Social Edge Intelligence: Integrating Human and Artificial Intelligence at the Edge

Wang, Dong; Zhang, Dongke; Zhang, Yang; Rashid, Tahmid; Shang, Lanyu; Wei, Na

doi:10.1109/cogmi48466.2019.00036

Cited by 19 publications

(11 citation statements)

References 40 publications

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“…However, digital twins are an interesting research topic, especially in terms of automated discovery and digital representation of physical industrial settings, and the subsequent optimization. Finally, human interaction with Smart applications can be used to augment AI, creating Social Edge Intelligence (SEI) [163]. SEI can drastically improve applications in which AI is used to analyze gathered data, but in which some steps benefit from higher cognitive abilities than the state of the art currently offers.…”

Section: Applicationsmentioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with "Enabling Technology" for Edge Intelligence, "Organization" of the edge using AI, and AI "Applications" in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The "Organization" aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

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Section: Applicationsmentioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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show abstract

“…At the level of sensing, [22] develops the concept of Social Edge Intelligence, that proposes the integration of artificial intelligence with human intelligence to address critical research challenges of Edge computing. In this context, it proposes the challenge of efficient resource management that exploit the heterogeneity present in the Edge devices and diagnoses the need of additional research to enable seamless device collaboration for timely task execution.…”

Section: Related Workmentioning

confidence: 99%

Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms

Ferrer

Becker

Schmidt

et al. 2021

Preprint

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In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional data centre boundaries. The focal point of this continuum are cognitive devices, which have to make decisions autonomously using their on-board computation and storage capacity based on information sensed from their environment. Such devices are moving and cannot rely on fixed infrastructure elements, but instead realise on-the-fly networking and thus frequently join and leave temporal swarms. All this creates novel demands for the underlying architecture and resource management, which must bridge the gap from edge to cloud environments, while keeping the QoS parameters within required boundaries. The paper presents an initial architecture and a resource management framework for the implementation of this type of IT service provisioning.

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“…A third probable future avenue of research can focus on designing decentralized model training algorithms for collaboratively acquiring local model updates from privately-owned devices. With the intent for preserving privacy and reducing network bandwidth requirements, federated learning (FL) is gaining traction as a decentralized AI training paradigm [271], [272], where a shared global AI model is trained from a collection of edge devices owned by end users [273]. Future research can focus on constructing FL solutions that can consider the data and device heterogeneity originating from the social and physical sensors in SPS.…”

Section: Harnessing Adaptive Artificial Intelligence (Ai) In Spsmentioning

confidence: 99%

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

Rashid¹,

Wei²,

Wang³

2021

Preprint

Self Cite

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting the real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the occurrences in the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates the social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing innovative and practical SPS systems, which require careful scrutiny and research to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and the road-map for future work. While existing survey papers related to sensing have discussed sensing schemes that are partly related to SPS, they do not explicitly provide an extensive overview of the SPS paradigm or the state-of-the-art SPS schemes. This paper intends to bridge this knowledge gap by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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Social Edge Intelligence: Integrating Human and Artificial Intelligence at the Edge

Cited by 19 publications

References 40 publications

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

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