Mobile Cloud Computing: Issues, Applications and Scope in COVID-19
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- Hariket Sukesh Kumar Sheth ORCID: orcid.org/0000-0001-5283-7716 15 &
- Amit Kumar Tyagi ORCID: orcid.org/0000-0003-2657-8700 16
Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))
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As the world is transitioning into a tech-savvy era, the twenty-first century is evidence of many technological advancements in the field of AI, IoT, ML, etc. Mobile Cloud computing (MCC) is one such emerging technology, providing services regardless of the time and place, contours the limitations of mobile devices to process bulk data, providing multi-platform support and dynamic provisioning. Not only there is an enhancement in computation speed, energy efficiency, execution, integration, but also incorporates considerate issues in terms of client-to-cloud and cloud-to-client authentication, privacy, trust, and security. Reviewing and overcoming addressed concerns is essential to provide reliable yet efficient service in nearing future. Mobile Cloud Computing has the potential to bring wonders in the fields such as education, medical science, biometry, forensics, and automobiles, which could counter the challenges faced in the ongoing COVID-19 Pandemic. To combat the prevailing challenges due to COVID-19, it has become critical that more efficient and specialized technologies like Mobile Cloud Computing are accepted that enable appropriate reach and delivery of vital services by involving gamification, cloud rendering, and collaborative practices. This paper provides a detailed study about MCC, mitigated security and deployment attacks, issues, applications of MCC, providing developers and practitioners opportunities for future enhancements.
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Acknowledgement
We thank our college, Vellore Institute of Technology, Chennai for their constant support, encouragement and support. The authors have properly acknowledged and cited the scholars whose articles or content were consulted for this manuscript. The authors extend their gratitude to authors/editors/publishers of all those articles, journals and books. All the diagrams/figures/illustrations used and added in the paper are made using Open-Source software, ensuring no copyright issues.
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Sheth, H.S.K., Tyagi, A.K. (2022). Mobile Cloud Computing: Issues, Applications and Scope in COVID-19. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_55
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- DOI: 10.1007/s11227-024-06395-0
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The role of 6g technologies in advancing smart city applications: opportunities and challenges.
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1. Introduction
- Literature Survey and Review Process
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- Through this article, we have extensively covered in detail the potential 6G technologies in terms of requirements, architecture, visions, and usage, which are anticipated to be integrated in futuristic 6G-enabled smart cities.
- Secondly, we have discussed prominent smart city applications with underlying 6G technologies. This includes smart waste management, smart healthcare, smart grids, and more.
- Thirdly, potential challenges are also highlighted along with discussion on each technology and application. Also, at the end of this survey paper, challenges and suggestions for possible future research directions are also highlighted for the 6G-enabled smart city paradigm.
- Research Objectives
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2. Potential 6G Enabling Technologies
2.1. role of ai in 6g and smart city arena, 2.1.1. applications, 2.1.2. challenges, 2.2. role of integrated sensing and communication (isac) in smart city concept, 2.2.1. applications, 2.2.2. challenges, 2.3. iot for smart cities with 6g, 2.3.1. characteristics of 6g-iot, 2.3.2. classification of iot, 2.4. blockchain (bc) and 6g-enabled smart cities, 2.5. terahertz (thz) communication, 2.5.1. applications/use cases, 2.5.2. challenges, 2.6. quantum communication (qc), 2.6.1. applications, 2.6.2. challenges, 2.7. immersive communication (ic), 2.7.1. types of immersive communication, 2.7.2. use cases for immersive communication, 2.8. visible light communication (vlc), 2.8.1. free-space optics (fso), 2.8.2. fiber-wireless system (fiwi), 2.8.3. power over fiber (pof), 2.8.4. challenges, 2.9. mobile edge computing (mec), applications, 2.10. reconfigurable intelligent surfaces (riss), 2.11. non-terrestrial networks (ntns), 2.11.1. airborne base stations (abs), uavs, and drones uses in a 6g smart city, applications/benefits, 2.11.2. satellite communication, 3. applications of 6g in smart cities, 3.1. industrial automation and smart manufacturing, 3.2. vehicle-to-everything (v2x) technology in smart cities, use cases of v2x, 3.3. smart healthcare, 3.4. smart grid, 3.5. smart waste management, 4. conclusions, open challenges and possible future research, conflicts of interest.
