitoring how many people use public areas like parks and walking trails, allowing better planning, maintenance and cleaning.
- Networked LED street lighting: is turned on and off automatically during twilight periods to reduce energy consumption and thus electricity costs and CO2 emissions.
- Waste bin sensors: to measure waste levels and send alerts if the bin is full or still empty to help deliver more cost-effective waste management services.
- Networked irrigation system: monitors the soil and sends alerts when watering is required. Additionally, flow meters identify problems and leaks to reduce the waste of water.
- Networked flood sensors: installed on roads and bridges, which are susceptible to flooding.
2.9. Rio de Janeiro
- Emergency monitoring and response. The video feeds from the surveillance cameras installed in the city are the core of the Intelligent Operations Centre (IOC) focusing on controlling traffic and weather to ensure smooth functioning of day-to-day operations.
- Local government data sharing. Departments from the local government hosted at the IOC premises exchange data amongst themselves using IBM’s platform to increase the efficiency of their services.
- Rio Agora. A platform which combines an online social platform with the already existing portal called Central 1746  which allows citizens to propose and debate public policy with municipal departments and agencies in different themes as well as to request a number of services from the city’s government such as waste removal, repair of damaged roads and walkways or reporting of illegal activity, the requests posted on the website are integrated in the IOC and its lifecycle managed through the system.
2.10. Cities Review Summary
3. Key Technologies Developed
3.1. Sensor and Actuator Technologies
- Stationary sensors: In the static deployment of sensors, all the nodes are stationary. In this case, the most important aspect is the design of the network, i.e., specifying beforehand where the sensors should be deployed for an optimal monitoring, paying special attention to providing a good coverage of the monitored area and to guaranteeing connectivity of the wireless nodes. Static WSNs are heavily used in smart city scenarios as they are well-suited for smart metering of public services networks such as water and sewage [33,34], public lighting , parking  or traffic monitoring [37,38,39], as well as for generating the baseline monitoring of the city environment [38,39].
- Mobile sensors: In the case of mobile deployments, nodes are installed in vehicles that move around to collect data. These vehicles could be specially designed robots , or more recently drones , or public vehicles that are equipped with specific sensors [42,43]. Mobile sensors are a sensible solution to enhance the performance of city monitoring in terms of coverage, cost or resiliency, to mention a few. In this sense, they are mainly used to complement the sensing of existing static WSNs. For example, while city traffic monitoring is typically addressed using road side cameras  and inductive loops  embedded in the road, many cities are using the information collected from sensors in vehicles, usually public vehicles like buses  or garbage trucks  to have more detailed and fine-grained information. Additionally, some aspects like road surface conditions cannot be correctly monitored through stationary sensors. On the downside, mobile nodes introduce new challenges and problems, mainly related to the connectivity guarantee. Additionally, for the design of the network, it is important to consider the paths that the vehicles will follow in order to consider how well the sensors they are equipped with will cover the monitored area.
- Crowd-sourced sensors: Crowd sensing basically means outsourcing the monitoring of the environment to the crowd. In cities, this mainly relates to the capacity of leveraging the abundance of digital devices that are equipped with sensors. Citizens’ smart phones are the most commonly used ones, and also the ones providing rich information. Crowd sensing is typically catalogued into two main types: participatory sensing [48,49] and opportunistic sensing [50,51]. In participatory sensing, the users are directly involved in the sensing action. For example, in  citizens reported issues like broken public infrastructures (e.g., streetlights, benches, bins, etc.) or cultural activities (e.g., street art, festivals, etc.). In opportunistic sensing, the users might be unaware of the sensing of the smart phone. For example, the solution presented in  is designed to monitor the urban traffic and road surface conditions and an estimation of air quality and PM2.5 in cities is presented in .
3.2. Networking Technologies
3.3. IoT Platforms
- oneM2M standardizes an IoT service layer that constitutes software middleware between IoT hardware and communication technologies on one side and IoT applications on the other side. oneM2M  is a partnership project, whose partners are the regional telecommunication standardization organizations in Europe, Asia and North America, and whose respective member organizations drive the standardization process. The oneM2M service layer provides common service functions for data management, security, device management, connectivity and group management. A REST-style API provides standardized mechanisms for building REST resource structures and supports standardized interaction patterns. For the abstract API, different communication protocol bindings have been defined, including HTTP, CoAP and MQTT. oneM2M is agnostic to the IoT information model used, i.e., it can store and handle any type of IoT information.
- OMA Lightweight M2M (LwM2M) is a protocol for IoT device management and service enablement. It is standardized by the Open Mobile Alliance (OMA) . LwM2M defines and application layer communication protocol between a LwM2M server and a LwM2M client located on each device. The device management functionalities are connectivity management, provisioning of security credentials, firmware updates and remote device diagnostics. The service enablement functionalities are sensor and meter readings, actuation and configuration of devices.
- NGSI-LD provides a Context Information Management API and an underlying linked data-based information model resulting in a knowledge graph. NGSI-LD is specified by the Industry Specification Group on cross-cutting Context Information Management of the European Telecommunications Standards Institute (ETSI ISG CIM) . It represents the evolution of the NGSI context interfaces originally standardized in OMA and later evolved in the FIWARE open-source ecosystem. Applications can manage, retrieve and discover the information they need, using filters and geographic scopes to limit the results to what is relevant. Using NGSI-LD, applications get information on a suitable abstraction level and independent of device technology specific data models.
