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Implicit feedback-based group recommender system for internet of things applications

- Guo, Zhiwei, Yu, Keping, Guo, Tan, Bashir, Ali, Imran, Muhammad, Guizani, Mohsen

  • Authors: Guo, Zhiwei , Yu, Keping , Guo, Tan , Bashir, Ali , Imran, Muhammad , Guizani, Mohsen
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual Taipei, 7-11 December 2020 Vol. 2020-January
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  • Description: With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods. © 2020 IEEE.

Robust spammer detection using collaborative neural network in internet-of-things applications

- Guo, Zhiwei, Shen, Yu, Bashir, Ali, Imran, Muhammad, Kumar, Neeraj, Zhang, Di, Yu, Keping

  • Authors: Guo, Zhiwei , Shen, Yu , Bashir, Ali , Imran, Muhammad , Kumar, Neeraj , Zhang, Di , Yu, Keping
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Internet of Things Journal Vol. 8, no. 12 (2021), p. 9549-9558
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  • Description: Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines. © 2014 IEEE.

A novel real-time deterministic scheduling mechanism in industrial cyber-physical systems for energy internet

- Peng, Yuhuai, Jolfaei, Alireza, Yu, Keping

  • Authors: Peng, Yuhuai , Jolfaei, Alireza , Yu, Keping
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5670-5680
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  • Description: As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems. © 2005-2012 IEEE.
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