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Click here to enlarge figure
Parameter | 5G | 6G |
---|---|---|
Data Rate, Band | ~20 Gbps, sub-6 GHz, Crowded | ~1 TBPS, ultra-fast (THz) |
Services | Limited capability to support new communication | Holographic communication, augmented reality, immersive gaming, etc. |
Latency | Low latency | Ultra-low latency and high reliability |
Architecture | Massive MIMO | Cell-free massive MIMO, intelligent surfaces |
Coverage | Infrastructure-based | Ubiquitous connectivity (space–air–ground–sea) |
Security | Security issues | Blockchain and quantum communication. |
AI Integration | Partial | Full |
Satellite Integration | No | Full |
Source Databases | IEEE Xplore, Web of Science (WoS), Taylor and Francis, ASCE Library, Scopus, and Springer |
---|---|
Search String | (“Artificial Intelligence” OR “THz” OR “ISAC” OR “Block Chain” OR “UAV”) AND (“6G”) AND (“Smart Cities”) |
Time period | 2019–2024 |
Article Type | Journal, Review, Letter, Book Chapter, Short Survey, Article |
Language Restriction | English |
Included Subject Area | Computer Science, Engineering, Energy, Business, Management and Accounting, Mathematics, Environmental Science, Decision Sciences |
Excluded Subject Area | Chemical Engineering, Arts and Humanities, Health Professions, Agricultural and Biological Sciences, Neuroscience, Multidisciplinary, Psychology, Pharmacology, Toxicology and Pharmaceutics, Immunology and Microbiology, Nursing, Social Sciences, Economics Econometrics and Finance, Physics and Astronomy, Materials Science, Medicine, Biochemistry, Genetics and Molecular Biology, Chemistry, Earth and Planetary Sciences |
Ref. | Authors | Year of Public. | Research Area | Major Contribution |
---|---|---|---|---|
[ ] | Fong, B et al. | 2023 | Vehicular | Investigates technical issues regarding the design and implementation of vehicle-to-infrastructure (V2I) systems to enhance reliability in a smart city with 6G as backbone. |
[ ] | P Mishra et al. | 2023 | IoT, Vision | Proposes framework, architecture and requirements for 6G IoT network. Discusses emerging technologies for 6G concerning artificial intelligence/machine learning, sensing networks, spectrum bands, and security. |
[ ] | Nahid Parvaresh, Burak Kantarci, | 2023 | UAV base station | Network performance of UAV-BS is improved by use of proposed continuous actor-critic deep reinforcement learning method to address the 3D location optimization issue of UAV-BSs in smart cities. |
[ ] | Z. Yang et al. | 2023 | Edge cloud, Energy efficiency | Paper analyzes challenges in developing a low-carbon smart city in 6G-enabled smart cities. Also proposes a visual end-edge-cloud architecture (E C) that is AI-driven for attaining low carbon emission in smart cities. |
[ ] | N. Sehito et al. | 2024 | IRS, UAV, NOMA, Spectral efficiency | Paper introduces a new optimization scheme by utilizing IRSs in NOMA multi-UAV networks in 6G-enabled smart cities, resulting in significant performance enhancement in terms of spectral efficiency. |
[ ] | Prabhat Ranjan Singh et al. | 2023 | AI, Technology evolution, Smart city applications | Paper covers evolution of network technology, AI approaches for 6G systems, importance of AI in advanced network model development in 6G-enabled smart city applications. |
[ ] | Murroni, M et al. | 2023 | Vision, Enabling technologies | Paper furnishes an update on the smart city arena with the use of 6G. Paper describes the role of enabling technologies and their specific employment plans. |
[ ] | Kamruzzaman | 2022 | IoT, Energy efficiency, Use cases | Presents key technologies, their applications, and IoT technologies trends for energy-efficient 6G-enabled smart city. Also, identifies and discusses key enabling technologies. |
[ ] | Kim, N et al. | 2024 | Standardization and key enabling technologies | Paper provides key features and recent trends in standardization of smart city concept. Paper highlights potential key technologies of 6G that can be used in various urban use cases in 6G-enabled smart cities. |