- MIM 1 is called OASC Context Information Management and relates to the API that allows accessing to real-time context information. The recommended standard for this is NGSI-LD.
- MIM 2 is called OASC Data Models and refers to guidelines and a catalogue of common data models that enable interoperability for applications and systems among cities. There are several data models already proposed (e.g., SAREF  supported by ETSI, OneDM  supported by the Zigbee Alliance) but OASC seems to focus on the models by Smart Data Models initiative , which it recently joined.
- MIM3 is called OASC Ecosystem Transactions Management and refers to the Marketplace API that exposes functionality like catalogue, ordering and revenue management. Here, the TM Forum Open APIs  are proposed for building interoperable solutions.
3.4. Smart City Applications
- Smart transportation: This type of application is intended to establish a demand-responsive transportation system by installing transportation infrastructure suitable for the era of autonomous driving and providing consumer-oriented transportation services.
- Smart environment: This type of application introduces the latest smart water management technology for water resource management and water disaster prevention, and presents a water management leading model in waterfront cities.
- Smart energy: This type of application introduces new and renewable energy and establishes an energy demand management system to increase the energy independence of the city.
- Smart security: This type of application provides fast and accurate civil safety services by applying solutions using innovative technologies related to disaster and safety.
3.5. Artificial Intelligence and Big Data
- Descriptive Analytics processes historic data and provides metrics and measures to summarize the information. It is often taken as an initial step followed by further analytics. For example, it can be used to find the peak of traffic congestion.
- Diagnostic Analytics after an issue has been detected, e.g., as a result of descriptive analytics, diagnostic analytics can be applied to attempt to understand the cause, e.g., rush hour traffic in combination with construction sites.
- Predictive Analytics can be used to forecast situations and events in the future. Historic information can be used to create a function that predicts a value, e.g., traffic congestion at a certain time on a weekday under certain weather conditions, which can be applied to predict the situation at a future point in time.
- Prescriptive Analytics goes one step further than predictive analytics as it tries to understand how different factors influence a situation. This can be used to take actions to improve the situation, e.g., adapt the traffic light scheduling to improve the traffic flow and reduce traffic congestion.
- Supervised Learning tries to learn how to map a set of input values to an output value. This requires labelled training data, i.e., data for which this mapping is given. After training, the resulting model is able to predict the output for new input data. For example, based on the movement characteristics of a user as extracted from a smart phone, the means of transport can be determined. Typical supervised learning techniques are Support Vector Machine (SVM), K-nearest Neighbour (KNN), Random Forest, Linear Regression (LR) and Decision Trees (DT).
- Unsupervised Learning tries to find clusters with closely related input values. For example, identifying tourists showing similar behaviour can be used for targeted advertising. In some cases, noise and outliers can also be found in a dataset. k-means and DBSCAN are typical examples of unsupervised learning techniques.
- Reinforcement Learning is an approach where a system can adapt its own behaviour in different ways, evaluating the results achieved. An improved result leads to a higher reward and the goal is to maximize the cumulative results. For example, reinforcement learning can be used to adapt traffic signal control, e.g., with the goal of minimizing queue length. Examples of reinforcement learning techniques are Q-learning and SARSA.
- Deep Learning represents a family of machine learning methods, where “deep” refers to the use of artificial neural networks with multiple layers. Deep learning is particularly suitable for applications with high data dimensionality and can cope with high volume and velocity of data. On the other hand, deep learning requires a large amount of training data. Typical deep learning techniques are Convolutional Neural Networks (CNN), Restricted Boltzman Machine and Deep Belief Networks.
- Secure booting: When malware infects the boot sector of the system, it may not be detected by the security system present in the operating system. In particular, if malware infects a system on which a smart city IoT platform operates, a threat may be posed to the entire smart city infrastructure. To solve this problem, secure booting technology is implemented, thereby preemptively preventing operating systems and software that are not authorized. This security procedure technology serves as the first wall against various malicious attacks that can target a smart city. Secure booting technology checks the encrypted boot loader, kernel, and system software in authorized hardware. To this end, hardware is manufactured with authentication keys embedded in it. Secure booting technology checks such encrypted signatures before the operating system reads the kernel and system images, and only then the authenticated files are booted.
- Secure networks: IoT devices in a smart city use various network technologies to access the cloud-based IoT platform. To prevent exposing important information because of security threats occurring during data transmission, messages are encrypted and transmitted using encryption algorithms. For example, devices using Bluetooth versions prior to BLE 4.0/4.1 use encrypted authentication pairing, while BLE 4.2 devices use encrypted low-power security connection authentication feature. In the case of ZigBee, it defines security based on a 128-bit AES encryption algorithm, and uses three types of security keys (master, network, and link) for network security. The 3GPP 5G technology has additional security features to securely process network access volume increased by over ten times because of IoT devices. For this, the 3GPP uses international mobile subscriber identity (IMSI) encryption to protect subscriber information, security edge protection proxy (SEPP) that resolves the roaming domain security issue (Signaling System No. 7 ) and implements security between application layers among different carriers, and an integrated authentication framework feature that enables the use of the same authentication method for access to 3GPP and non-3GPP components.
- Authentication and access control: Devices and users connected to a smart city are managed using existing authentication and access control technologies. However, lightweight IoT-based devices are not powerful enough for using such technologies, hence, manual access control technologies are used. Various ways to resolve this issue are being proposed, including more detailed log and event monitoring, isolation of suspected devices, and role-based access control policies.
