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Recommender system for mobile users

Enjoy internet of things socially with wireless device-to-device physical links

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Abstract

As predicted, trillions of devices and billions of services will be integrated into Internet of Things (IoT), where most value added applications rely on wireless physical links. In this paper, we develop a recommender system to overcome the challenges of large-scale mobile IoT. The proposed recommender system socially matches wireless devices to communicate and share their contents based on similarities, distance, velocity, wireless channel quality and remaining energy. The physical layer connections are realized by device-to-device spectrum sharing techniques, and we accordingly designed a cooperative multicast service case to make full use of the wireless broadcasting nature. A “green communication” orientated algorithm is proposed to allocate power resources, adaptively adjust data rate and recommend partners as mobile relays. Simulation results show that the proposed system can efficiently utilize the wireless resource of mobile IoT and appropriately recommend partners to assist more users into IoT services.

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Notes

  1. This is possible when \(\mathcal {V}(t)\) may be “incomplete” in Section 3.2.2. If \(|\overline {\mathcal {V}}|<N-|\mathcal {V}(t)|\), we have \(|V(t)\cup \overline V|\leq N\).

  2. In this paper, we do not consider the inaccuracy of CQI. For relaying scenarios with time-variant channel of unknown CQI, please refer to our previous works [18] and [19].

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China under grants 61540046 and 61601347, by “111” project of China under grant B08038, and by the scholarship from China Scholarship Council (CSC) under grant No. 201506960024.

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Authors and Affiliations

  1. Xidian University, No.2 South Taibai Road, Xi’an, 710071, China

    Chao Ren, Jian Chen, Yonghong Kuo, Di Wu & Mengqi Yang

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  1. Chao Ren
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  2. Jian Chen
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  3. Yonghong Kuo
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  4. Di Wu
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  5. Mengqi Yang
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Correspondence to Jian Chen.

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Ren, C., Chen, J., Kuo, Y. et al. Recommender system for mobile users. Multimed Tools Appl 77, 4133–4153 (2018). https://doi.org/10.1007/s11042-017-4527-y

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