In-Network Convolution in Grid Shaped Sensor Networks
Keywords:Sensor networks, edge computing, fall detection, convolutional neural networks, network simulator ns-3
Gathering information is the primary purpose of a Sensor Network. The task is performed by spatially distributed nodes equipped with sensing, processing, and communication capabilities. However, data gathered from a sensor network must be processed, and often the collective computation capability of nodes forming the sensor network is neglected in favor of data processing on cloud systems. Nowadays, Edge Computing has emerged as a new paradigm aiming to migrate data processing close to data sources. In this contribution, we focus on the development of a sensor network designed to detect a person’s fall. We named this sensor network the smart floor. Fall detection is tackled with a Convolutional Neural Network, and we propose an approach for in-network processing of convolution layers on grid-shaped sensor networks. The proposed approach could lead to the development of a sensor network that detects falls by performing CNN inference processing on the edge. We complement our work with a simulation using the simulator ns-3. The simulation is designed to emulate the communication overhead of the proposed approach applied to a wired sensor network that resembles the smart floor. Simulation results provide evidence on the feasibility of the proposed concept applied to wired grid shaped sensor networks.
Ejaz Ahmed, Arif Ahmed, Ibrar Yaqoob, Junaid Shuja, Abdullah Gani, Muhammad Imran, and Muhammad Shoaib. Bringing computation closer toward the user network: Is edge computing the solution? IEEE Communications Magazine, 55(11):138–144, 2017.
F Arifin, H Robbani, T Annisa, and NNMI Ma’Arof. Variations in the number of layers and the number of neurons in artificial neural networks: Case study of pattern recognition. In Journal of Physics: Conference Series, volume 1413, page 012016. IOP Publishing, 2019.
Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Pieter Simoens, Piet Demeester, and Bart Dhoedt. Distributed neural networks for internet of things: The big-little approach. In Internet of Things. IoT Infrastructures, pages 484–492. Springer International Publishing, 2016.
Stuart E Dreyfus. An appraisal of some shortest-path algorithms. Operations research, 17(3):395–412, 1969.
Rong Du, Sindri Magnusson, and Carlo Fischione. The internet of things as a deep neural network. IEEE Communications Magazine, 58(9):20–25, 2020.
Le Fang, Yu Wu, Chuan Wu, and Yizhou Yu. A non-intrusive elderly home monitoring system. IEEE Internet of Things Journal, 2020.
Guodong Feng, Jiechao Mai, Zhen Ban, Xuemei Guo, and Guoli Wang. Floor pressure imaging for fall detection with fiber-optic sensors. IEEE Pervasive Computing, 15:40–47, 03 2016.
Yuta Fukushima, Daiki Miura, Takashi Hamatani, Hirozumi Yamaguchi, and Teruo Higashino. Microdeep: In-network deep learning by micro-sensor coordination for pervasive computing. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pages 163–170. IEEE, 2018.
Rafael C Gonzalez, Richard E Woods, and Barry R Masters. Digital image processing third edition. Pearson Prentice Hall, pages 743–747, 2008.
R Jan Gurley, Nancy Lum, Merle Sande, Bernard Lo, and Mitchell H Katz. Persons found in their homes helpless or dead. New England Journal of Medicine, 334(26):1710–1716, 1996.
Thomas R Henderson, Mathieu Lacage, George F Riley, Craig Dowell, and Joseph Kopena. Network simulations with the ns-3 simulator. SIGCOMM demonstration, 14(14):527, 2008.
Nada B Jarah. Deep learning in wireless sensor network. Journal of Al-Qadisiyah for computer science and mathematics, 13(1):Page–11, 2021.
Nicholas D Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar. Deepx: A software accelerator for low-power deep learning inference on mobile devices. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pages 1–12. IEEE, 2016.
F Le Deist and M Latouille. Acceptability conditions for telemonitoring gerontechnology in the elderly: optimising the development and use of this new technology. Irbm, 37(5-6):284–288, 2016.
Zewen Li, Wenjie Yang, Shouheng Peng, and Fan Liu. A survey of convolutional neural networks: analysis, applications, and prospects. arXiv preprint arXiv:2004.02806, 2020.
Wen-Hwa Liao, Jang-Ping Sheu, and Yu-Chee Tseng. Grid: A fully location-aware routing protocol for mobile ad hoc networks. Telecommunication systems, 18(1):37–60, 2001.
Pavlo Molchanov, Shalini Gupta, Kihwan Kim, and Jan Kautz. Hand gesture recognition with 3d convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 1–7, 2015.
Mithun Mukherjee, Rakesh Matam, Constandinos X Mavromoustakis, Hao Jiang, George Mastorakis, and Mian Guo. Intelligent edge computing: Security and privacy challenges. IEEE Communications Magazine, 58(9):26–31, 2020.
Hrovatin Niki. Neintruzivna identikacija padcev s pomočjo pametnih tal: magistrsko delo. PhD thesis, Univerza na Primorskem, Fakulteta za matematiko, naravoslovje in informacijske tehnologije, 2020.
World Health Organization, World Health Organization. Ageing, and Life Course Unit. WHO global report on falls prevention in older age. World Health Organization, 2008.
Kazi Chandrima Rahman. A survey on sensor network. Journal of Computer and Information Technology, 1(1):76–87, 2010.
Qiongfeng Shi, Zixuan Zhang, Tianyiyi He, Zhongda Sun, Bingjie Wang, Yuqin Feng, Xuechuan Shan, Budiman Salam, and Chengkuo Lee. Deep learning enabled smart mats as a scalable floor monitoring system. Nature communications, 11(1):1–11, 2020.
Anuradha Singh, Saeed Ur Rehman, Sira Yongchareon, and Peter Han Joo Chong. Sensor technologies for fall detection systems: A review. IEEE Sensors Journal, 20(13):6889–6919, 2020.
Ashish Singh and Kakali Chatterjee. Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79:88–115, 2017.
Axel Steinhage and Christl Lauterbach. Sensfloor® and navifloor®: Robotics applications for a large-area sensor system. International Journal of Intelligent Mechatronics and Robotics (IJIMR), 3(3):43–59, 2013.
Aleksandar Tošić, Niki Hrovatin, and Jernej Vičič. Data about fall events and ordinary daily activities from a sensorized smart floor. Data in Brief, 37:107253, 2021.
Aleksandar Tošić, Jernej Vičič, and Michael David Burnard. Privacy preserving indoor location and fall detection system. In Human-Computer Interaction in Information Society : proceedings of the 22nd International Multiconference Information Society – IS 2019, pages 9–12, 2019.
Sun-Chong Wang. Artificial neural network. In Interdisciplinary computing in java programming, pages 81–100. Springer, 2003.
Wei Yu, Fan Liang, Xiaofei He, William Grant Hatcher, Chao Lu, Jie Lin, and Xinyu Yang. A survey on the edge computing for the internet of things. IEEE access, 6:6900–6919, 2017.
Salifu Yusif, Jeffrey Soar, and Abdul Hafeez-Baig. Older people, assistive technologies, and the barriers to adoption: A systematic review. International journal of medical informatics, 94:112–116, 2016.