In Search of the Future Technologies: Fusion of Machine Learning, Fog and Edge Computing in the Internet of Things

Naveen, Soumyalatha; Kounte, Manjunath R

doi:10.1007/978-3-030-24643-3_33

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Using their well-designed learning techniques, intelligent machines can automatically execute various functions such as learning, perceiving, reasoning, natural language processing, and resolving complex problems [147]. The robust AI-based intelligence is obtained from machine learning, deep learning, fuzzy logic, semantic/neural network, and data fusion techniques to provide instinctive resource provision [148], [149], [150], [151]. The main aim of the techniques mentioned above is to acquire hidden knowledge by processing the raw data.…”

Section: Integration Of Iot and Artificial Intelligencementioning

confidence: 99%

A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications

et al. 2023

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Internet of Things is emerging as an incredible future technology to improve the existing lifestyle from the research community, industry, and the public sector. The main intention of IoT is to create an efficient, interactive, and autonomous infrastructure for a safer and healthier world. Moreover, it grows faster day-to-day with the support of many other technologies, i.e. Cloud computing, Blockchain, Wireless Body Sensor Networks, Nanotechnology, and Artificial Intelligence for smart applications, including healthcare, environment, automotive industries, transportation, agriculture, etc. Nevertheless, managing big data is one of the challenging tasks due to the increased number of devices leading to various serious issues like security, privacy, accuracy, latency, scheduling, etc. Further, specific infrastructures with remarkable techniques are required to analyze the bulk of raw data to progress the quality of life and allow timely intervention through various capabilities, i.e., data capture, unique identification, actuation, communication, data mining, etc. In past literature, numerous reviews/surveys are presented that explore the technologies mentioned above as standalone and application specific. However, this paper aims to integrate all the mentioned technologies and deliver a clear vision to future researchers (newcomers) as a kick-start article to boost up and understand the status of the existing research through a comprehensive review of the Internet of Things and its evolution in wireless telecommunications from a general perspective. The most significant challenges and issues are highlighted to research further in these evolving domains.

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Section: Integration Of Iot and Artificial Intelligencementioning

confidence: 99%

A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications

et al. 2023

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“…This difficulty has been partially alleviated by the presented techniques, but it remains a concern [82]. In the application placement problem, applied techniques for optimising time performance metrics in the category of deep learning algorithms [83,84,85]. We may be able to achieve better outcomes if we use different machine learning algorithms and evolutionary algorithms or novel combinatorial techniques.…”

Section: Open Challengesmentioning

confidence: 99%

“…AI and machine learning systems absorb vast volumes of data to spot patterns and deliver reliable suggestions [111]. Cloud-based streaming of high-definition video data results in latency issues and increased costs, since huge bandwidth is utilised, in AI use cases requiring video analysis [84]. When ML triggers, decisions and actions must be made in real time, the latency and dependence on central processing in the cloud are detrimental.…”

Section: Edge Computingmentioning

confidence: 99%

“…Highly dispersed and diverse networks define edge settings. Because of the disparate nature of the infrastructure's components and the high costs associated with acquiring the various skills and resources required, this creates "edge silos", which only serves to complicate matters further [84]. To handle the extremely dispersed and heterogeneous edge environment, AI-based intelligent software is crucial.…”

Section: Open Challengesmentioning

confidence: 99%

“…• Many small and medium-sized businesses (SMEs), IT managers, and government decision-makers are unaware of the possibilities and applications of AI-integrated edge computing [84]. It may take some time for nations in Asia-Pacific that are still learning about cloud computing to adopt its capabilities for edge computing.…”

Section: Open Challengesmentioning

confidence: 99%

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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In Search of the Future Technologies: Fusion of Machine Learning, Fog and Edge Computing in the Internet of Things

Cited by 12 publications

References 13 publications

A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications

A Review on the Immediate Advancement of the Internet of Things in Wireless Telecommunications

AI for Next Generation Computing: Emerging Trends and Future Directions

Oil Spill Detection and Confrontation Using Instance Segmentation and Swarm Intelligence

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