[ ] | Ismail, L.; Buyya, R | 2022 | AI-enabled 6G smart cities | Discusses evolution of wireless-technology generations, AI implementation in 6G and its self-learning models in smart city applications. |
[ ] | Zakria Qadir et al. | 2023 | Survey, IoT | Emerging 6G connectivity solutions and their applications in IoT to serve smart cities are surveyed in this paper. |
[ ] | Misbah Shafi et al. | 2024 | 6G technologies | The framework of 6G network is presented with its key technologies that have substantial effect on the key performance indicators of a wireless communication network. |
Natural Resources and Energy | Mobility and Transport | Living and Environment | People and Economy | Government |
---|---|---|---|---|
Smart Grid. | People Mobility. | Pollution Control. | Education and School. | e-Governance. |
Public Lighting. | City Logistics. | Public Safety. | Entertainment and Culture. | Transparency. |
Waste Management. | Health Care. | Entrepreneurship and Innovation. | ||
Water Management | Public Spaces | |||
Welfare Services. | ||||
Smart Homes. |
Ref. | THz | AI | BC | QC | NTN (UAV) | MEC | RIS | ISAC | HC | VLC |
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This Paper | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Potential 6G Technology | Brief Description |
---|---|
Artificial Intelligence (AI) | AI can be used to analyze, manage and optimize resources and to efficiently support 6G networks. AI can be used for tasks like efficient channel estimation, energy efficiency, modulation recognition, data caching, traffic prediction, radio resource management, mobility management, etc. |
Terahertz Communication (THz) | Uses frequency band 0.1 to 10 THz. Ability to attain ultra-high (up to 1 Tbps) data rates and wide bandwidth. |
Blockchain (BC) | A type of distributed ledger technology to ensure safety, privacy, scalability and reliability in this complex heterogeneous architecture. |
Quantum Computing (QC) | Based on quantum no-cloning theorem and the principle of uncertainty, absolute randomness is introduced by the use of the quantum nature of information, which provides security and enhanced channel capacity. |
Non Terrestrial networks (NTN) | Includes drones and satellites and is used to extend coverage footprint of terrestrial base stations, provide additional capacity in dense urban hotspots. Used in disaster recovery and remote or rural areas. |
Mobile Edge Communication (MEC) | By placing computing resources closer to end user, it reduces delays and latency and enhances processing speed and on-premise security |
Integrated Sensing and Communication (ISAC) | Optimizes the allocation of scarce resources and contributes to better decision-making processes by combining both sensing and communication tasks, which enhances efficiency. |
Reconfigurable Intelligent Surfaces (RISs) | A planar surface with array of passive elements whose characteristics can be altered dynamically. Used in 6G-THz to improve coverage, NLOS scenarios. |
Holographic Communication (HC) | HC is an application used in transmitting human-sized immersive and interactive holograms consisting of 3D videos and images that require extremely high data rates with ultra-low latency. |
Visible Light Communication (VLC) | VLC offers numerous advantages, such as, energy efficiency, cost-effectiveness, un-licensed spectrum, no electromagnetic interference, secure access technology, and large bandwidth. |
Ref. | Year | Application Domain of Smart Cities | Technologies Used | Areas/Topics Covered |
---|---|---|---|---|
[ ] | 2024 | V2X | 6G, Blockchain, Federated learning, Fog Computing | Comprehensive V2X security analysis. Future research direction for privacy in XR, secure SDN, physical layer security in THz. |
[ ] | 2024 | Smart Traffic Management | Edge Computing, Blockchain, Reinforced learning | Traffic optimization is achieved by decentralized integration of IoT sensors on vehicles and traffic signals and edge devices and the use of BC rules for real-time decisions. |
[ ] | 2024 | Supply Chain Management | Blockchain, IoT, Edge Computing | A Blockchain-based and IoT-enabled transparent and secure supply chain management framework is proposed for public emergency services in smart cities. |
