Artificial Intelligence of Things (AIoT): A Systematic Review of Smart Applications
Pages
86-106Keywords:
Abstract
With the rapid growth of the Internet of Things (IoT), ever greater quantities of complex data are created. To analyze this data and take better decisions, Artificial Intelligence (AI) especially in the form of Machine Learning (ML) and Deep Learning (DL) has been widely adopted, which has led to the rise of Artificial Intelligence of Things (AIoT). This study is a systematic literature review consisting of 79 peer-reviewed articles published in the last five years (from 2019 to 2024), following the PRISMA guidelines. We thoroughly searched major databases, and used strict quality assessment and inclusion/exclusion criteria for assessing the influence of the AI in five fields, namely, Smart Home, Smart Transportation, Smart Education, Smart Grid, and Robotics. Based on our quantitative results, we notice that the most studied domain is the Smart Transportation, and the most used technologies are recurrent models such as LSTM and convolutional networks (CNN). The review compares the performance of the models critically, by considering the evaluation metrics including accuracy, latency, and computational cost. Moreover, the paper discusses key challenges and limitations in the existing AIoT deployments such as data privacy, cybersecurity threats, power consumption and explainability of the models. This review aims to fill the gap of literature by providing a structured reference towards future research on scalable, secure and energy-efficient AIoT systems.
References
- J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, Sept. 2013.
- J. Zhang and D. Tao, “Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things,” IEEE Internet of Things Journal, vol. 8, no. 10, pp. 7789–7817, 2021.
- S. D. Tiwari, “Smart home automation based on iot and machine learning,” International Journal of Computer Applications, vol. 178, no. 9, pp. 1–5, Mar. 2019.
- A. Ferreira, P. Leitão, and P. Vrba, “Challenges of ict and artificial intelligence in smart grids,” in 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES), 2014, pp. 6–11.
- Vostroknutov, S. Grigoriev, and L. Surat, “Search for a new paradigm of education and artificial intelligence. place and role of artificial intelligence in the new education system,” in 2021 1st International Conference on Technology Enhanced Learning in Higher Education (TELE), 2021, pp. 80–82.
- K. S. Awaisi, Q. Ye, and S. Sampalli, "A Survey of Industrial AIoT: Opportunities, Challenges, and Directions," IEEE Access, vol. 12, pp. 96946-96996, 2024, doi: 10.1109/ACCESS.2024.3426279.
- S. H. Rafique, A. Abdallah, N. S. Musa, and T. Murugan, “Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends,” Sensors, vol. 24, no. 6, p. 1968, 2024.
- L. Deng and D. Yu, “Deep learning: Methods and applications,” Foundations and Trends® in Signal Processing, vol. 7, no. 3-4, pp. 197–387, June 2014.
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.
- S. A. Fattah and H. Xu, “Challenges in iot data security and privacy,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 938–944, Feb. 2019.
- B. Ślusarczyk, “Industry 4.0–are we ready?” Polish Journal of Management Studies, vol. 17, no. 1, pp. 232–248, 2018.
- A. K. Salama and M. M. Abdellatif, "AIoT-based smart home energy management system," in 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), IEEE, 2022.
- P. Poonia and V. K. Jain, "Short-term traffic flow prediction: Using LSTM," in 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), IEEE, 2020.
- P. Kumar, A. Aljuhani, D. Javeed, A. Jolfaei, and A. Islam, "Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity," Solar Energy, vol. 263, p. 111921, 2023, doi: 10.1016/j.solener.2023.111921.
- Y. Wang, Z. Gao, J. Zhang, X. Cao, D. Zheng, and Y. Gao, “Trajectory design for UAV-based internet of things data collection: A deep reinforcement learning approach,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3899–3912, Mar. 2022.
- W. Zhang et al., “A deep reinforcement learning based UAV trajectory planning method for integrated sensing and communications networks,” in 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023.
- R. W. Anwar, A. Ismael, and K. N. Qureshi, Advanced AIoT Applications and Services, 1st ed. CRC Press, 2024.
- E. Kanjo, E. M. G. Younis, and C. S. Ang, “Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection,” Information Fusion, vol. 49, pp. 46–56, 2019.
- X. Gao, H. Luo, Q. Wang, F. Zhao, L. Ye, and Y. Zhang, “A human activity recognition algorithm based on stacking denoising autoencoder and lightgbm,” Sensors, vol. 19, no. 4, pp. 947–957, 2019.
- R. D. Manu, S. Kumar, and S. Snehashish, “Smart home automation using iot and deep learning,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 3, pp. 123–130, 2020.
- R. Tang and Y. Inoue, “Services on platform ecosystems in the smart home 2.0 era: Elements influencing consumers’ value perception for smart home products,” Sensors, vol. 21, no. 7391, 2021.
- E. Struckell, D. K. Ojha, P. C. Patel, and A. Dhir, “Ecological determinants of smart home ecosystems: A coopetition framework,” Technological Forecasting and Social Change, vol. 173, no. 121147, 2021.