- Data protection: Ensuring the integrity of data constituting a smart city is an important task. If the integrity of key data is compromised, it may cause attacks on the overall smart city services and infrastructure. System settings information, log files, system libraries, and binary execution files are examples of data that must be protected in a smart city platform. Traditional cyclic redundancy check (CRC) and hash functions for SHA-2 and above are used to verify data integrity. In addition, smart cities inevitably collect personal information; in recent years, governments have been strengthening laws to protect personal information, enforcing systems handling personal information to follow such regulations. For example, the General Data Protection Regulations (GDPR) enacted in Europe in May 2018 puts strong restrictions on the processing, storage and usage of personal information and what is required for properly anonymizing information . The regulations consider broad subjects spanning consent management, right to be deleted, and other personal information processing and management. Korea has also revised the existing personal information protection law to be compliant with the European GDPR, and other countries around the world are instituting laws to protect personal information and developing various supporting technologies for smart cities.
4. Open Challenges for IoT-Enabled Smart Cities
4.1. Overcome Application Silos
4.2. Evolve towards Flexible IoT Platforms
4.3. Evolve towards Multi-Player, Cross-Organization IoT Platforms and Applications
4.4. Business Opportunities Creation
- Favour peer-to-peer relations -in contrast to the traditionally hierarchical command and contractual relationships.
- Settle new value distribution and governance among the community of peers where profitability is not its main/unique driving force.
- Leverage privacy-aware public infrastructure that results in the (generally) open access provision of common resources that favour access, reproducibility and derivativeness.
4.5. Achieve Transparency and Acceptance
4.6. Integration on Future Networks and Novel Computing Paradigms
4.7. Develop Resiliency to Support Mission-Critical Applications
4.8. Privacy Regulations on Data in Different Countries
Conflicts of Interest
- Bairoch, P. Cities and Economic Development: From the Dawn of History to the Present; University of Chicago Press: Chicago, IL, USA, 1988. [Google Scholar]
- United Nations, Department of Economic and Social Affairs. “2018 Revision of World Urbanization Prospects”. Available online: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html (accessed on 30 June 2021).
- Hoornweg, D.; Pope, K. Population Predictions for the World’s Largest Cities in the 21st Century. Environ. Urban. 2017, 29, 195–216. [Google Scholar] [CrossRef][Green Version]
- Ochoa, J.J.; Tan, Y.; Qian, Q.K.; Shen, L.; Moreno, E.L. Learning from Best Practices in Sustainable Urbanization. Habitat Int. 2018, 78, 83–95. [Google Scholar] [CrossRef]
- Addanki, S.C.; Venkataraman, H. Greening the Economy: A Review of Urban Sustainability Measures for Developing New Cities. Sustain. Cities Soc. 2017, 32, 1–8. [Google Scholar] [CrossRef]
- Shahidehpour, M.; Li, Z.; Ganji, M. Smart Cities for a Sustainable Urbanization: Illuminating the Need for Establishing Smart Urban Infrastructures. IEEE Electrif. Mag. 2018, 6, 16–33. [Google Scholar] [CrossRef]
- Ménard, A. How Can We Recognize the Real Power of the Internet of Things? Available online: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-can-we-recognize-the-real-power-of-the-internet-of-things# (accessed on 17 January 2021).
- D’Auria, A.; Tregua, M.; Vallejo-Martos, M. Modern Conceptions of Cities as Smart and Sustainable and Their Commonalities. Sustainability 2018, 10, 2642. [Google Scholar] [CrossRef][Green Version]
- Alavi, A.H.; Jiao, P.; Buttlar, W.G.; Lajnef, N. Internet of Things-Enabled Smart Cities: State-of-the-Art and Future Trends. Meas. J. Int. Meas. Confed. 2018, 129, 589–606. [Google Scholar] [CrossRef]
- Gharaibeh, A.; Salahuddin, M.A.; Hussini, S.J.; Khreishah, A.; Khalil, I.; Guizani, M.; Al-Fuqaha, A. Smart Cities: A Survey on Data Management, Security, and Enabling Technologies. IEEE Commun. Surv. Tutor. 2017, 19, 2456–2501. [Google Scholar] [CrossRef]
