X. Li, D. Li, J. Wan, A. V. Vasilakos, C.-F. Lai, and S. Wang, "A review of industrial wireless networks in the context of Industry 4.0," Wireless networks, vol. 23, no. 1, pp. 23-41, 2017.
 D.-H. Kim et al., "Smart machining process using machine learning: A review and perspective on machining industry," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 5, no. 4, pp. 555-568, 2018.
 W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, "A survey of deep neural network architectures and their applications," Neurocomputing, vol. 234, pp. 11-26, 2017.
 K. Vamsikrishna, D. P. Dogra, and M. S. Desarkar, "Computer-vision-assisted palm rehabilitation with supervised learning," IEEE Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 991-1001, 2015.
 F. Tao and C. Busso, "Gating neural network for large vocabulary audiovisual speech recognition," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 7, pp. 1290-1302, 2018.
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580-587.
 M. Abadi et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, 2016.
 L. Dekker and C. Ritsema, "Wetting patterns and moisture variability in water repellent Dutch soils," Journal of Hydrology, vol. 231, pp. 148-164, 2000.
 L. Meng and S. M. Quiring, "A comparison of soil moisture models using soil climate analysis network observations," Journal of Hydrometeorology, vol. 9, no. 4, pp. 641-659, 2008.
 N. D. Lane, S. Bhattacharya, A. Mathur, P. Georgiev, C. Forlivesi, and F. Kawsar, "Squeezing deep learning into mobile and embedded devices," IEEE Pervasive Computing, vol. 16, no. 3, pp. 82-88, 2017.
 M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, "Deep learning for IoT big data and streaming analytics: A survey," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923-2960, 2018.
 F. Samie, S. Paul, L. Bauer, and J. Henkel, "Highly efficient and accurate seizure prediction on constrained iot devices," in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018: IEEE, pp. 955-960.
 N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, and F. Kawsar, "An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices," in Proceedings of the 2015 international workshop on internet of things towards applications, 2015, pp. 7-12.
 A. E. Eshratifar and M. Pedram, "Energy and performance efficient computation offloading for deep neural networks in a mobile cloud computing environment," in Proceedings of the 2018 on Great Lakes Symposium on VLSI, 2018, pp. 111-116.
 H. Li, K. Ota, and M. Dong, "Learning IoT in edge: Deep learning for the Internet of Things with edge computing," IEEE network, vol. 32, no. 1, pp. 96-101, 2018.
 J. Lee, M. Stanley, A. Spanias, and C. Tepedelenlioglu, "Integrating machine learning in embedded sensor systems for Internet-of-Things applications," in 2016 IEEE international symposium on signal processing and information technology (ISSPIT), 2016: IEEE, pp. 290-294.
 Y. Fukushima, D. Miura, T. Hamatani, H. Yamaguchi, and T. Higashino, "MicroDeep: In-network deep learning by micro-sensor coordination for pervasive computing," in 2018 IEEE International Conference on Smart Computing (SMARTCOMP), 2018: IEEE, pp. 163-170.
 X. Fafoutis, L. Marchegiani, A. Elsts, J. Pope, R. Piechocki, and I. Craddock, "Extending the battery lifetime of wearable sensors with embedded machine learning," in 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), 2018: IEEE, pp. 269-274.
 P. Park, S. C. Ergen, C. Fischione, C. Lu, and K. H. Johansson, "Wireless network design for control systems: A survey," IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 978-1013, 2017.
 C. Zhang, P. Patras, and H. Haddadi, "Deep learning in mobile and wireless networking: A survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019.
 M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, "Machine learning in wireless sensor networks: Algorithms, strategies, and applications," IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996-2018, 2014.
 K. Ovsthus and L. M. Kristensen, "An industrial perspective on wireless sensor networks—A survey of requirements, protocols, and challenges," IEEE communications surveys & tutorials, vol. 16, no. 3, pp. 1391-1412, 2014.
 C. Lu et al., "Real-time wireless sensor-actuator networks for industrial cyber-physical systems," Proceedings of the IEEE, vol. 104, no. 5, pp. 1013-1024, 2015.
 B. Keswani et al., "Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms," Neural Computing and Applications, vol. 31, no. 1, pp. 277-292, 2019.
 A. L. Johann, A. G. de Araújo, H. C. Delalibera, and A. R. Hirakawa, "Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener," Computers and Electronics in Agriculture, vol. 121, pp. 420-428, 2016.
 M. K. Gill, T. Asefa, M. W. Kemblowski, and M. McKee, "Soil moisture prediction using support vector machines 1," JAWRA Journal of the American Water Resources Association, vol. 42, no. 4, pp. 1033-1046, 2006.
 L. L. Bello and W. Steiner, "A Perspective on IEEE Time-Sensitive Networking for Industrial Communication and Automation Systems," Proceedings of the IEEE, vol. 107, no. 6, pp. 1094-1120, 2019.
 M. Wollschlaeger, T. Sauter, and J. Jasperneite, "The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0," IEEE industrial electronics magazine, vol. 11, no. 1, pp. 17-27, 2017.
 S. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, "The internet of things for health care: a comprehensive survey," IEEE Access, vol. 3, pp. 678-708, 2015.
 M. Verhelst and B. Moons, "Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices," IEEE Solid-State Circuits Magazine, vol. 9, no. 4, pp. 55-65, 2017.
 Q. Wang et al., "Reducing delay and maximizing lifetime for wireless sensor networks with dynamic traffic patterns," IEEE Access, vol. 7, pp. 70212-70236, 2019.
 X. Ge, F. Yang, and Q.-L. Han, "Distributed networked control systems: A brief overview," Information Sciences, vol. 380, pp. 117-131, 2017.
 R. V. Kulkarni, A. Forster, and G. K. Venayagamoorthy, "Computational intelligence in wireless sensor networks: A survey," IEEE communications surveys & tutorials, vol. 13, no. 1, pp. 68-96, 2010.
 H. Xu, W. Yu, D. Griffith, and N. Golmie, "A survey on industrial Internet of Things: A cyber-physical systems perspective," IEEE Access, vol. 6, pp. 78238-78259, 2018.
 Scikit-learn. "Machine Learning in Python." https://scikit-learn.org/stable/ (accessed May 2020).
 D. P. K. a. J. L. Ba, "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION," arXiv:1412.6980v9, 2017.
[Online]. Available: https://arxiv.org/pdf/1412.6980.pdf.
 F. Javed, M. K. Afzal, M. Sharif, and B.-S. Kim, "Internet of things (IoT) operating Systems support, networking technologies, applications, and challenges: A comparative review," IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2062-2100, 2018.
 F. Kaup, P. Gottschling, and D. Hausheer, "PowerPi: Measuring and modeling the power consumption of the Raspberry Pi," in 39th Annual IEEE Conference on Local Computer Networks, 2014: IEEE, pp. 236-243.
 F. Kaup, S. Hacker, E. Mentzendorff, C. Meurisch, and D. Hausheer, "Energy models for NFV and service provisioning on fog nodes," in NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, 2018: IEEE, pp. 1-7.
 Advanticsys. "802.15.4 Mote Modules." https://www.advanticsys.com/shop/802154-mote-modules-c-7_3.html (accessed 2020).