International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Self-Learning Neuromorphic Sensors for Environmental Monitoring |
|---|---|
| ABSTRACT | Environmental monitoring systems require intelligent sensing platforms capable of real-time analysis, low power consumption, adaptive learning, and autonomous operation. Conventional sensor systems often suffer from high energy consumption, delayed response, and limited adaptability in dynamic environments. Neuromorphic sensors, inspired by the biological nervous system, provide event-driven computation and self-learning capabilities suitable for next-generation environmental monitoring applications. This paper presents a comprehensive study of self-learning neuromorphic sensors integrated with edge artificial intelligence for environmental monitoring. The proposed framework combines spiking neural networks (SNNs), memristive synapses, and adaptive learning algorithms for efficient sensing and decision-making. The architecture enables real-time detection of environmental parameters such as temperature, humidity, air quality, toxic gases, and noise pollution while minimizing power consumption. Simulation results demonstrate improved accuracy, energy efficiency, and response time compared with conventional IoT-based sensing systems. The proposed system shows significant potential for smart cities, industrial safety, agricultural monitoring, and climate observation applications. This perspective highlights the potential of various classes of device technologies for next-generation neuromorphic AI hardware, showcasing key break through sin robust, flexible, and conformable device platforms. Such technologies are particularly promising for resource-constrain ededge platforms, such as wearable electronics, soft robotics, and autonomous embedded sensing systems. Lastly, we discuss thatcircuit- and system-level design must advance alongside device innovation, including robust biasing schemes, reliable peripheral integration, and scalable architectures that can support dense neuromorphic arrays. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications. |
| AUTHOR | SUMAN JAIN Faculty, CTAE, Udaipur, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405088 |
| pdf/88_Self-Learning Neuromorphic Sensors for Environmental Monitoring.pdf | |
| KEYWORDS | |
| References | 1. Patterson, D.; Gonzalez, J.; Hölzle, U.; Le, Q.; Liang, C.; Munguia, L.-M.; Rothchild, D.; So, D.; Texier, M.; Dean, J. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer 2022, 55, 18–28. [CrossRef] 2. Balasubramanian, V. Brain Power. Proc. Natl. Acad. Sci. USA 2021, 118, e2107022118. [CrossRef] [PubMed] 3. Bing, Z.; Meschede, C.; Röhrbein, F.; Huang, K.; Knoll, A.C. A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks. Front. Neurorobot. 2018, 12, 35. [CrossRef] 4. Liu, J.; Lu, H.; Luo, Y.; Yang, S. Spiking neural network-based multi-task autonomous learning for mobile robots. Eng. Appl. Artif. Intell. 2021, 104, 104362. [CrossRef] 5. Liu, J.; Hua, Y.; Yang, R.; Luo, Y.; Lu, H.; Wang, Y.; Yang, S.; Ding, X. Bio-Inspired Autonomous Learning Algorithm with Application to Mobile Robot Obstacle Avoidance. Front. Neurosci. 2022, 16, 905596. [CrossRef] 6. Russo, N.; Huang, H.; Nikolic, K. Live Demonstration: Neuromorphic Robot Goalie Controlled by Spiking Neural Network. In Proceedings of the 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), Taipei, Taiwan, 13–15 October 2022; p. 249. 7. Deng, X.; Weirich, S.; Katzschmann, R.; Delbruck, T. A Rapid and Robust Tendon-Driven Robotic Hand for Human-Robot Interactions Playing Rock-Paper-Scissors. In Proceedings of the IEEE RO-MAN 2024, Pasadena, CA, USA, 26–30 August 2024 8. Clawson, T.S.; Ferrari, S.; Fuller, S.B.; Wood, R.J. Spiking Neural Network (SNN) Control of a Flapping Insect-Scale Robot. In Proceedings of the 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA, 12–14 December 2016; pp. 3381–3388. 9. Cheng, R.; Mirza, K.B.; Nikolic, K. Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks. Appl. Syst. Innov. 2020, 3, 28. [CrossRef] 10. Russo, N.; Huang, H.; Donati, E.; Madsen, T.; Nikolic, K. An Interface Platform for Robotic Neuromorphic Systems. Chips 2023, 2, 20–30. [CrossRef] 11. Lobov, S.A.; Mikhaylov, A.N.; Shamshin, M.; Makarov, V.A.; Kazantsev, V.B. Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot. Front. Neurosci. 2020, 14, 88. [CrossRef] [PubMed] 12. O’Connor, P.; Neil, D.; Liu, S.-C.; Delbruck, T.; Pfeiffer, M. Real-Time Classification and Sensor Fusion with a Spiking Deep Belief Network. Front. Neurosci. 2013, 7, 178. [CrossRef] 13. Ivanov, D.; Chezhegov, A.; Kiselev, M.; Grunin, A.; Larionov, D. Neuromorphic artificial intelligence systems. Front. Neurosci. 