- Heng, T.M.; Low, L. Practioners’ Forum: The Intelligent City: Singapore Achieving the Next Lap. Technol. Anal. Strateg. Manag. 1993, 5, 187–202. [Google Scholar] [CrossRef]
- Tariq, M.A.U.R.; Faumatu, A.; Hussein, M.; Shahid, M.L.U.R.; Muttil, N. Smart City-Ranking of Major Australian Cities to Achieve a Smarter Future. Sustainability 2020, 12, 2797. [Google Scholar] [CrossRef][Green Version]
- Chamoso, P.; González-Briones, A.; Rodríguez, S.; Corchado, J.M. Tendencies of Technologies and Platforms in Smart Cities: A State-of-the-Art Review. Wirel. Commun. Mob. Comput. 2018, 2018. [Google Scholar] [CrossRef][Green Version]
- Silva, B.N.; Khan, M.; Han, K. Towards Sustainable Smart Cities: A Review of Trends, Architectures, Components, and Open Challenges in Smart Cities. Sustain. Cities Soc. 2018, 38, 697–713. [Google Scholar] [CrossRef]
- Talari, S.; Shafie-khah, M.; Siano, P.; Loia, V.; Tommasetti, A.; Catalão, J. A Review of Smart Cities Based on the Internet of Things Concept. Energies 2017, 10, 421. [Google Scholar] [CrossRef][Green Version]
- Institute for Management Development; Singapore University for Technology and Design (SUTD). Smart City Index 2020; Singapore University for Technology and Design: Singapore, 2020. [Google Scholar]
- Berrone, P.; Ricart, J.E. IESE Cities in Motion Index; IESE Business School. University of Navarra: Barcelona, Spain, 2019. [Google Scholar]
- Galache, J.A.; Santana, J.R.; Gutiérrez, V.; Sánchez, L.; Sotres, P.; Muñoz, L. Towards Experimentation-Service Duality within a Smart City Scenario. In Proceedings of the 9th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Courmayeur, Italy, 9–11 January 2012; pp. 175–181. [Google Scholar]
- Gutiérrez, V.; Galache, J.A.; Sańchez, L.; Munõz, L.; Hernández-Muñoz, J.M.; Fernandes, J.; Presser, M. SmartSantander: Internet of Things Research and Innovation through Citizen Participation. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2013; Volume 7858 LNCS, pp. 173–186. [Google Scholar]
- Cirillo, F.; Solmaz, G.; Berz, E.L.; Bauer, M.; Cheng, B.; Kovacs, E. A Standard-Based Open Source IoT Platform: FIWARE. IEEE Internet Things Mag. 2020, 2, 12–18. [Google Scholar] [CrossRef]
- Cantera-Fonseca, J.M.; Galán-Márquez, F.; Jacobs, T. FIWARE-NGSI v2 Specification. Available online: https://orioncontextbroker.docs.apiary.io/# (accessed on 22 November 2018).
- Sotres, P.; Lanza, J.; Sánchez, L.; Santana, J.R.; López, C.; Muñoz, L. Breaking Vendors and City Locks through a Semantic-Enabled Global Interoperable Internet-of-Things System: A Smart Parking Case. Sensors 2019, 19, 229. [Google Scholar] [CrossRef][Green Version]
- Chang, F.; Das, D. Smart Nation Singapore: Developing Policies for a Citizen-Oriented Smart City Initiative. In Developing National Urban Policies; Springer: Singapore, 2020; pp. 425–440. [Google Scholar]
- Hu, R. The State of Smart Cities in China: The Case of Shenzhen. Energies 2019, 12, 4375. [Google Scholar] [CrossRef][Green Version]
- City IQ platform. Available online: https://docs.cityiq.io (accessed on 30 June 2021).
- Martin-Caravaca, S. How to Breaking Down Silos to Create a Better Smart City? Available online: http://smartcitybrand.com/smart-city/how-to-breaking-down-silos-to-create-a-better-smart-city (accessed on 14 May 2021).
- Amsterdam Smart City. Available online: https://amsterdamsmartcity.com (accessed on 13 January 2021).
- Macpherson, L. 8 Years On, Amsterdam Is Still Leading the Way as A Smart City. Available online: https://towardsdatascience.com/8-years-on-amsterdam-is-still-leading-the-way-as-a-smart-city-79bd91c7ac13 (accessed on 13 January 2021).
- Bee Smart City. Amsterdam Smart City: A World Leader in Smart City Development. Available online: https://hub.beesmart.city/city-portraits/smart-city-portrait-amsterdam (accessed on 13 January 2021).
- Sunshine Coast Council. A Smarter Sunshine Coast, Smart City Brochure. Available online: https://d1j8a4bqwzee3.cloudfront.net/~/media/Corporate/Documents/SmartCities/SCIP Brochure.pdf?la=en (accessed on 13 January 2021).
- Sunshine Coast Council. Smart City Program. Available online: https://www.sunshinecoast.qld.gov.au/smartcities (accessed on 13 January 2021).
- Central de Atendimento Rio. Available online: https://www.1746.rio/ (accessed on 18 January 2021).