- D. J. Langley, J. van Doorn, I. C. L. Ng, S. Stieglitz, A. Lazovik, and A. Boonstra, “The internet of everything: Smart things and their impact on business models,” Journal of Business Research, vol. 122, pp. 853–863, 2021.
- R. Patel and T. Nguyen, “Optimizing smart home iot data transmission with machine learning,” IEEE Access, vol. 11, pp. 5432–5442, 2023.
- W. A. Alonazi, H. Hamdi, N. A. Azim, and A. A. Abd El-Aziz, “Sdn architecture for smart homes security with machine learning and deep learning,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 10, pp. 112–120, 2022.
- A. Kumar and S. Sharma, “Smart home security enhancement using deep reinforcement learning,” IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 112–124, 2023.
- M. Lee and J. Kim, “Real-time anomaly detection in smart home iot networks using deep learning,” Journal of Network and Computer Applications, vol. 148, pp. 85–97, 2023.
- N. Karmous, Y. B. Dhiab, M. O. Aoueileyine, N. Youssef, R. Bouallegue, and A. Yazidi, “Deep learning approaches for protecting iot devices in smart homes from mitm attacks,” Frontiers in Computer Science, vol. 6, no. 128, pp. 112–120, 2024.
- R. Allafi and I. R. Alzahrani, “Enhancing cybersecurity in the internet of things environment using artificial orca algorithm and ensemble learning model,” IEEE Access, vol. 12, pp. 63282–63291, 2024.
- N. S. Alotaibi, H. I. S. Ahmed, S. O. M. Kamel, and G. F. ElKabbany, “Secure enhancement for mqtt protocol using distributed machine learning framework,” Sensors, vol. 24, no. 5, pp. 1638–1648, 2024.
- M. S. Aliero, K. N. Qureshi, M. F. Pasha, and G. Jeon, “Non-intrusive room occupancy prediction performance analysis using different machine learning techniques,” Energies, vol. 15, no. 23, p. 9231, 2022.
- X. Luo, D. Li, Y. Yang, and S. Zhang, “Spatiotemporal traffic flow prediction with knn and lstm,” Journal of Advanced Transportation, vol. 2019, p. 4145353, 2019.
- X. Wang, L. Xu, and K. Chen, “Data-driven short-term forecasting for urban road network traffic based on data processing and lstm-rnn,” Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3043–3060, 2019.
- S. Zhang, Y. Yao, J. Hu, Y. Zhao, S. Li, and J. Hu, “Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks,” Sensors, vol. 19, no. 14, p. 3052, 2019.
- X. Ma, H. Yu, Y. Wang, and Y. Wang, “Large-scale transportation network congestion evolution prediction using deep learning theory,” PloS One, vol. 10, 2019.
- Z. Li, G. Xiong, Y. Chen, Y. Lv, B. Hu, F. Zhu, and F. Y. Wang, "A hybrid deep learning approach with GCN and LSTM for traffic flow prediction," in Proc. 2019 IEEE Intell. Transp. Syst. Conf. (ITSC), Auckland, New Zealand, 2019, pp. 1929–1933, doi: 10.1109/ITSC.2019.8916778.
- B. Yang, S. Sun, J. Li, X. Lin, and Y. Tian, "Traffic flow prediction using LSTM with feature enhancement," Neurocomputing, vol. 332, pp. 320–327, Mar. 2019, doi: 10.1016/j.neucom.2018.12.016.
- Y. Zhang, "Short-term traffic flow prediction methods: A survey," Journal of Physics Conference Series, vol. 1486, no. 5, p. 052018, Apr. 2020, doi: 10.1088/1742-6596/1486/5/052018.
- X. Yin, G. Wu, J. Wei, Y. Shen, H. Qi, and B. Yin, "Deep learning on traffic prediction: Methods, analysis, and future directions," IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 4927–4943, Jun. 2022 (Published online 2021), doi: 10.1109/TITS.2021.3054840.
- B. Feng, J. Xu, Y. Zhang, and Y. Lin, “Multi-step traffic speed prediction based on ensemble learning on an urban road network,” Applied Sciences, vol. 11, no. 4423, 2021.
- W. Zhuang and Y. Cao, “Short-term traffic flow prediction based on cnn-bilstm with multicomponent information,” Applied Sciences, vol. 12, no. 8714, 2022.
- R. Shi and L. Du, “Multi-section traffic flow prediction based on mlr-lstm neural network,” Sensors, vol. 22, no. 7517, 2022.
- N. U. Khan, M. A. Shah, C. Maple, E. Ahmed, and N. Asghar, “Traffic flow prediction: An intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble,” Sustainability, vol. 14, no. 4164, 2022.
- W. Chai, Y. Zheng, L. Tian, J. Qin, and T. Zhou, “Ga-kelm: Genetic-algorithm-improved kernel extreme learning machine for traffic flow forecasting,” Mathematics, vol. 11, no. 3574, 2023.
- H. Yang, L. Du, G. