Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore
Abstract
:1. Introduction
- Automation: The capability to handle automatic devices and undertake automatic functions.
- Multi-functionality: Allow more than one optimization function in a building.
- Adaptability: The capability to adapt to the needs of users based on learning, prediction, and variables.
- Interactivity: Allow interaction between users in a building.
- Efficiency: The ability to achieve energy and other efficiencies promoting time and cost-saving.
- High bandwidth requirements: With a greater emphasis on integrating BIM with FM, the future SFM will use BIM as the primary visualization and information source model. Additionally, the adoption of new technologies in FM such as AR/VR/MR, image analytics, drones, computer vision, cloud computing requires high bandwidth to support seamless real-time analysis. The current wireless networks are not consistent enough to provide high bandwidth on the go [23].
- Low latency: Latency is the time that passes from the moment information is sent from a device until the receiver can use it. Low latency is a must for future cloud-based systems, AR/VR/MR, drones, and computer vision. The current wireless networks cannot support very low latency requirements [24].
- Cybersecurity: The current wireless networks pose huge cybersecurity risks when millions of devices are connected using IoT. Current networks cannot transact with large amounts of encrypted data at a time [25].
- Supporting a large volume of devices: There will be a gradual increase in IoT devices as SFM adoption increases. It will be difficult for current wireless networks to support such a large volume of devices in a defined radius [26].
2. Literature Review
2.1. Smart Facilities Management (SFM)
2.2. Importance of 5G Network
2.2.1. 5G Network
2.2.2. Expected Benefits of 5G Network
2.2.3. Sustainable Future with 5G Network
- Smart energy management across various sectors through the smart meter and grid system, IoT based monitoring, and AI-enabled analytics.
- Remote working, incorporating new-age technologies like AR, VR, work from home, leading to reduced office space and travel.
- Low wastage and proactive planning leading to just-in-time efficiency in broader processes.
- Intelligent movement management of people and goods leading to reduced journey time.
- Smart building management through effective real-time systems.
- Empowered agriculture sector through predictive and sensor-based technology leading to reduced food waste.
2.3. Drivers of Smart Facilities Management
2.3.1. Internet of Things (IoT)
2.3.2. Artificial Intelligence (AI)
2.3.3. Augmented and Virtual Reality (AR/VR)
2.3.4. Digital Twin (DT)
2.3.5. Video and Image Analytics
2.3.6. Drones
2.4. Singapore’s Roadmap for 5G Technologies Implementation
2.4.1. Singapore Smart Nation Vision
- Research institutes and industry collaboration for test-bedding,
- A data portal for open exchange of government data,
- Research and development (R&D) investments,
- Laboratories for the development and piloting of technological solutions,
- Promoting start-ups and innovations,
- Safeguarding data, systems, and networks through enhanced cybersecurity measures,
- Building computational capabilities among citizens through educational programs at various levels, including young children, secondary-school students, and working professionals.
2.4.2. NUS Department of Building (DoB) Smart Nation Research Initiatives
2.4.3. Infocomm Media Development Authority (IMDA)’s 5G Roadmap
2.4.4. Department of Building’s 5G Advanced BIM Lab
3. 5G Use Cases in Department of Building (DoB), NUS
3.1. Research and Industry Collaboration Projects
3.1.1. Smart Energy Management: Energy Digital Twin
- Real-time performance monitoring of building systems against design specifications.
- Real-time optimization of a building’s operation to achieve improved building performance and indoor environmental quality.
- Integration with machine learning algorithms for automated fault detection and diagnostics.
- Solar photovoltaic (PV) energy output analysis for automated correction to maximize output.
3.1.2. Smart Maintenance Management: Lift Maintenance System and Training Application Using Augmented Reality and Artificial Intelligence and Digital Twin
- a)
- AR platform development: The AR platform development will connect lift technicians with the AR cloud-based centralized system for AR experience, automated work orders, job notifications, and building animated repair sequences to guide technicians through specific lift checking and maintenance tasks.
- b)
- AR content for lift training: AR content development for lift training ready-made lesson plans covers a wide range of lift maintenance topics. The easy, intuitive AR platform allows experienced trainers to create virtual 3D infographics to share their maintenance and troubleshooting sequences with trainees’ recorded scenes.
- c)
- AI diagnostic tool: AI diagnostic tool will help to identify the most common lift faults and their causes. This will also help in fostering the predictive maintenance technologies in the lift maintenance system.
- d)
- AR remote guidance tool: AR remote guidance tool will allow simple and quick remote guidance from a team of subject matter experts who can resolve issues and perform diagnostics without incurring any travel expenses and have a higher rate of first-time fixes.
3.1.3. Smart Maintenance Management: NUS BIM Integrated Facilities Management System
- A BIM integrated system to manage town for town council, managing agents, and residents.
- A comprehensive all in one platform to create and manage defects, reporting, booking of facilities, and performance management.
- A powerful tool to integrate BIM with FM for better visualization.
- Powered by data analytics to gain detailed insight into facilities.
- Integration with GIS with more updated building outlines, information about government services and urban mobility.
- Collaborate with industry partners to integrate their application inside BiFM such as washroom management, smart control of devices, smart security, smart hand sanitation, and BIM object library.
3.1.4. Smart Maintenance Management: Real-time Facade Inspection System Using AI and Drones
- Drone-based camera to capture and analyze façade defects using AI.
- Real-time analysis by remote qualified personal for better operational efficiency.
- Cloud-based AI-powered system on conducting faster processing.
3.1.5. Smart Maintenance Management: BIM-Based Design-For-Maintainability Assessment of Building Systems
- Methodology for a design for maintainability assessment system for building designs using BIM.
- Novel and flexible assessment system for maintainability with global applicability.
- Building design decision support with forecasting expected maintenance cost integrated with BIM.
- Established variable dependence between maintainability benchmarks and defects.
- Knowledge and evidence-based hybrid approach to building Bayesian belief networks integrated with BIM.
3.1.6. Smart Indoor Occupant Comfort Management: Real-time IoT Based Indoor Air Quality (IAQ) Monitoring Using 5G and Cloud Computing
- It utilizes IoT for efficient monitoring of real-time data.
- It also uses the adoption of cloud computing for real-time analysis of indoor air quality over a 5G network.
- A one-stop web-based portal to access real-time data from anywhere, anytime.
- An AI-powered analytics tool to find fault inside the existing system and propose a solution.
- An AI-powered automated system to trigger safety functions in the event of IAQ falling below dangerous levels.
3.1.7. Smart Space Management: BIM Integrated Suitable Activity-Based Workspaces Using Environmental Preferences
- Optimized space planning and utilization using data from IoT sensors.
- Enhanced energy efficiency and reduced energy wastage based on sensor data.
- Occupancy behavior model-based lighting and HVAC control.
- Improved user experience with better-customized space matching experience.
- BIM-based visualization and data integration.
- Demand-based facilities management and maintenance activities.
3.1.8. Smart Traffic Management: BIM-Based Smart Parking Management System
- Develop a 5G and IoT based parking system to effectively locate parking in metropolitan areas.
- Connect sensors monitor whether parking spaces are occupied, enabling drivers to use the app to see in real-time where they can park.
3.1.9. Smart Security Management: BIM-Computer Vision-Based Smart Security System
- Develop a computer vision and BIM-based smart security system to automatically prevent security threats and provide better location mapping of threats.
- Use the system to implement the crowd management system in the event of communicable disease outbreaks like COVID.
- Implement automated access control measures using computer vision to prevent security threats to high-security zones.
3.1.10. Smart Document, Code, and Transaction Management: Common Data Environment for Facilities Management
- Computer vision-based safety hazard identification.
- LIDAR/drone-based productivity enhancement and as-built BIM modeling with greater accuracy.
- AI and analytics-based anomaly detection techniques for FM.
- Product data templates for automated data capturing, analysis of whole life cycle, and progress payment during maintenance activities.
3.1.11. Smart Document, Code, and Transaction Management: Code Checking for Facilities Management
- Development of a BIM-based system for automated code compliance checking for FM stage.
- The system will be able to automatically check statutory compliance for various spaces.
- The system will help facilities managers to analyze building with building code requirements in post occupancy phase.
3.2. Education (Teaching and Industry Training)
3.2.1. 5G AR/VR/BIM Application for Facilities Management of Smart Building
- This is the first of a kind project in Singapore and ASEAN countries in construction education, which combines various technologies into a single solution for the customized need. There is no existing solution that combines AR/VR, BIM, 5G, cloud computing, and remote teaching/learning experiences using wireless on 5G.
- This is the first of a kind project which aims to study high data required BIM model upload and download requirements using 5G over the cloud and powered by cloud rendering to feed on wireless devices.
- Almost all the current VR BIM solutions require a wired network [105]. This is the first project where we want to conduct trials for AR/VR BIM for wireless solutions to delimit the boundaries of educational space and bring it home.
- This is the first a kind project which wants to expand the teaching and learning classroom size, up to 30, to avoid repetition of course content to small groups of students.
- This is the first of a kind research study aiming to amalgamate different verticals (construction space, education space, advanced technology space) into a new vertical, opening new research and development verticals in the future.
- Unlike other AR/VR projects, this project is being developed by a joint venture between a telecom partner, AR/VR developer, and buildings domain experts (Department of Building). The network is customized for an enhanced user experience that is not viable with other such development.
3.2.2. AR/VR/Digital Twin Application in Construction Lifecycle Using BIM
- Use AR/VR/digital twin application to teach students BIM usage in the entire life cycle.
- Increase learning motivation through intervention of new age technologies in BIM teaching.
- Transit BIM learning from BIM modeling to BIM application using advanced technologies.
3.2.3. Facilitating Industry in 5G Workforce Transformation
- Upskill in the areas of 5G network, cybersecurity, and solution engineering.
- Train workers to be future-ready before the commercial rollout of 5G.
- State of the art lab to support industry training. The lab is equipped with AR/VR devices, drones, high-power customized PCs, LIDAR equipment, servers, AR glasses, interactive whiteboards, robots, holographic projectors, a computer lab, etc.
- 5G-enabled smart facilities management
- 5G-enabled smart building and smart estates
- Managing 5G future built environment
3.3. Future Challenges for 5G Uses
- Many 5G use cases for SB and SFM are still evolving and under different stages of research experimentation and pilot-testing. Actual commercialization of 5G hardware products are still in progress and 5G devices such as 5G chipsets, 5G VR headsets, 5G laptops, 5G sensors, and 5G smart AR glasses are still under product development as 5G telecommunications protocol race for global standardization.
- Test-bedding of many 5G use cases are taking place within institutes of higher learning and largely limited within the physical boundaries of Singapore. Trans-boundary deployments and global scale 5G use cases are few, especially in developing ASEAN countries.
- Studies on cost-efficient 5G design, adoption, and implementation is necessary to attract any building developers and private industry to adopt any new expensive telecommunication protocol. Achieving the best cost-effective system to achieve optimal performance with minimal investment is a major concern for companies.
- Minimizing the energy consumption of energy-intensive 5G network infrastructures while incorporating alternative renewable energy sources into smart buildings is another concern to maintain low running costs and sustainability of operations.
- Preserving security of sensitive data is demanded in any connected environments. Building occupants must be convinced about the security of sensitive data, otherwise they will simply tend to avoid using ICT platform of smart buildings. Introducing stringent security measures is an essential necessity that requires further investigation.
- Integrating heterogeneous devices and proprietary software applications is necessary to fully exploit the benefits of open source platforms and devices, so as to ensure universal inter-operability and allow different devices to inter-communicate with each other without restrictions. Universal accessibility entails removing incompatibilities between multiple operational platforms and monopolistic protectionism at all levels of 5G implementation.
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Su, K.; Li, J.; Fu, H. Smart city and the applications. In 2011 International Conference on Electronics, Communications and Control (ICECC); IEEE: Ningbo, China, 2011; pp. 1028–1031. [Google Scholar]
- Wong, J.K.; Li, H. Development of intelligence analytic models for integrated building management systems (IBMS) in intelligent buildings. Intell. Build. Int. 2009, 1, 5–22. [Google Scholar] [CrossRef]
- Recast, E. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast). Off. J. Eur. Union 2010, 18, 2010. [Google Scholar]
- Ghaffarianhoseini, A.; AlWaer, H.; Clements-Croome, D.; Berardi, U.; Raahemifar, K.; Tookey, J. Intelligent or smart cities and buildings: A critical exposition and a way forward. Intell. Build. Int. 2018, 10, 122–129. [Google Scholar] [CrossRef]
- Albino, V.; Berardi, U.; Dangelico, M.R. Smart cities: Definitions, dimensions, performance, and initiatives. J. Urban Technol. 2015, 22, 3–21. [Google Scholar] [CrossRef]
- Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart grid—The new and improved power grid: A survey. IEEE Commun. Surv. Tutor. 2011, 14, 944–980. [Google Scholar] [CrossRef]
- Depuru, S.S.S.R.; Wang, L.; Devabhaktuni, V.; Gudi, N. Smart meters for power grid—Challenges, issues, advantages and status. In Proceedings of the 2011 IEEE/PES Power Systems Conference and Exposition, Phoenix, AZ, USA, 20–23 March 2011. [Google Scholar]
- Xu, Z.; Guan, X.; Jia, Q.S.; Wu, J.; Wang, D.; Chen, S. Performance analysis and comparison on energy storage devices for smart building energy management. IEEE Trans. Smart Grid 2012, 3, 2136–2147. [Google Scholar] [CrossRef]
- Lê, Q.; Nguyen, H.B.; Barnett, T. Barnett, Smart homes for older people: Positive aging in a digital world. Future Internet 2012, 4, 607–617. [Google Scholar] [CrossRef]
- Kiliccote, S.; Piette, M.A.; Ghatikar, G. Smart buildings and demand response. In AIP Conference Proceedings; American Institute of Physics: College Park, MD, USA, 2011. [Google Scholar]
- Cook, D.J.; Das, S.K. How smart are our environments? An updated look at the state of the art. Pervasive Mob. Comput. 2007, 3, 53–73. [Google Scholar] [CrossRef]
- McGlinn, K.; O′Neill, E.; Gibney, A.; O′Sullivan, D.; Lewis, D. SimCon: A Tool to Support Rapid Evaluation of Smart Building Application Design using Context Simulation and Virtual Reality. J. UCS 2010, 16, 1992–2018. [Google Scholar]
- Sinopoli, J.M. Smart Buildings Systems for Architects, Owners and Builders; Butterworth-Heinemann: Oxford, UK, 2009. [Google Scholar]
- Buckman, A.H.; Mayfield, M.; Beck, S.B. What is a smart building? Smart Sustain. Built Environ. 2014, 3, 92–109. [Google Scholar] [CrossRef]
- GCRN—Great Central Railway, Nottingham. Cost Analysis of Inadequate Interoperability in the US Capital Facilities Industry; National Institute of Standards and Technology (NIST): Gaithersburg, MD, USA, 2004; pp. 223–253.
- Khajavi, S.H.; Motlagh, N.H.; Jaribion, A.; Werner, L.C.; Holmström, J. Digital twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access 2019, 7, 147406–147419. [Google Scholar] [CrossRef]
- Cheng, J.C.; Chen, W.; Chen, K.; Wang, Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Autom. Constr. 2020, 112, 103087. [Google Scholar] [CrossRef]
- Du, J.; Zou, Z.; Shi, Y.; Zhao, D. Zero latency: Real-time synchronization of BIM data in virtual reality for collaborative decision-making. Autom. Constr. 2018, 85, 51–64. [Google Scholar] [CrossRef]
- Kumar, V.; Teo, E.A.L. Conceptualizing “COBieEvaluator”: A rule based system for tracking asset changes using COBie datasheets. Eng. Constr. Archit. Manag. 2020, 27, 1093–1118. [Google Scholar] [CrossRef]
- Kumar, V.; Teo, E.A.L. Conceptualizing “COBieEvaluator”: An Application for Data Mining COBie Datasets to Track Asset Changes Throughout Project Lifecycle. In Proceedings of the International Congress and Conferences on Computational Design and Engineering 2019 (I3CDE 2019), Penang, Malaysia, 7–10 July 2019. [Google Scholar]
- Chew, M.Y.L. Maintainabillity of Facilities-Green FM for Building Professionals, 2nd ed.; World Scientific: Singapore, 2016. [Google Scholar]
- Kumar, V.; Teo, E.A.L. Development of a rule-based system to enhance the data consistency and usability of COBie datasheets. J. Comput. Des. Eng. in press.
- Huawei. Cloud VR Solution White Paper; Huawei: Shenzhen, Guangdong, 2018. [Google Scholar]
- Zhang, Y.; Liu, H.; Kang, S.C.; Al-Hussein, M. Virtual reality applications for the built environment: Research trends and opportunities. Autom. Constr. 2020, 118, 103311. [Google Scholar] [CrossRef]
- Newman, L.H. 5G Is More Secure than 4G and 3G—Except When It’s Not. 2019. Available online: https://www.wired.com/story/5g-more-secure-4g-except-when-not/ (accessed on 1 November 2020).
- Vella, H. 5G vs. 4G: What Is the Difference? 2019. Available online: https://www.raconteur.net/technology/5g/4g-vs-5g-mobile-technology/ (accessed on 1 November 2020).
- T-mobile. How The 5G Era Could Help Build A More Sustainable Future. 2019. Available online: https://www.forbes.com/sites/tmobile/2019/10/21/how-the-5g-era-could-help-build-a-more-sustainable-future/?sh=5a400ee3664f (accessed on 24 November 2020).
- Facilio. Smart Facility Management Is The Future. 2019. Available online: https://www.iotforall.com/iot-driven-facility-management-future (accessed on 4 November 2020).
- Tucker, M.; Smith, A. User perceptions in workplace productivity and strategic FM delivery. Facilities 2008, 26, 196–212. [Google Scholar] [CrossRef] [Green Version]
- Lau, D.; Liu, J.; Majumdar, S.; Nandy, B.; St-Hilaire, M.; Yang, C.S. A cloud-based approach for smart facilities management. In 2013 IEEE Conference on Prognostics and Health Management (PHM); IEEE: Piscataway, NJ, USA, 2013. [Google Scholar]
- Fairchild, A. Twenty-first-century smart facilities management: Ambient networking in intelligent office buildings. In Guide to Ambient Intelligence in the IoT Environment; Springer: Berlin/Heidelberg, Germany, 2019; pp. 271–289. [Google Scholar]
- Alsamhi, S.H.; Ma, O.; Ansari, M.S.; Almalki, F.A. Survey on collaborative smart drones and internet of things for improving smartness of smart cities. IEEE Access 2019, 7, 128125–128152. [Google Scholar] [CrossRef]
- Katonaa, A.; Panfilov, P. Building predictive maintenance framwork for smart environment applicaion systems. Ann. DAAAM Proc. 2018, 29, 0460–0470. [Google Scholar]
- Armstrong, P.; Brambley, M.R.; Pratt, R.G.; Chassin, D.P. Building Controls and Facilities Management in the 21st Century. 2000. Available online: https://www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel7_Paper04.pdf (accessed on 3 November 2020).
- Qualcomm. Everything You Need to Know about 5G. 2020. Available online: https://www.qualcomm.com/invention/5g/what-is-5g (accessed on 3 November 2020).
- Wild, T.; Schaich, F.; Chen, Y. 5G air interface design based on universal filtered (UF-) OFDM. In 2014 19th International Conference on Digital Signal Processing; IEEE: Hong Kong, China, 2014. [Google Scholar]
- Bhushan, N.; Ji, T.; Koymen, O.; Smee, J.; Soriaga, J.; Subramanian, S.; Wei, Y. 5G air interface system design principles. IEEE Wirel. Commun. Mag. 2017, 24, 6–8. [Google Scholar] [CrossRef]
- Qualcomm. Making 5G NR a Reality: Leading the Technology Inventions for a Unified, More Capable 5G Air Interface, White Paper; Qualcomm: San Diego, CA, USA, 2016. [Google Scholar]
- Segan, S. What Is 5G? 2020. Available online: https://sea.pcmag.com/cell-phone-service-providers-products/15385/what-is-5g (accessed on 3 November 2020).
- GSMA Intelligence. Understanding 5G: Perspectives on Future Technological Advancements in Mobile, White paper; GSMA Intelligence: London, UK, 2014; pp. 1–26. [Google Scholar]
- Thales. What Is 5G? 2020. Available online: https://www.thalesgroup.com/en/markets/digital-identity-and-security/mobile/inspired/5G (accessed on 3 November 2020).
- Jain, A. Is 5G Network Slicing the Missing Link for Widespread Adoption of AR? 2020. Available online: https://www.rcrwireless.com/20200714/opinion/readerforum/is-5g-network-slicing-the-missing-link-for-widespread-adoption-of-ar-reader-forum (accessed on 3 November 2020).
- Qiao, X.; Ren, P.; Dustdar, S.; Liu, L.; Ma, H.; Chen, J. Web AR: A promising future for mobile augmented reality—State of the art, challenges, and insights. Proc. IEEE 2019, 107, 651–666. [Google Scholar] [CrossRef]
- Alcatel-Lucent. Mobility for Enterprise: Why Wi-Fi is the Right Choice. 2019. Available online: https://www.al-enterprise.com/-/media/assets/internet/documents/wifi-vs-5g-whitepaper-en.pdf (accessed on 3 November 2020).
- Horwitz, J. Siggraph 2019 Showed Why Wireless VR All But Demands 5G or Wi-Fi 6. 2019. Available online: https://venturebeat.com/2019/08/02/siggraph-2019-showed-why-wireless-vr-all-but-demands-5g-or-wi-fi-6/ (accessed on 3 October 2020).
- Scott. What’s the Point of 5G? A Virtual Reality Example. 2020. Available online: https://wellthatsinteresting.tech/what-is-5g-virtual-reality-example/ (accessed on 3 October 2020).
- Xu, L. Cloud and 5G Bring the Reality to AR and VR. 2019. Available online: https://www.huawei.com/en/publications/winwin-magazine/33/cloud-and-59-bring-the-reality-to-arvr (accessed on 5 October 2020).
- Huawei iLab. Cloud VR Bearer Networks; Huawei iLab: Shenzhen, China, 2017. [Google Scholar]
- West, D.M. Achieving Sustainability in a 5G World; The Brookings Institution: Washington, DC, USA, 2016. [Google Scholar]
- Nokia. Build a More Sustainable Future with 5G. 2020. Available online: https://www.nokia.com/networks/5g/building-sustainable-5g/ (accessed on 24 November 2020).
- Pickup, O. Can 5G Really Be Sustainable? 2020. Available online: https://www.raconteur.net/technology/5g/5g-environmental-impact/ (accessed on 20 November 2020).