- Slaný, V.; Lučanský, A.; Koudelka, P.; Mareček, J.; Krčálová, E.; Martínek, R. An Integrated IoT Architecture for Smart Metering Using Next Generation Sensor for Water Management Based on LoRaWAN Technology: A Pilot Study. Sensors 2020, 20, 4712. [Google Scholar] [CrossRef]
- Abbas, O.; Abou Rjeily, Y.; Sadek, M.; Shahrour, I. A Large-Scale Experimentation of the Smart Sewage System. Water Environ. J. 2017, 31, 515–521. [Google Scholar] [CrossRef]
- Pasolini, G.; Toppan, P.; Zabini, F.; de Castro, C.; Andrisano, O. Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems. Appl. Sci. 2019, 9, 3281. [Google Scholar] [CrossRef][Green Version]
- Saleem, Y.; Sotres, P.; Fricker, S.; Lopez de la Torre, C.; Crespi, N.; Lee, G.M.; Minerva, R.; Sanchez, L. IoTRec: The IoT Recommender for Smart Parking System. IEEE Trans. Emerg. Top. Comput. 2020, 1. [Google Scholar] [CrossRef]
- Yuksel, K.; Kinet, D.; Chah, K.; Caucheteur, C. Implementation of a Mobile Platform Based on Fiber Bragg Grating Sensors for Automotive Traffic Monitoring. Sensors 2020, 20, 1567. [Google Scholar] [CrossRef][Green Version]
- García-Domínguez, A.; Galvan-Tejada, C.E.; Zanella-Calzada, L.A.; Gamboa, H.; Galván-Tejada, J.I.; Celaya Padilla, J.M.; Luna-García, H.; Arceo-Olague, J.G.; Magallanes-Quintanar, R. Deep Artificial Neural Network Based on Environmental Sound Data for the Generation of a Children Activity Classification Model. PeerJ Comput. Sci. 2020, 6, e308. [Google Scholar] [CrossRef]
- Arroyo, P.; Herrero, J.; Suárez, J.; Lozano, J. Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring. Sensors 2019, 19, 691. [Google Scholar] [CrossRef][Green Version]
- Huang, H.; Savkin, A.V.; Ding, M.; Huang, C. Mobile Robots in Wireless Sensor Networks: A Survey on Tasks. Comput. Netw. 2019, 148, 1–19. [Google Scholar] [CrossRef]
- Qi, F.; Zhu, X.; Mang, G.; Kadoch, M.; Li, W. UAV Network and IoT in the Sky for Future Smart Cities. IEEE Netw. 2019, 33, 96–101. [Google Scholar] [CrossRef]
- Anjomshoaa, A.; Duarte, F.; Rennings, D.; Matarazzo, T.J.; Desouza, P.; Ratti, C. City Scanner: Building and Scheduling a Mobile Sensing Platform for Smart City Services. IEEE Internet Things J. 2018, 5, 4567–4579. [Google Scholar] [CrossRef]
- Wang, J.; Jiang, C.; Zhang, K.; Quek, T.Q.S.; Ren, Y.; Hanzo, L. Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications. IEEE Wirel. Commun. 2018, 25, 122–132. [Google Scholar] [CrossRef][Green Version]
- Lv, B.; Xu, H.; Wu, J.; Tian, Y.; Zhang, Y.; Zheng, Y.; Yuan, C.; Tian, S. LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities. IEEE Access 2019, 7, 79895–79907. [Google Scholar] [CrossRef]
- Guerrero-Ibáñez, J.; Zeadally, S.; Contreras-Castillo, J. Sensor Technologies for Intelligent Transportation Systems. Sensors 2018, 18, 1212. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Sanchez, L.; Muñoz, L.; Galache, J.A.; Sotres, P.; Santana, J.R.; Gutierrez, V.; Ramdhany, R.; Gluhak, A.; Krco, S.; Theodoridis, E.; et al. SmartSantander: IoT Experimentation over a Smart City Testbed. Comput. Netw. 2014, 61, 217–238. [Google Scholar] [CrossRef][Green Version]
- Chen, Y.; Nakazawa, J.; Yonezawa, T.; Tokuda, H. Cruisers: An Automotive Sensing Platform for Smart Cities Using Door-to-Door Garbage Collecting Trucks. Ad Hoc Netw. 2019, 85, 32–45. [Google Scholar] [CrossRef]
- Burke, A.; Estrin, D.; Hansen, M.; Parker, A.; Ramanathan, N.; Reddy, S.; Srivastava, M.B. Participatory Sensing. In Proceedings of the First Workshop on World-Sensor-Web: Mobile Device Centric Sensory Networks and Applications (WSW 2006) at aACM SenSys 2006, Boulder, CO, USA, 31 October 2006; ACM: New York, NY, USA, 2006. [Google Scholar]
- Dutta, P.; Aoki, P.M.; Kumar, N.; Mainwaring, A.; Myers, C.; Willett, W.; Woodruff, A. Common Sense—Participatory Urban Sensing Using a Network of Handheld Air Quality Monitors. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009, Berkeley, CA, USA, 4–6 November 2009; ACM Press: New York, NY, USA, 2009; pp. 349–350. [Google Scholar]
- Campbell, A.T.; Eisenman, S.B.; Lane, N.D.; Miluzzo, E.; Peterson, R.A. People-Centric Urban Sensing. In Proceedings of the ACM International Conference Proceeding Series; ACM Press: New York, NY, USA, 2006; Volume 220, p. 18-es. [Google Scholar]
- Ma, H.; Zhao, D.; Yuan, P. Opportunities in Mobile Crowd Sensing. IEEE Commun. Mag. 2014, 52, 29–35. [Google Scholar] [CrossRef]
- Sanchez, L.; Gutierrez, V.; Galache, J.A.; Sotres, P.; Santana, J.R.; Muñoz, L. Engaging Individuals in the Smart City Paradigm: Participatory Sensing and Augmented Reality. Interdiscip. Stud. J. 2014, 3, 129. [Google Scholar]
- Mohan, P.; Padmanabhan, V.N.; Ramjee, R. Nericell: Rich Monitoring of Road and Traffic Conditions Using Mobile Smartphones. In Proceedings of the SenSys’08—6th ACM Conference on Embedded Networked Sensor Systems, Raleigh, NC, USA, 5–7 November 2008; ACM Press: New York, NY, USA, 2008; pp. 323–336. [Google Scholar]
- Liu, X.; Song, Z.; Ngai, E.; Ma, J.; Wang, W. PM2:5 Monitoring Using Images from Smartphones in Participatory Sensing. In Proceedings of the IEEE INFOCOM Workshops, Hong Kong, China, 26 April–1 May 2015; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2015; Volume 2015, pp. 630–635. [Google Scholar]
- Montenegro, G.; Kushalnagar, N.; Hui, J.; Culler, D. Transmission of IPv6 Packets over IEEE 802.15.4 Networks; Internet proposed standard RFC, 4944; Internet Engineering Task Force: Wilmington, DE, USA, 2007; p. 130. [Google Scholar]
- DigiMesh Products. Available online: https://www.digi.com/products/browse/digimesh (accessed on 22 September 2020).
