| 1. |
- Boobalan, Parimala, et al.
(författare)
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Fusion of Federated Learning and Industrial Internet of Things : A survey
- 2022
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Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 212
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Tidskriftsartikel (refereegranskat)abstract
- Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, Federated Learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributed machine learning and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning (ML), deep learning (DL) and blockchain techniques for FL in secure IIoT. Further we analyze and summarize several ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in automotive, robotics, agriculture, energy, and healthcare industries. Finally, we shed light on challenges, some possible solutions and potential directions for future research.
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| 2. |
- Javed, Abdul Rehman, et al.
(författare)
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A Survey of Explainable Artificial Intelligence for Smart Cities
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities
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| 3. |
- Pandya, Sharnil, et al.
(författare)
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A Study of the Impacts of Air Pollution on the Agricultural Community and Yield Crops (Indian Context)
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:20
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Tidskriftsartikel (refereegranskat)abstract
- Air pollution has been an vital issue throughout the 21st century, and has also significantly impacted the agricultural community, especially farmers and yield crops. This work aims to review air-pollution research to understand its impacts on the agricultural community and yield crops, specifically in developing countries, such as India. The present work highlights various aspects of agricultural damage caused by the impacts of air pollution. Furthermore, in the undertaken study, a rigorous and detailed discussion of state-wise and city-wise yield-crop losses caused by air pollution in India and its impacts has been performed. To represent air-pollution impacts, the color-coding-based AQI (Air Quality Index) risk-classification metrics have been used to represent AQI variations in India's agrarian states and cities. Finally, recent impacts of air pollution concerning AQI variations for May 2019 to February 2020, Seasonal AQI variations, impacts of PM2.5 , and PM10 in various agrarian states and India cities are presented using various tabular and graphical representations.
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| 4. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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Federated learning for smart cities : A comprehensive survey
- 2023
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Ingår i: Sustainable Energy Technologies and Assessments. - : Elsevier. - 2213-1388 .- 2213-1396. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
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| 5. |
- Victor, Nancy, et al.
(författare)
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Federated learning for iout : Concepts, applications, challenges and future directions
- 2022
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Ingår i: IEEE Internet of Things Magazine (IoT). - 2576-3180 .- 2576-3199. ; 5:4
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.
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