- Gabriel, C.; Chern, A. Green 5G: Building a Sustainable World; A.M. Limited: London, UK, 2020. [Google Scholar]
- Vermesan, O.; Friess, P.; Guillemin, P.; Gusmeroli, S.; Sundmaeker, H.; Bassi, A.; Jubert, I.S.; Mazura, M.; Harrison, M.; Eisenhauer, M.; et al. Internet of things strategic research roadmap. Internet Things-Global Technol. Soc. Trends 2011, 1, 9–52. [Google Scholar]
- Kumar, S.; Livermont, G.; Mckewan, G. Stage implementation of RFID in hospitals. Technol. Health Care 2010, 18, 31–46. [Google Scholar] [CrossRef] [PubMed]
- Rousek, J.B.; Pasupathy, K.; Gannon, D.; Hallbeck, S. Asset management in healthcare: Evaluation of RFID. IIE Trans. Healthc. Syst. Eng. 2014, 4, 144–155. [Google Scholar] [CrossRef]
- Gubbi, J. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef] [Green Version]
- Li, N.; Becerik-Gerber, B. Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Adv. Eng. Inform. 2011, 25, 535–546. [Google Scholar] [CrossRef]
- Araya, D.B.; Grolinger, K.; ElYamany, H.F.; Capretz, M.A.; Bitsuamlak, G. An ensemble learning framework for anomaly detection in building energy consumption. Energy Build. 2017, 144, 191–206. [Google Scholar] [CrossRef]
- Dong, B.; Lam, K.P. Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. J. Build. Perform. Simul. 2011, 4, 359–369. [Google Scholar] [CrossRef]
- Iotswc. Advantages of 5G and How Will Benefit IOT. 2019. Available online: https://www.iotsworldcongress.com/advantatges-of-5g-and-how-will-benefit-iot/ (accessed on 4 November 2020).
- Mitchell, R.; Michalski, J.; Carbonell, T. An Artificial Intelligence Approach; Springer: Berlin, Germany, 2013. [Google Scholar]
- Rahwan, I.; Simari, G.R. Argumentation in Artificial Intelligence; Springer: Heidelberg, Germany, 2009; Volume 47. [Google Scholar]
- Nilsson, N.J. Principles of Artificial Intelligence; Morgan Kaufmann: Burlington, MA, USA, 2014. [Google Scholar]
- Dominguez, R.; Onieva, E.; Alonso, J.; Villagra, J.; Gonzalez, C. LIDAR based perception solution for autonomous vehicles. In 2011 11th International Conference on Intelligent Systems Design and Applications; IEEE: Cordoba, Spain, 2011. [Google Scholar]
- Qela, B.; Mouftah, H.T. Observe, learn, and adapt (OLA)—An algorithm for energy management in smart homes using wireless sensors and artificial intelligence. IEEE Trans. Smart Grid 2012, 3, 2262–2272. [Google Scholar] [CrossRef]
- Scupola, A. ICT adoption in facilities management supply chain: The case of Denmark. J. Glob. Inf. Technol. Manag. 2012, 15, 53–78. [Google Scholar] [CrossRef]
- Ko, C.-H. RFID, Web-based, and artificial intelligence integration in facilities management. In Proceedings of the International Symposium on Automation and Robotics in Construction, Taipei, Taiwan, 28 June–1 July 2017. [Google Scholar]
- Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. [Google Scholar] [CrossRef]
- Miller, S.A. System and Method for Augmented and Virtual Reality. U.S. Patent No. 9,215,293, 15 December 2015. [Google Scholar]
- Tobias, M. How Augmented Reality Can Improve Facility Management. 2019. Available online: https://www.ny-engineers.com/blog/how-augmented-reality-can-improve-facility-management (accessed on 4 November 2020).
- Berlin, R. Augmented Reality Is Shaping the Future of Facilities. 2017. Available online: https://www.facilitiesnet.com/powercommunication/tip/Augmented-Reality-is-Shaping-the-Future-of-Facilities--40319 (accessed on 31 October 2020).
- Carreira, P.; Castelo, T.; Gomes, C.C.; Ferreira, A.; Ribeiro, C.; Costa, A.A. Virtual reality as integration environments for facilities management: Application and user perception. Eng. Constr. Archit. Manag. 2018, 25, 90–112. [Google Scholar] [CrossRef]
- El Ammari, K.; Hammad, A. Remote interactive collaboration in facilities management using BIM-based mixed reality. Autom. Constr. 2019, 107, 102940. [Google Scholar] [CrossRef]
- Soria, G.; Ortega Alvarado, L.; Feito, F.R. Augmented and virtual reality for underground facilities management. J. Comput. Inf. Sci. Eng. 2018, 18, 041008–041017. [Google Scholar] [CrossRef]
- Lee, S.; Akin, Ö. Augmented reality-based computational fieldwork support for equipment operations and maintenance. Autom. Constr. 2011, 20, 338–352. [Google Scholar] [CrossRef]
- PaleBlue Corporate. Is the Future of VR/AR Relying on 5G? 2019. Available online: https://pale.blue/2019/12/16/is-the-future-of-vr-ar-relying-on-5g/ (accessed on 5 October 2020).
