[1] E. W. L. Cheng, H. Li, P. E. D. Love, and Z. Irani, “Network communication in the construction

industry,” Corporate Communications: An International Journal, vol. 6, no. 2, pp. 61–70, 2001.

[2] M. Schlichting, M. M. Díaz, J. Xin, and M. Rosbash, “Neuron-specific knockouts indicate the

importance of network communication to Drosophila rhythmicity,” Elife, vol. 8, p. e48301, 2019.

[3] M. Kang, G. Yang, Y. Yoo, and C. Yoo, “TensorExpress: In-network communication scheduling for

distributed deep learning,” in 2020 IEEE 13th international conference on cloud computing (CLOUD),

2020, pp. 25–27.

[4] X.-B. Chen, Y.-L. Wang, G. Xu, and Y.-X. Yang, “Quantum network communication with a novel

discrete-time quantum walk,” Ieee Access, vol. 7, pp. 13634–13642, 2019.

[5] S. K. Tripathi, M. Kumar, and A. Kumar, "Graphene-based tunable and wideband terahertz antenna for

wireless network communication," Wireless Networks, vol. 25, no. 7, pp. 4371–4381, 2019.

[6] W. Li, Z. Xie, J. Zhao, and P. K. Wong, "Velocity-based robust fault-tolerant automatic steering control

of autonomous ground vehicles via adaptive event-triggered network communication," Mechanical

Systems and Signal Processing, vol. 143, p. 106798, 2020.

[7] Y.-C. Sun and G.-H. Yang, “Event-triggered state estimation for networked control systems with lossy

network communication,” Information Sciences, vol. 492, pp. 1–12, 2019.

[8] V. N. Poole, O.-Y. Lo, T. Wooten, I. Iloputaife, L. A. Lipsitz, and M. Esterman, “Motor-cognitive neural

network communication underlies walking speed in community-dwelling older adults,” Frontiers in

aging neuroscience, vol. 11, p. 159, 2019.

[9] H. Lin, Z. T. Kalbarczyk, and R. K. Iyer, "Raincoat: Randomization of network communication in power

grid cyberinfrastructure to mislead attackers," IEEE Transactions on Smart Grid, vol. 10, no. 5, pp.

4893–4906, 2018.

[10] T. Kustermann, T. Popov, G. A. Miller, and B. Rockstroh, “Verbal working memory‐related neural

network communication in schizophrenia,” Psychophysiology, vol. 55, no. 9, p. e13088, 2018.

[11] H. Huang et al., "Deep learning for physical-layer 5G wireless techniques: Opportunities, challenges,

and solutions," IEEE Wireless Communications, vol. 27, no. 1, pp. 214–222, 2019.

[12] H. Wu, X. Li, and Y. Deng, “Deep learning-driven wireless communication for edge-cloud computing:

opportunities and challenges,” Journal of Cloud Computing, vol. 9, no. 1, pp. 1–14, 2020.

[13] Z. Tao and Q. Li, “{eSGD}: Communication Efficient Distributed Deep Learning on the Edge,” 2018.

[14] G. Thamilarasu and S. Chawla, “Towards deep-learning-driven intrusion detection for the internet of

things,” Sensors, vol. 19, no. 9, p. 1977, 2019.

[15] Z. Lv, A. K. Singh, and J. Li, “Deep learning for security problems in 5G heterogeneous networks,”

IEEE Network, vol. 35, no. 2, pp. 67–73, 2021.

[16] A. Koloskova, T. Lin, S. U. Stich, and M. Jaggi, “Decentralized deep learning with arbitrary

communication compression,” arXiv preprint arXiv:1907.09356, 2019.

[17] J. Chen, K. Li, Q. Deng, K. Li, and S. Y. Philip, “Distributed deep learning model for intelligent video

surveillance systems with edge computing,” IEEE Transactions on Industrial Informatics, 2019.

[18] M. Mahdavisharif, S. Jamali, and R. Fotohi, “Big data-aware intrusion detection system in

communication networks: a deep learning approach,” Journal of Grid Computing, vol. 19, no. 4, pp. 1–

28, 2021.

[19] N. Shazeer et al., "Mesh-TensorFlow: Deep learning for supercomputers," Advances in neural

information processing systems, vol. 31, 2018.

[20] H. Geng, H. Liu, L. Ma, and X. Yi, "Multi-sensor filtering fusion meets censored measurements under a

constrained network environment: advances, challenges, and prospects," International Journal of

Systems Science, vol. 52, no. 16, pp. 3410–3436, 2021.

[21] C. Yuan, W. Yue-ping, T. Xue-mei, and Z. Xiao-fang, “An evaluation method for network

communication system efficiency based on multi-source information fusion,” in 2018 37th Chinese

Control Conference (CCC), 2018, pp. 4305–4309.

[22] T. Lin, P. Wu, and F. Gao, “Information security of flowmeter communication network based on multisensor

data fusion,” Energy Reports, vol. 8, pp. 12643–12652, 2022.

[23] A.-M. Yang, X.-L. Yang, J.-C. Chang, B. Bai, F.-B. Kong, and Q.-B. Ran, "Research on a fusion scheme

of the cellular network and wireless sensor for cyber-physical social systems," Ieee Access, vol. 6, pp.

18786–18794, 2018.

[24] T. Li, J. M. Corchado, and S. Sun, "Partial consensus and conservative fusion of Gaussian mixtures for

distributed Ph.D. fusion," IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 5, pp.

2150–2163, 2018.

[25] Y. Yuan and Y. Li, “Research on Spatial Agglomeration Characteristics of Aerospace Cultural and

Creative Industries in Smart City under Multidata Fusion,” Security and Communication Networks, vol.

2022, 2022.

[26] R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “Opv2v: An open benchmark dataset and fusion

pipeline for perception with vehicle-to-vehicle communication,” in 2022 International Conference on

Robotics and Automation (ICRA), 2022, pp. 2583–2589.

[27] G. Li, Z. Yan, Y. Fu, and H. Chen, “Data fusion for network intrusion detection: a review,” Security and

Communication Networks, vol. 2018, 2018.