[ ] | 2023 | Intelligent Transport System (ITS) | Blockchain | An ITS cross-domain data interaction framework between devices and agencies is proposed to achieve secure and efficient cross-chain communication. |
[ ] | 2023 | IoT | Blockchain, Big Data, AI | Framework and architecture based on Blockchain, AI and Big Data. |
[ ] | 2023 | Industrial Applications | 6G, Blockchain, IoT | Case study of smart supply chain. Benefits and challenges of BT and 6G-IoT |
[ ] | 2023 | IoD (Internet of Drones) | 6G, Blockchain | Analysis of multilayered Blockchain-IoD novel Global Compliance System (GCoS) and Swarm Security (Sse) system |
[ ] | 2023 | IoT-Blockchain efficiency | 6G, IoT-oriented Blockchain | Improves Blockchain-IoT performance by targeted optimization to improve low power efficiency and slow ledger synchronization. |
[ ] | 2022 | IoV | 6G, Blockchain | A survey paper for BC in IoVs sharing underlying 6G technology. Explores how privacy and security issues in IoVs can be tackled using BC technology. |
[ ] | 2022 | Food Supply Chain Management | IoT, Blockchain | Blockchain enables traceability of food supply from factories/fields to the customer’s table. IoT devices probe food condition. |
Use Case | Description |
---|---|
Remote Surgery | ]. ]. ]. |
Holographic Teleconferencing | ]. ]. ]. |
Immersive Gaming | ]. |
Metaverse | ]. |
Tech. | Applications/Benefits | Challenges |
---|---|---|
AI | ]. , ]. , , , ]. ]. ] ]. ]. , , , , , , , , , , , ]. ]. ]. | , , ]. |
ISAC | , ]. ]. | ]. ]. ]. |
THz | , ]. , ]. ] | ]. ]. ]. |
BC | , ]. ]. ]. ] | |
QC | , , ]. , ]. , ]. , , ] | ]. , ]. |
NTN | , ]. | |
MEC | , ]. ]. | |
RIS | ]. ] ] and high-precision positioning [ , ]. ] | |
IC | , , ]. , ]. ]. ]. | |
VLC |
Application (Use Case) | Benefits | Devices/Tech Used |
---|---|---|
Smart Routing | Avoidance of traffic congestion. Useful for emergency vehicles. Traffic balancing on roads. Reduction in emissions [ ] Reduce delays. | IOT sensors. Vehicle ad-hoc networks. AI real-time routing algorithms [ ]. Cloud and edge computing for data processing and analysis. |
Smart Parking | Contribution to sustainability. Optimal utilization of parking spaces. Reduced time for drivers to search for parking spaces. | V2V and V2I communication. Use of sensors for indicating parking status. AI and cloud computing. |
Speed Harmonization | Reduces frequent need for acceleration and deceleration. Continuous traffic flow. Reduces emissions. Safe travel. | AI and cloudification. Green-light coordination. |
Green Driving | Reduction of fuel consumption. Reduction of pollution near critical areas like hospitals. | Collection of pollution data by roadside sensors. Data transfer to centralized cloud. Traffic management decision based on AI algorithm. On-road displays for flashing traffic management decisions. |
Coordinated Maneuvers | Smooth traffic flow. Emission reduction. | V2I information exchange among vehicles and RSU [ ]. Low-latency, low-delay transmission. Advanced AI implemented at edge for delay-free decisions. |
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Share and Cite
Sharma, S.; Popli, R.; Singh, S.; Chhabra, G.; Saini, G.S.; Singh, M.; Sandhu, A.; Sharma, A.; Kumar, R. The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges. Sustainability 2024 , 16 , 7039. https://doi.org/10.3390/su16167039
Sharma S, Popli R, Singh S, Chhabra G, Saini GS, Singh M, Sandhu A, Sharma A, Kumar R. The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges. Sustainability . 2024; 16(16):7039. https://doi.org/10.3390/su16167039
Sharma, Sanjeev, Renu Popli, Sajjan Singh, Gunjan Chhabra, Gurpreet Singh Saini, Maninder Singh, Archana Sandhu, Ashutosh Sharma, and Rajeev Kumar. 2024. "The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges" Sustainability 16, no. 16: 7039. https://doi.org/10.3390/su16167039
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Green computing and artificial intelligence safety will be among the focus areas of a new AI research and innovation centre, which is expected to be set up at the NUS School of Computing by 2025.