- ABI Research. Augmented and Virtual Reality: The First Wave of 5G Killer Apps; ABI Research: New York, NY, USA, 2017. [Google Scholar]
- Tao, F.; Zhang, H.; Liu, A.; Nee, A.Y. Digital twin in industry: State-of-the-art. IEEE Trans. Ind. Inform. 2018, 15, 2405–2415. [Google Scholar] [CrossRef]
- Tao, F.; Cheng, J.; Qi, Q.; Zhang, M.; Zhang, H.; Sui, F. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. [Google Scholar] [CrossRef]
- Kaur, M.J.; Mishra, V.P.; Maheshwari, P. The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action, in Digital Twin Technologies and Smart Cities; Springer: Cham, Switzerland, 2020; pp. 3–17. [Google Scholar]
- Dröder, K.; Bobka, P.; Germann, T.; Gabriel, F.; Dietrich, F. A machine learning-enhanced digital twin approach for human-robot-collaboration. Procedia Cirp 2018, 76, 187–192. [Google Scholar] [CrossRef]
- Qiuchen Lu, V.; Parlikad, A.K.; Woodall, P.; Ranasinghe, G.D.; Heaton, J. Developing a dynamic digital twin at a building level: Using Cambridge campus as case study. In International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data-Informed Decision-Making; ICE Publishing: Hongkong, China, 2019. [Google Scholar]
- Kang, S.; Koschan, A.; Abidi, B.; Abidi, M. Video surveillance of high security facilities. In Proceedings of the 10th International Conference on Robotics & Remote Systems for Hazardous Environments, Gainesville, FL, USA, 28–31 March 2004. [Google Scholar]
- Foroughi, H.; Aski, B.S.; Pourreza, H. Intelligent video surveillance for monitoring fall detection of elderly in home environments. In 2008 11th International Conference Importance of 5G Network on Computer and Information Technology; IEEE: Piscataway, NJ, USA, 2008. [Google Scholar]
- Regazzoni, C.S.; Cavallaro, A.; Wu, Y.; Konrad, J.; Hampapur, A. Video analytics for surveillance: Theory and practice [from the guest editors]. IEEE Signal Process. Mag. 2010, 27, 16–17. [Google Scholar] [CrossRef] [Green Version]
- Xu, L.-Q. Issues in video analytics and surveillance systems: Research/prototyping vs. applications/user requirements. In Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, London, UK, 5–7 September 2007. [Google Scholar]
- Zhang, Q.; Sun, H.; Wu, X.; Zhong, H. Edge video analytics for public safety: A review. Proc. IEEE 2019, 107, 1675–1696. [Google Scholar] [CrossRef]
- Nokia. Video Surveillance & Analytics. 2020. Available online: https://www.nokia.com/networks/5g/use-cases/video-surveillance/ (accessed on 29 October 2020).
- Verma, S.; Banik, G. Video Surveillance-as-a-Service: Next Generation CCTV Surveillance Leveraging 5G and AI. 2019. Available online: https://teletimesinternational.com/2020/video-surveillance-as-a-service/ (accessed on 30 November 2020).
- Nokia. SpaceTime Scene Analytics. 2020. Available online: https://www.nokia.com/networks/solutions/spacetime-scene-analytics/ (accessed on 30 November 2020).
- Workforce Singapore. Professional Conversion Programmes (PCP) for Individuals. 2020. Available online: https://www.wsg.gov.sg/programmes-and-initiatives/professional-conversion-programmes-individuals.html (accessed on 1 November 2020).
- Dasgupta, J. When 5G and Video Surveillance Met in a Smart City. 2020. Available online: https://www.nokia.com/blog/when-5g-and-video-surveillance-met-smart-city/ (accessed on 24 November 2020).
- Chen, K.; Reichard, G.; Xu, X. Opportunities for Applying Camera-Equipped Drones towards Performance Inspections of Building Facades. In Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience; American Society of Civil Engineers: Reston, VA, USA, 2019; pp. 113–120. [Google Scholar]
- Chew, M.Y.L.; Asmone, M.A.; Conejos, S. Design for Maintainability: Benchmarks for Quality Buildings; World Scientific: Singapore, 2018. [Google Scholar]
- Masiero, A.; Fissore, F.; Vettore, A. Low cost UAV and image classification for monitoring the deterioration on building façades. In Proceedings of the 4th Joint International Symposium on Deformation Monitoring, Athens, Greece, 15–17 May 2019. [Google Scholar]
- Mitchell, R. Drone Technology and 5G–How the Two Will Help Each Other. 2020. Available online: https://www.electropages.com/blog/2020/09/drone-technology-and-5g-how-two-will-help-each-other (accessed on 29 October 2020).