- Z-Wave Technology. Available online: https://www.z-wave.com/ (accessed on 22 September 2020).
- Zigbee Alliance, WPAN Industry Group. The Industry Group Responsible for the ZigBee Standard and Certification. Available online: http://www.zigbee.org (accessed on 23 September 2020).
- Bluetooth Core Specification Working Group. Bluetooth Core Specification. Available online: https://www.bluetooth.com/specifications/bluetooth-core-specification/ (accessed on 23 September 2020).
- Haartsen, J.C. Bluetooth Radio System. IEEE Pers. Commun. 2000, 7, 28–36. [Google Scholar] [CrossRef]
- ECMA International. ECMA-340. Near Field Communication-Interface and Protocol (NFCIP-1); ECMA: Geneva, Switzerland, 2013. [Google Scholar]
- Sornin, N.; Luis, M.; Eirich, T.; Kramp, T.; Hersent, O. LoRaWAN Specification; LoRa Alliance: Fremont, CA, USA, 2015. [Google Scholar]
- Sigfox Device Radio Specifications. Available online: https://build.sigfox.com/sigfox-device-radio-specifications (accessed on 23 September 2020).
- 3GPP. Evolved Universal Terrestrial Radio Access (E-UTRA); NB-IOT; Technical Report for BS and UE Radio Transmission and Reception. Technical Report (TR) 36.802, V13.0.0. 2016. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3033 (accessed on 30 June 2021).
- 3GPP Release 13. Available online: https://www.3gpp.org/release-13 (accessed on 30 June 2021).
- Mekki, K.; Bajic, E.; Chaxel, F.; Meyer, F. A Comparative Study of LPWAN Technologies for Large-Scale IoT Deployment. ICT Express 2019, 5, 1–7. [Google Scholar] [CrossRef]
- Sigfox Coverage Map. Available online: www.sigfox.com/en/coverage (accessed on 30 June 2021).
- Internet of Things (IoT) on IBM Cloud. Available online: https://www.ibm.com/cloud/internet-of-things (accessed on 13 January 2021).
- Microsoft Azure IoT. Available online: https://azure.microsoft.com/en-us/overview/iot/ (accessed on 13 January 2021).
- Google Cloud IoT Solutions. Available online: https://cloud.google.com/solutions/iot (accessed on 13 January 2021).
- OneM2M. Available online: https://onem2m.org/ (accessed on 13 January 2021).
- OMA Lightweight M2M. Available online: https://omaspecworks.org/what-is-oma-specworks/iot/lightweight-m2m-lwm2m/ (accessed on 13 January 2021).
- ETSI ISG CIM. NGSI-LD. Available online: https://www.etsi.org/committee/cim (accessed on 13 January 2021).
- Open and Agile Smart Cities (OASC). Available online: https://oascities.org/ (accessed on 13 January 2021).
- Eurocities. Available online: https://eurocities.eu/ (accessed on 13 January 2021).
- OASC Minimum Interoperability Mechanisms, Open & Agile Smart Cities. Available online: https://oascities.org/minimal-interoperability-mechanisms/ (accessed on 30 June 2021).
- Smart Applications REFerence Ontology, and Extensions. Available online: https://saref.etsi.org/ (accessed on 30 June 2021).
- One Data Model. Available online: https://onedm.org/ (accessed on 30 June 2021).
- Smart Data Models initiative. Available online: https://smartdatamodels.org/ (accessed on 30 June 2021).
- TM Forum Open APIs. Available online: https://projects.tmforum.org/wiki/display/API/Open+API+Table (accessed on 30 June 2021).
- FIWARE Foundation. Available online: https://www.fiware.org (accessed on 13 January 2021).
- Willems, J.; van den Bergh, J.; Viaene, S. Smart City Projects and Citizen Participation: The Case of London. In Public Sector Management in a Globalized World; Springer Fachmedien Wiesbaden: Berlin/Heidelberg, Germany, 2017; pp. 249–266. [Google Scholar]
- Gaur, A.; Scotney, B.; Parr, G.; McClean, S. Smart City Architecture and Its Applications Based on IoT. In Procedia Computer Science; Elsevier B.V.: Amsterdam, The Netherlands, 2015; Volume 52, pp. 1089–1094. [Google Scholar]
- Mehmood, Y.; Ahmad, F.; Yaqoob, I.; Adnane, A.; Imran, M.; Guizani, S. Internet-of-Things-Based Smart Cities: Recent Advances and Challenges. IEEE Commun. Mag. 2017, 55, 16–24. [Google Scholar] [CrossRef]
- Younas, M. Research Challenges of Big Data. Serv. Oriented Comput. Appl. 2019, 13, 105–107. [Google Scholar] [CrossRef][Green Version]
- Habibzadeh, H.; Kaptan, C.; Soyata, T.; Kantarci, B.; Boukerche, A. Smart City System Design: A Comprehensive Study of the Application and Data Planes. ACM Comput. Surv. 2019, 52, 1–38. [Google Scholar] [CrossRef]
- Eckhoff, D.; Wagner, I. Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions. IEEE Commun. Surv. Tutor. 2018, 20, 489–516. [Google Scholar] [CrossRef][Green Version]
- Russell, T. Signaling System# 7; McGraw-Hill Education: New York, NY, USA, 2006. [Google Scholar]
- Seo, J.; Kim, K.; Park, M.; Park, M.; Lee, K. An Analysis of Economic Impact on IoT under GDPR. In Proceedings of the International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, Jeju Island, Korea, 18–20 October 2017; Volume 2017, pp. 879–881. [Google Scholar]
- Soe, R.-M. Smart Cities: From Silos to Cross-Border Approach. Int. J. E Plan. Res. 2018, 7, 70–88. [Google Scholar] [CrossRef]
- Upton, N.; Hewlett Packard Enterprise. Why the Internet of Things Demands a Flexible Platform? Available online: https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Why-the-internet-of-things-demands-a-flexible-platform (accessed on 11 April 2021).
