AIoT

Artificial Intelligence of Things.

The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.

Ranging from home automation to climate prediction, the use-cases for AIoT is wide and varied. TensorLet is investing heavily on AIoT.

High Quality Standards

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Leading Experts

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Complex Sollutions

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Flexible Prices

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TensorLet Team

The achievement of AIoT we did by now!

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Related Publications

[BookChapter] X.-Y. Liu, Y. Fang, L. Yang, Z. Li, A. Walid, High performance tensor decompositions for compressing and accelerating deep neural networks, in the book Tensors for Data Processing, Elsevier, 2021.

[TNNLS] X.-Y. Liu, X. Wang*. Real-time indoor localization for smartphones using tensor-generative adversarial nets. IEEE Transactions on Neural Networks and Learning Systems, 2020.

[TBD] X.-Y. Liu, X. Wang. LS-decomposition for robust recovery of sensory big data. IEEE Transactions on Big Data, 2017.

[TMC] X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang, M.-Y. Wu. Adaptive sampling of RF fingerprints for fine-grained indoor localization. IEEE Transactions on Mobile Computing, 2016.

[IoT Journal] H. Zheng, M. Gao, Z. Zhang, X.-Y. Liu, X. Feng. An adaptive sampling scheme via approximate volume sampling for fingerprint-based indoor localization. IEEE Internet of Things Journal, 2019.

[TPDS] T. Zhang, X.-Y. Liu, and X. Wang. High Performance GPU tensor completion with tubal-sampling pattern. IEEE Transactions on Parallel and Distributed Systems, 2020.

[TPDS] X.-Y. Liu, Y. Zhu, L. Kong, C. Liu, Y. Gu, A. V. Vasilakos, M.-Y. Wu. CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,Vol.26, No.8, pp. 2188-2197, 2015. (ESI-Highly Cited)

[TPDS] L. Kong, M. Xia, X.-Y. Liu, G. Chen, Y. Gu, M.-Y. Wu, X. Liu. Data loss and reconstruction in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,Vol. 25, No. 11, pp. 2818-2828, 2014. (ESI-Highly Cited)

[TPDS] L. Kong, M. Zhao, X.-Y. Liu, J. Lu, Y. Liu, M.-Y. Wu, and W. Shu. Surface coverage in sensor networks. IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 1, pp. 234-243, 2013.

[TNSE] Y. Wu, X.-Y. Liu, L. Fu, X. Wang. Energy-efficient and robust tensor-encoder for wireless camera networks in Internet of things. IEEE Transactions on Network Science and Engineering, 2018.

[TITS] Ming Zhu, Xiao-Yang Liu, and Xiaodong Wang. An online ride-sharing path planning strategy for public vehicle systems. IEEE Transactions on Intelligent Transportation Systems (TITS), 2018.

[TITS] Ming Zhu, Xiao-Yang Liu, and Xiaodong Wang. Joint transportation and charging scheduling in public vehicle systems-a game theoretic approach. IEEE Transactions on Intelligent Transportation Systems (TITS), 2018.

[TITS] Ming Zhu, Xiao-Yang Liu, et al. Public vehicles for future urban transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2016.

[ToSN] R. Jia, J. Zhang, X.-Y. Liu, P. Liu, L. Fu, and X. Wang. Optimal rate control for energy-harvesting systems with random data and energy arrivals. ACM Transactions on Sensor Networks (ToSN), Vol. 15, No. 1, pp. 1-30, 2019.

[Geophysics] F. Qian, M. Yin, X.-Y. Liu, Y.-J. Wang, C. Lu, G.-M. Hu. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 2018.

[COMNET] L. Kong, Q. Xiang, X. Liu, X.-Y. Liu*, X. Gao, G. Chen, M.-Y. Wu. ICP: Instantaneous clustering protocol for wireless sensor networks. Elsevier Computer Networks, 2016.

[Geophysics] F. Qian, M. Yin, X.-Y. Liu, Y.-J. Wang, C. Lu, G.-M. Hu. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 2018.

[ICDCS] L. Kong, L. He, X.-Y. Liu, Y. Gu, M.-Y. Wu, X. Liu. Privacy-preserving compressive sensing for crowdsensing based trajectory recovery. IEEE ICDCS, Columbus, Ohio, USA, 2015.

[INFOCOM] L. Kong, M. Xia, X.-Y. Liu, M.-Y. Wu, Xue Liu. Data loss and reconstruction in sensor networks. IEEE INFOCOM, Turin, Italy, 2013.

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