2022, 16, 959626. [CrossRef] 14. Furber, S.B.; Galluppi, F.; Temple, S.; Plana, L.A. The SpiNNaker Project. Proc. IEEE 2014, 102, 652–665. [CrossRef] 15. Davies, M.; Srinivasa, N.; Lin, T.-H.; Chinya, G.; Cao, Y.; Choday, S.H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S.; et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning. IEEE Micro 2018, 38, 82–99. [CrossRef] 16. Akopyan, F.; Sawada, J.; Cassidy, A.; Alvarez-Icaza, R.; Arthur, J.; Merolla, P.; Imam, N.; Nakamura, Y.; Datta, P.; Nam, G.-J.; et al. TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 2015, 34, 1537–1557. [CrossRef] 17. Linares-Barranco, B.; Serrano-Gotarredona, T.; Camuñas-Mesa, L.A.; Perez-Carrasco, J.A.; Zamarreño-Ramos, C.; Masquelier, T. On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex. Front. Neurosci. 2011, 5, 26. [CrossRef] 18. Basu, A.; Acharya, J.; Karnik, T.; Liu, H.; Li, H.; Seo, J.-S.; Son, C. Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions. IEEE J. Emerg. Sel. Top. Circuits Syst. 2018, 8, 6. [CrossRef] 19. Natale, L.; Bartolozzi, C.; Nori, F.; Sandini, G.; Metta, G. iCub. arXiv 2021, arXiv:2105.02313. 20. Chou, T.-S.; Bucci, L.D.; Krichmar, J.L. Learning touch preferences with a tactile robot using dopamine modulated stdp in a model of insular cortex. Front. Neurorobot. 2015, 9, 6. [CrossRef] 21. Liu, S.-C.; Delbruck, T. Neuromorphic sensory systems. Curr. Opin. Neurobiol. 2010, 20, 1–8. [CrossRef] 22. Baby, S.A.; Vinod, B.; Chinni, C.; Mitra, K. Dynamic Vision Sensors for Human Activity Recognition. In Proceedings of the 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), Nanjing, China, 26–29 November 2017. [CrossRef] 23. Maass, W. Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Netw. 1997, 10, 1659–1671. [CrossRef] 24. Juarez-Lora, A.; Ponce-Ponce, V.H.; Sossa, H.; Rubio-Espino, E. R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm. Front. Neurorobot. 2022, 16, 904017. [CrossRef] 25. Diehl, P.; Cook, M. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity. Front. Comput. Neurosci. 2015, 9, 99. [CrossRef] [PubMed] 26. Russo, N.; Yuzhong, W.; Madsen, T.; Nikolic, K. Pattern Recognition Spiking Neural Network for Classification of Chinese Characters. In Proceedings of the ESANN 2023 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 4–6 October 2023. [CrossRef] 27. Bohté, S.M.; Kok, J.N.; Poutré, H.L. SpikeProp: Backpropagation for Networks of Spiking Neurons. In Proceedings of the ESANN 2000 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 26–28 April 2000; Volume 48, pp. 419–424. 28. Stimberg, M.; Brette, R.; Goodman, D.F. Brian 2, an Intuitive and Efficient Neural Simulator. eLife 2019, 8, e47314. [CrossRef] [PubMed] 29. Xue, J.; Xie, L.; Chen, F.; Wu, L.; Tian, Q.; Zhou, Y.; Ying, R.; Liu, P. EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing. Sensors 2023, 23, 6548. [CrossRef] [PubMed] 30. Raspberry Pi 5 Single Board Computer. Available online: https://www.raspberrypi.com/5 (accessed on 3 February 2024). 31. Katz, M.L.; Nikolic, K.; Delbruck, T. Live Demonstration: Behavioural Emulation of Event-Based Vision Sensors. In Proceedings of the 2012 IEEE International Symposium on Circuits and Systems, Seoul, Republic of Korea, 20–23 May 2012; pp. 736–740. 32. Lichtsteiner, P.; Posch, C.; Delbruck, T. A 128 × 128 120 dB 15 Latency Asynchronous Temporal Contrast Vision Sensor. IEEE J. Solid-State Circuits 2008, 43, 566–576. [CrossRef] 33. iniVation AG. Libcaer Documentation. Available online: https://libcaer.inivation.com (accessed on 5 February 2024). 34. Yue, D. PyAer Documentation. GitHub. Available online: https://github.com/duguyue100/pyaer (accessed on 14 March 2024). 35. Raspberry Pi Foundation. GPIO Zero Documentation. Available online: https://gpiozero.readthedocs.io (accessed on 19 March 2024). 36. Grinberg, M. Flask Web Development: Developing Web Applications with Python; O’Reilly Media: Sebastopol, CA, USA, 2018. 37. Huang, X.; Li, Z.; Xiang, Y.; Ni, Y.; Chi, Y.; Li, Y.; Yang, L.; Peng, X.B.; Sreenath, K. Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning. In Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 1–5 October 2023. [CrossRef] 38. Katz, B.; Carlo, J.D.; Kim, S. Mini Cheetah: A Platform for Pushing the Limits of Dynamic Quadruped Control. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 6295–6301. 39. Wise, M.; Ferguson, M.; King, D.; Diehr, E.; Dymesich, D. Fetch and Freight: Standard Platforms for Service Robot Applications. In Proceedings of the Workshop on Autonomous Mobile Service Robots, New York, NY, USA, 9–15 July 2016. Available online: https://docs.fetchrobotics.com/FetchAndFreight2016.pdf (accessed on 21 August 2024). |