- Hoe, S.L. Defining a smart nation: The case of Singapore. J. Inf. Commun. Ethics Soc. 2016, 14, 323–333. [Google Scholar] [CrossRef]
- Chan, C.M.; Lau, Y.; Pan, S.L. E-government implementation: A macro analysis of Singapore’s e-government initiatives. Gov. Inf. Q. 2008, 25, 239–255. [Google Scholar] [CrossRef]
- Tan, C.W.; Pan, S.L. Managing e-transformation in the public sector: An e-government study of the Inland Revenue Authority of Singapore (IRAS). Eur. J. Inf. Syst. 2003, 12, 269–281. [Google Scholar] [CrossRef]
- Woo, J.J. Technology and Governance in Singapore’s Smart Nation Initiative. Ash Cent. Policy Briefs Ser. 2018. Available online: https://ash.harvard.edu/files/ash/files/282181_hvd_ash_paper_jj_woo.pdf (accessed on 29 October 2020).
- Teo, E.A.L. Creation of SMART Towns: An Experiential Learning Journey. In Proceedings of the CIB Congress, Hong Kong, China, 17–19 June 2019. [Google Scholar]
- Leow, A. Government to Invest $40m in 5G Innovation as a Start: Iswaran. 2019. Available online: https://www.businesstimes.com.sg/government-economy/government-to-invest-s40m-in-5g-innovation-as-a-start-iswaran (accessed on 23 November 2020).
- IMDA. Building a Robust Digital Economy, Embracing Our Digital Future; IMDA: Singapore, 2019.
- Dobberstein, N.; Venkataramani, H. 5G in ASEAN: Reigniting Growth in Enterprise and Consumer Markets; A.T. Kearney, Inc.: Chicago, IL, USA, 2019. [Google Scholar]
- Sampaio, A.Z. Enhancing BIM methodology with VR technology. In State of the Art Virtual Reality and Augmented Reality Knowhow; IntechOpen: London, UK, 2018; pp. 59–79. [Google Scholar]
No. | Function | Target Descriptions | Technical Requirements |
---|---|---|---|
1 | Smart energy management |
|
|
2 | Smart maintenance management |
|
|
3 | Smart indoor occupant comfort management |
|
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4 | Smart space management |
|
|
5 | Smart traffic management |
|
|
6 | Smart security management |
|
|
7 | Smart document code, and transaction management |
|
|
No. | Wi-Fi/4G (General Limitations) | 5G (Theoretical Benefits) |
---|---|---|
1 | Longer wait due to latency, lag, and the application getting stuck at regular intervals. | Low latency, high network speed, no lag. |
2 | Interference on Wi-Fi/4G connection. | No interference in 5G connection. |
3 | Connectivity issues (varied connection strength, intermittent disconnection). | Steady, consistent, and high-speed connection strength. |
4 | No end-to-end control. | Can be customized specifically to AR/VR end-to-end needs. |
5 | Less scalable to high device requirements. | Scalable to high devices requirements. |
6 | Unavailability of a consistent high-speed network for remote learning. Fixed to space. | Network speed can be available for all locations with consistency. Does not have to be confined to a pre-determined space. |
7 | Inconsistent speed leads to high-end PC requirements in VR for local rendering. | High speed enables the use of all in one wireless device (lightweight with mobility) and replace PC with a cloud server. |
8 | No efficient method for locating and resolving Wi-Fi performance issues. | Can be customized for VR experience. |
9 | Signal interference, signal attenuation, and mutual influence of services. | No issues in 5G connection. |
No. | Country | % |
---|---|---|
1 | Singapore | 56.89 |
2 | Malaysia | 39.81 |
3 | Thailand | 33.00 |
4 | Indonesia | 27.24 |
5 | Brunei | 17.10 |
6 | Myanmar | 16.99 |
7 | Philippines | 14.77 |
8 | Vietnam | 6.31 |
9 | Laos | 5.44 |
10 | Cambodia | 3.18 |
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Chew, M.Y.L.; Teo, E.A.L.; Shah, K.W.; Kumar, V.; Hussein, G.F. Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore. Sustainability 2020, 12, 10259. https://doi.org/10.3390/su122410259
Chew MYL, Teo EAL, Shah KW, Kumar V, Hussein GF. Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore. Sustainability. 2020; 12(24):10259. https://doi.org/10.3390/su122410259
Chicago/Turabian StyleChew, Michael Yit Lin, Evelyn Ai Lin Teo, Kwok Wei Shah, Vishal Kumar, and Ghassan Fahem Hussein. 2020. "Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore" Sustainability 12, no. 24: 10259. https://doi.org/10.3390/su122410259
APA StyleChew, M. Y. L., Teo, E. A. L., Shah, K. W., Kumar, V., & Hussein, G. F. (2020). Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore. Sustainability, 12(24), 10259. https://doi.org/10.3390/su122410259