Cognitive Popularity Based AI Service Sharing for Software-Defined Information-Centric Networks

Liao, Siyi; Wu, Jun; Li, Jianhua; Mumtaz, Shahid; Jolfaei, Alireza; Bubnys, Remigijus

doi:10.1109/tnse.2020.2993457

Cited by 17 publications

(10 citation statements)

References 24 publications

(3 reference statements)

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“…Combined with edge computing, the emerging edge AI has already become the last mile of artificial intelligence for IoT [20] [21]. Once appeared, it received a tremendous amount of interest.…”

Section: B Edge Intelligence For Iotmentioning

confidence: 99%

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Lin

Bashir

et al. 2022

IEEE Internet Things J.

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Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy-preserving and quality of service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this paper, we propose a novel Wirelessly Powered Edge intelliGence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. Firstly, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly-powered multi-agent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

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Section: B Edge Intelligence For Iotmentioning

confidence: 99%

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Lin

Bashir

et al. 2022

IEEE Internet Things J.

Self Cite

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“…1) Joint Optimized Computing Efficiency and Accuracy: In smart computer/communication networks, users inevitably generate similar ML tasks that may require the same type of data and even expect the same training results [179]. Since the computing resources of smart computer/communication networks are limited, these large numbers of repeated model training lead to a large number of redundant computations in the network and cause a significant waste of resources.…”

Section: A Smart Computer/communication Networkmentioning

confidence: 99%

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang,

Yu,

Xie

et al. 2022

Preprint

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The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

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“…This transition into a cognitive city. Such cognition (Tripathi et al, 2020, Lioa et al, 2020, Ali et al, 2020, Kumar et al, 2020b) will be based on an appropriate set of information at the right time, and will lead to a proactive and personalized city. Results from recent studies show that deep learning models can be most effective against cyberbullying when directly compared with others and paves the way for future hybrid technologies that may be employed to combat this serious online issue (Iwendi et al, 2020, Kumar & Sachdeva, 2021).…”

Section: Major Findingsmentioning

confidence: 99%

Soft computing for abuse detection using cyber‐physical and social big data in cognitive smart cities

Sangwan

Bhatia

2021

Expert Systems

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The internet of things (IoT) and the smart city crusade has seen an exponential increase in the number of data points we collect. Cities can use this data for an exceedingly heterogeneous range of purposes such as traffic, parking and public safety. Quite evidently, public safety is an essential pillar of these interconnected, instrumented and intelligent cities. The big data from cyber-physical systems and social media platforms can be used for predictive policing to identify potential criminal activities, abuse, offenders, and victims of abuse. This study offers a systematic literature review on the use of soft computing techniques for abuse detection in the complex cyber-physical-social big data systems in cognitive smart cities. The objective is to define and identify the diverse concept of abuse and systematize techniques for automatic abuse detection for cyber abuse detection on social media and real-time abuse detection using IoT. The cyber abuse studies on social media platforms have further been categorized as cyber-hate and cyberbullying whereas the real-time abuse includes studies using Internet of cyber-physical systems. As in a cognitive smart city, citizens expect more from their urban environments with minimal intervention, this study helps to establish the need to capture situational context and awareness in real-time and foster the need to develop a proactive as well as reactive safety mechanism to mitigate the risks of online abuse. The need of the hour is to entrench self-learning, thinking and understanding capabilities into the physical and social world for reinforcing intelligent mechanisms which can detect assaults and disorderly conduct.

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Cognitive Popularity Based AI Service Sharing for Software-Defined Information-Centric Networks

Cited by 17 publications

References 24 publications

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Soft computing for abuse detection using cyber‐physical and social big data in cognitive smart cities

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