The proposed centre, a collaboration between IBM and NUS, aims to accelerate scientific research here by tapping the American tech giant’s full-stack AI infrastructure.
Minister for Digital Development and Information Josephine Teo announced the new centre at the IBM Think 2024 event held at the Sands Expo and Convention Centre on Aug 15.
Mrs Teo described IBM as a longstanding partner of the Republic in the area of AI, with many of the firm’s efforts serving the public good.
This will mark the first time IBM’s full-stack AI infrastructure system – the entire spectrum of software and technology required to build, test and deploy an application – is installed on a university campus in the Asia-Pacific region.
The AI-optimised computing infrastructure will operate on watsonx, a data and AI platform developed by IBM, as well as the company’s Red Hat hybrid cloud platform.
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Government agencies and companies, as well as academic and research institutions, will be able to take advantage of the new centre to conduct cutting-edge AI research that can benefit people, NUS and IBM said in a statement.
“The proposed collaboration would leverage NUS’ expertise to drive technological progress in AI, enabling more powerful, efficient and versatile AI systems that can tackle increasingly complex tasks,” they said.
The centre will take a sustainability-focused open innovation approach to developing AI technologies – incorporating ideas from both external and internal sources – which has greater potential to improve the quality and pace of adoption of new AI technologies, NUS and IBM added.
They hope to work together to develop tools and methodologies to help build trust in AI.
“IBM and NUS share a common goal to enable innovations in AI and sustainable computing, and we look forward to furthering this collaboration,” said IBM Research hybrid cloud and AI platform vice-president Priya Nagpurkar.
Professor Liu Bin, NUS’ deputy president for research and technology, said the university was “very excited” about the opportunity to collaborate with IBM.
“Building on the new NUS AI Research Institute announced earlier this year and the university’s commitment to green computing and sustainability, we aim to be the leading force in addressing rising industry demand for AI intelligence, cultivating a robust talent pool and contributing to Singapore’s decarbonisation efforts,” she said.
As part of efforts to grow local deep-tech start-ups, the collaboration will also allow the NUS Graduate Research Innovation Programme (Grip), together with local start-ups as well as small and medium-sized enterprises, to access technologies such as IBM’s watsonx platform and Red Hat OpenShift AI.
Grip is an initiative to help launch start-ups stemming from university research.
IBM and NUS did not say how much money was being invested in the new centre.
It is separate from the NUS AI Institute, launched in March, which conducts research on addressing ethical concerns associated with AI as well as the technology’s application across areas such as education and healthcare.
IBM was one of the university’s collaborators in the NUS AI Institute.
NUS said that while the two initiatives are separate, it expects “a lot of synergy” in the research efforts of the NUS AI Institute and the new centre.
Mrs Teo, who is also Minister-in-charge of Smart Nation and Cybersecurity, described the project as an example of how Singapore’s AI ecosystem is “steadily building up”.
The Government appreciates the potential for AI to serve the public good, she added.
She pointed to Readliao, an AI-enabled tool developed by Open Government Products – an independent division of the Government Technology Agency – which provides the elderly with simplified summaries of letters from the authorities, so they can better understand them.
“We will continue to bring together industry, government and academia for meaningful partnerships that we can all benefit from,” she said.
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