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UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things

- Liu, Yalin, Dai, Hong-Ning, Wang, Hao, Imran, Muhammad, Wang, Xiaofen, Shoaib, Muhammad

  • Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Hao , Imran, Muhammad , Wang, Xiaofen , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Computer Communications Vol. 155, no. (2020), p. 184-196
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  • Description: Low power Internet of Things (IoT) is suffering from two limitations: battery-power limitation of IoT nodes and inflexibility of infrastructure-node deployment. In this paper, we propose an Unmanned Aerial Vehicle (UAV)-enabled data acquisition scheme with directional wireless energy transfer (WET) to overcome the limitations of low power IoT. The main idea of the proposed scheme is to employ a UAV to serve as both a data collector and an energy supplier. The UAV first transfers directional wireless energy to an IoT node which then sends back the data packets to the UAV by using the harvested energy. Meanwhile, we minimize the overall energy consumption under conditions of balanced energy supply and limited overall time. Moreover, we derive the optimal values of WET time and data transmission power. After analysing the feasibility of the optimal WET time and data transmission, we design an allocation scheme based on the feasible ranges of data size level and channel-fading degree. The numerical results show the feasibility and adaptability of our allocation scheme against the varied values of multiple system parameters. We further extend our scheme to the multi-node scenario by re-designing energy beamforming and adopting multi-access mechanisms. Moreover, we also analyse the mobility of UAVs in the proposed scheme. © 2020 Elsevier B.V.

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

- Dai, Hong-Ning, Wang, Hao, Xu, Guangquan, Wan, Jiafu, Imran, Muhammad

  • Authors: Dai, Hong-Ning , Wang, Hao , Xu, Guangquan , Wan, Jiafu , Imran, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Enterprise Information Systems Vol. 14, no. 9-10 (2020), p. 1279-1303
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  • Description: Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Blockchain-based data privacy management with Nudge theory in open banking

- Wang, Hao, Ma, Shenglan, Dai, Hong-Ning, Imran, Muhammad, Wang, Tongsen

  • Authors: Wang, Hao , Ma, Shenglan , Dai, Hong-Ning , Imran, Muhammad , Wang, Tongsen
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 110, no. (2020), p. 812-823
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  • Description: Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments. © 2019 Elsevier B.V.
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