Abstract
In today’s modern and developing world, security and privacy are essential ingredients for ensuring data safety and the legitimate access of one’s information for most of the real-time applications they utilize, be it using smartphones, laptops, tablets, or electronic gadgets which are connected through the Internet thus making it an easy target to leverage the security of that device, resulting in enabling the attackers getting access to the sensitive and confidential data of the individual or organization. With the progression of technology at such a rapid pace, it may be frequent to conclude that drones will be delivering goods and merchandise, thus catering to the accessibility of mobile hotspots and ensuring the security & surveillance of smart cities. Considering the long-term utility of drones for smart cities, there also comes the threat of cyber-attacks like Deauthentication Attacks, GPS Spoofing, etc., which will lead to the disclosure of sensitive information. The smart devices consist of various embedded SoCs (System-On-Chip), which are integrated to sustain a large amount of user data by focusing primarily on avoiding the trade-off between the complexity of the machine learning implemented model and the available compatible edge devices (Hardware SoCs). Thus, it is essential to enhance the security of edge devices on a large scale, specifically from the perspective of smart cities. Several researchers have also proposed methodologies to improve and sustain the security of smart devices using optimized blockchain-based security frameworks using physical parameters like temperature, light, etc. This chapter defines an insight towards ensuring the security (focuses majorly on the Edge computing devices) of the smart devices, which are the prime source to enhance and maximize privacy, thus enabling the smart cities to be more secure from any cyberattack.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vattapparamban, E., Gven, A., Yurekli, K. A., & Uluaa, S. (2016). Drones for smart cities: Issues in cybersecurity, privacy, and public safety. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 216–221).
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.
Zhang, Y., Zheng, D., & Deng, R. H. (2018). Security and privacy in smart health: Efficient policy-hiding attribute-based access control. IEEE Internet of Things Journal, 5(3), 2130–2145.
Li, Y., Dai, W., Ming, Z., & Qiu, M. (2016). Privacy protection for preventing data over-collection in smart city. IEEE Transactions on Computers, 65(5), 1339–1350.
Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys Tutorials, 19(4), 2456–2501.
Eckhoff, D., & Wagner, I. (2018). Privacy in the smart city applications, technologies, challenges, and solutions. IEEE Communications Surveys Tutorials, 20(1), 489–516.
Shen, M., Tang, X., Zhu, L., Du, X., & Guizani, M. (2019). Privacy-preserving support vector machine training over blockchain-based encrypted iot data in smart cities. IEEE Internet of Things Journal, 6(5), 7702–7712.
Rahman, M. A., Rashid, M. M., Hossain, M. S., Hassanain, E., Alhamid, M. F., & Guizani, M. (2019). Blockchain and iot-based cognitive edge framework for sharing economy services in a smart city. IEEE Access, 7, 18611–18621.
Jose, A. C., & Malekian, R. (2017, July 1). Improving smart home security: Integrating logical sensing into smart home. IEEE Sensors Journal, 17(13), 4269–4286.
Biswas, K., & Muthukkumarasamy, V. (2016). Securing smart cities using blockchain technology. In 2016 IEEE 18th international conference on high performance computing and communications; IEEE 14th international conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 1392–1393).
Khan, P. W., Byun, Y.-C., & Park, N. (2020). A data verification system for CCTV surveillance cameras using blockchain technology in smart cities. Electronics, 9(3), 484.
Khan, L. U., Yaqoob, I., Tran, N. H., Kazmi, S. M. A., Dang, T. N., & Hong, C. S. (2020). Edge-computing-enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal, 7(10), 10200–10232.
Tan, S., De, D., Song, W.-Z., Yang, J., & Das, S. K. (2017). Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys & Tutorials, 19(1), 397–422.
Cui, L., Xie, G., Qu, Y., Gao, L., & Yang, Y. (2018). Security and privacy in smart cities: Challenges and opportunities. IEEE Access, 6, 46134–46145.
Podder, P., Mondal, M. R. H., Bharati, S., & Paul, P. K. (2020, July). Review on the security threats of internet of things. International Journal of Computer Applications, 176, 37–45.
Husamuddin, M., & Qayyum, M. (2017). Internet of things: A study on security and privacy threats. In 2017 2nd International Conference on Anti-Cyber Crimes (ICACC) (pp. 93–97).
Toh, C. K. (2020, July). Security for smart cities. IET Smart Cities, 2(9), 95–104.
https://www.kaggle.com/datasets/elikplim/forest-fires-data-set
Mukhopadhyay, D., Iyer, R., Kadam, S., & Koli, R. (2019). Fpga deployable fire detection model for real-time video surveillance systems using convolutional neural networks. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1–7).
Chollet, F. et al. (2015). Keras. https://keras.io
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
Moujahid, A., ElAraki Tantaoui, M., Hina, M. D., Soukane, A., Ortalda, A., ElKhadimi, A., & Ramdane-Cherif, A. (2018). Machine learning techniques in ADAS: A review. In 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE) (pp. 235–242).
Borrego-Carazo, J., Castells-Rufas, D., Biempica, E., & Carrabina, J. (2020). Resource-constrained machine learning for ADAS: A systematic review. IEEE Access, 8, 40573–40598.
Yu, B., Hu, W., Xu, L., Tang, J., Liu, S., & Zhu, Y. (2020). Building the computing system for autonomous micromobility vehicles: Design constraints and architectural optimizations. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) (pp. 1067–1081).
Pansari, N., & Agarwal, A. (2020). A comparative study of analysis and investigation using digital forensics. International Journal of Linguistics and Computational Applications (IJLCA), 7(2), 16–20.
Pansari, N., & Kushwaha, D. (2018). Advancement in robust cyber attacks-an overview. International Journal of Research in Engineering, IT and Social Sciences, 8(Special Issue), 113–119.
Deng, Y., Zhang, T., Lou, G., Zheng, X., Jin, J., & Han, Q.-L. (2021). Deep learning-based autonomous driving systems: A survey of attacks and defenses. IEEE Transactions on Industrial Informatics, 17(12), 7897–7912.
Pansari, N., & Kushwaha, D. (2019). Forensic analysis and investigation using digital forensics-an overview. International Journal of Advance Research, Ideas and Innovations in Technology, 5, 191.
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv, 1412.6572.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 8024–8035.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255).
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv, 1602.07360.
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848–6856).
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510–4520).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 25). Curran Associates, Inc..
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 1409.1556.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Pansari, N., Saiya, R. (2023). Reliability and Security of Edge Computing Devices for Smart Cities. In: Ahad, M.A., Casalino, G., Bhushan, B. (eds) Enabling Technologies for Effective Planning and Management in Sustainable Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-031-22922-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-22922-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22921-3
Online ISBN: 978-3-031-22922-0
eBook Packages: Computer ScienceComputer Science (R0)