Smart City Aided by AIoT

Authors

  • RONAK SIGHATWADIA Dept. of Computer Engineering MPSTME,NMIMS UNIVERSITY Mumbai, India
  • KARTIK SHARMA Dept. of Computer Engineering MPSTME,NMIMS UNIVERSITY Mumbai, India

Keywords:

Artificial Intelligence (AI), Smart City, Big Data Processing, Smart Parking System, Environment Monitoring

Abstract

With growing development and advancement in the field of technology, smart cities are equipped with several electronic devices like cameras and other sensors. In many places Internet of Things (IoT), is being used to take advantage of available devices but still lacks efficiency. In this paper, we will discuss, how integration of AI and IoT will make smart cities even smarter, the paper also lists some practical applications of AIoT in smart city and the challenges that may arise while implementing artificial intelligence and internet of things in smart cities

References

Mohammadi, M., Al-Fuqaha, A., Guizani, M. and Oh, J.S., 2017. Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet of Things Journal, 5(2), pp.624-635.

Xhafa, F., Barolli, L., Barolli, A. and Papajorgji, P., 2015. Modeling and processing for next-generation Big-Data technologies. Cham: Springer International Publishing.

Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-khah, M. and Siano, P., 2016, June. Iot-based smart cities: A survey. In 2016 IEEE 16th international conference on environment and electrical engineering (EEEIC) (pp. 1-6). IEEE.

Bano, A., Ud Din, I. and Al-Huqail, A.A., 2020. AIoT-based smart bin for real-time monitoring and management of solid waste. Scientific Programming, 2020.

Hammi, B., Khatoun, R., Zeadally, S., Fayad, A. and Khoukhi, L., 2018. IoT technologies¡? show [AQ ID= Q1]?¿ for smart cities. IET networks, 7(1), pp.1-13.

Burnett, R.T., Pope III, C.A., Ezzati, M., Olives, C., Lim, S.S., Mehta, S., Shin, H.H., Singh, G., Hubbell, B., Brauer, M. and Anderson, H.R., 2014. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environmental health perspectives, 122(4), pp.397-403.

Tragos, E.Z., Angelakis, V., Fragkiadakis, A., Gundlegard, D., Nechi- for, C.S., Oikonomou, G., Po¨hls, H.C. and Gavras, A., 2014, March. Enabling reliable and secure IoT-based smart city applications. In 2014 IEEE International Conference on Pervasive Computing and Communi- cation Workshops (PERCOM WORKSHOPS) (pp. 111-116). IEEE.

Mohammadi, M., Al-Fuqaha, A., Sorour, S. and Guizani, M., 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys Tutorials, 20(4), pp.2923-2960.

Merenda, M., Porcaro, C. and Iero, D., 2020. Edge machine learning for ai-enabled iot devices: A review. Sensors, 20(9), p.2533.

Zhang, X., Hu, M., Xia, J., Wei, T., Chen, M. and Hu, S., 2020. Efficient federated learning for cloud-based AIoT applications. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 40(11), pp.2211-2223.

Dia, I., Ahvar, E. and Lee, G.M., 2022. Performance Evaluation of Machine Learning and Neural Network-Based Algorithms for Predicting Segment Availability in AIoT-Based Smart Parking. Network, 2(2), pp.225-238.

Dia, I., Ahvar, E. and Lee, G.M., 2022. Performance Evaluation of Machine Learning and Neural Network-Based Algorithms for Predicting Segment Availability in AIoT-Based Smart Parking. Network, 2(2), pp.225-238.

Thakur, A., Aich, S. and Kumar, R., 2022. A Structural Approach on Enablers of IoT for Sustainable Development of Smart Cities.

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Published

31-12-2022

How to Cite

RONAK SIGHATWADIA, & KARTIK SHARMA. (2022). Smart City Aided by AIoT. International Journal for Research Publication and Seminar, 13(5), 66–70. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/246

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Section

Original Research Article