- Santolalla, O. Why Smart Cities Services Need Federated Access. Available online: https://www.ubisecure.com/stories/smart-cities/ (accessed on 11 April 2021).
- Appio, F.P.; Lima, M.; Paroutis, S. Understanding Smart Cities: Innovation Ecosystems, Technological Advancements, and Societal Challenges. Technol. Forecast. Soc. Chang. 2019, 142, 1–14. [Google Scholar] [CrossRef]
- McLaren, D.; Agyeman, J. Sharing Cities: A Case for Truly Smart and Sustainable Cities; MIT Press: Cambridge, MA, USA, 2015; ISBN 9780262029728. [Google Scholar]
- Fuster-Morell, M.; Carballa-Smichowski, B.; Smorto, G.; Espelt, R.; Imperatore, P.; Rebordosa, M.; Rocas, M.; Rodríguez, N.; Senabre, E.; Ciurcina, M. Multidisciplinary Framework on Commons Collaborative Economy; Decode Project. Available online: https://decodeproject.eu/publications/multidisciplinary-framework-commons-collaborative-economy (accessed on 20 June 2021).
- Sepasgozar, S.M.E.; Hawken, S.; Sargolzaei, S.; Foroozanfa, M. Implementing Citizen Centric Technology in Developing Smart Cities: A Model for Predicting the Acceptance of Urban Technologies. Technol. Forecast. Soc. Chang. 2019, 142, 105–116. [Google Scholar] [CrossRef]
- Van Kranenburg, R.; Stembert, N.; Moreno, M.V.; Skarmeta, A.F.; López, C.; Elicegui, I.; Sánchez, L. Co-creation as the Key to a Public, Thriving, Inclusive and Meaningful EU IoT. In Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services; Springer: Berlin/Heidelberg, Germany, 2014; pp. 396–403. [Google Scholar]
- Gutiérrez, V.; Amaxilatis, D.; Mylonas, G.; Muñoz, L. Empowering Citizens Toward the Co-Creation of Sustainable Cities. IEEE Internet Things J. 2018, 5, 668–676. [Google Scholar] [CrossRef][Green Version]
- Rao, S.K.; Prasad, R. Impact of 5G Technologies on Smart City Implementation. Wirel. Pers. Commun. 2018, 100, 161–176. [Google Scholar] [CrossRef]
- Sabella, D.; Vaillant, A.; Kuure, P.; Rauschenbach, U.; Giust, F. Mobile-Edge Computing Architecture: The Role of MEC in the Internet of Things. IEEE Consum. Electron. Mag. 2016, 5, 84–91. [Google Scholar] [CrossRef]
- Skarin, P.; Tarneberg, W.; Arzen, K.E.; Kihl, M. Towards Mission-Critical Control at the Edge and over 5G. In Proceedings of the IEEE International Conference on Edge Computing, EDGE 2018, San Francisco, CA, USA, 2–7 July 2018; pp. 50–57. [Google Scholar]
- Badii, C.; Bellini, P.; Difino, A.; Nesi, P. Smart City IoT Platform Respecting GDPR Privacy and Security Aspects. IEEE Access 2020, 8, 23601–23623. [Google Scholar] [CrossRef]
|City||Status||Motivations||IoT Infrastructure Deployed||Supported Services||IoT Platform||Communication Technologies||Start of Developments|
|Santander, Spain||Urban Lab|
Smart Public Services
|Research and Innovation testbed|
Progressive smartization of public services
|12,000 sensors (testbed)||Parking Service|
Traffic Intensity Monitoring.
Parks and gardens irrigation.
Urban Waste Management.
Streetlight Management.Traffic Management
|Busan, South Korea||Commercial smart city services are available||Sustainable and interoperable IoT-based smart city||Six IoT living labs for energy, factory, logistics, healthcare, urban regeneration and transportation||26 IoT services incl. Smart Parking, IoT mirror, VR smart tourism, Context-awareness safety warning, Smart street light, Smart drone safety service, Smart energy management, Smart traffic management service, Smart cross road safety||oneM2M||Cellular (3G/LTE/5G)|
|Singapore||National level smart city. Commercial smart city services are available.||To resolve various future urban problems||Public WiFi access points|
Around 120 living labs
Water usage management
Smart street light
Security warning buttonWaste management
|Government supported proprietary platform.||WiFi||2014|
|Shenzen, China||City wide commercial services and platforms are available||A vision for using ICT to enhance public services, city management, and economic development||>400,000 NB-IoT enabled base stations||A city level health management portal|
Public surveillance system
Smart industry park
Sensors and actuators supporting street light automation
|Proprietary developed with several ICT firms and universities||NB-IoT|
|Atlanta, US||Commercial smart city applications are available + Pilot-level services||Leverage a data-centric approach to improve mobility, public safety, and sustainability, for ultimately enhancing citizen well-being and fostering the economic growth||CCTV cameras|
200 CityIQ sensor nodes (installed with various IoT sensors)
|Mobility (e.g., North Av. Smart Corridor, Bike sharing)|
Safety (e.g., ShotSpotter, NotifyATL)
Environment (e.g., Smart Neighbourhood, Smart trash bins)
|Proprietary, developed by GE and Intel.||Cellular (3G, 4G)|
|Amsterdam. Netherlands||Smart City umbrella organization supporting a variety of projects||Citizen-focused projects to improve quality of life, create sustainable growth and ensure efficient use of resources||iBeacons, Things Network with 46 gateways||Fuzzy navigation highlighting “hidden gems”|
MyNeighbour App supporting neighbourhood safety and sustainability
Open Data from different city departments
|No central platform||LoRa, Bluetooth||2009|
|Sunshine Coast Region, Australia||Commercial smart city deployment offering a number of services||Improve city services, reduce resource consumption and improve the safety and quality of life of citizens. Support data-driven design and planning||>200 public WiFi access points, smart street lights, waste bin sensors, irrigation sensors, flood sensors||Sunshine Coast Council (SCC) App|
Smart Street with free public WiFi collecting data.
Pedestrian and cyclist counters
Networked LED street lighting
Waste bin sensors
Networked irrigation system
Networked flood sensors
|Proprietary, developed with Cisco and Telstra||WiFi||2016|
|Rio de Janeiro, Brazil||Commercial smart city services are available||Safety, security and disaster prevention; Smooth functioning of day-to-day operations of public services||500 surveillance cameras, GPS sensors on all garbage trucks,||Video surveillance of traffic|
Garbage truck fleet management
Central 1746 (Participatory sensing)
|Proprietary, developed with IBM||Cellular (3G, 4G)||2010|
|Coverage||1~10 cm||3–30 m||30~100 m||30~100 m||3–15 km||5–25 km||~11 km||~15 km|
|Frequency||13.56 MHz||2.4 GHz||2.4 GHz||2.4 GHz||868–915 MHz||868–915 MHz||LTE in-band||LTE in-band|
|Data rate||106 ~ 424 kbps||1~3 Mbps||40~200 kbps||20/40/250 kbps||300 bps ~ 50 kbps||100 bps||~10 Mbps||~200 kbps|
|Payload size||358 bytes||64 bytes||127 bytes||256 bytes||12 bytes||-||-|
|Smart parking||Provides parking information in real time to improve the convenience of parking users and solve city parking difficulties.|
|Smart tram||Improves public transportation efficiency by introducing an eco-friendly energy-based unmanned tram, and to build a smart transportation system that enables experience and promotion by applying advanced technologies such as digital tokens in a package format.|
|Smart traffic control system||Improves traffic congestion by controlling signals by itself according to road conditions based on the analysis of traffic information collected in real time.|
|Transportation sharing||Provides shared vehicle and bicycle services that can be used from departure to destination to eliminate blind spots in public transportation.|
|Smart Environment||Eco-filtering||Improves the quality of river water by creating an eco-friendly storage and treatment space with a natural purification function on the riverside to prevent the direct inflow of pollutants into the river.|
|Fine dust management||Provides a fine dust forecast service with high spatial precision and prediction accuracy through IoT sensor data monitoring and learning, according to real-time environmental conditions.|
|Smart filtration management||Small building type water purification facilities near consumers in the city centre are distributed and arranged to supply fresh, low-chlorine water to the home. Real-time water quality/quantity monitoring and remote monitoring control are provided.|
|Water reuse||Developed as an alternative water resource from sewage treated water that has undergone an advanced treatment process, and is supplied as various necessary water (main transport water, maintenance water, washing water, etc.)|
|Smart Energy||Building energy management system||Provides a system that monitors building energy consumption in real time using IoT devices and automatically optimizes and controls and manages energy production and use.|
|Energy efficient building||Uses eco-friendly and renewable energy to secure energy independence and provide an energy transaction system between individuals.|
|Virtual power plant||Integrates the operation of small-scale distributed power facilities such as solar energy and household energy storage systems (ESS), and manages it as a single power plant.|
|Smart Security||Intelligent CCTV||Secures golden time by reducing human analysis errors and analysis time by real-time prediction of crime occurrence signs using visual intelligence technology and AI.|
|School zone security||Secures commuting safety by linking information tracking the movement of students and vehicles around the school zone with vehicle information and introducing an accident prevention system.|
|Disaster prediction||Prevents large-scale accidents by monitoring terrain changes and underground buried conditions with drones, satellites, and IoT sensors, and predicting AI-based ground subsidence.|
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Bauer, M.; Sanchez, L.; Song, J. IoT-Enabled Smart Cities: Evolution and Outlook. Sensors 2021, 21, 4511. https://doi.org/10.3390/s21134511
Bauer M, Sanchez L, Song J. IoT-Enabled Smart Cities: Evolution and Outlook. Sensors. 2021; 21(13):4511. https://doi.org/10.3390/s21134511Chicago/Turabian Style
Bauer, Martin, Luis Sanchez, and JaeSeung Song. 2021. "IoT-Enabled Smart Cities: Evolution and Outlook" Sensors 21, no. 13: 4511. https://doi.org/10.3390/s21134511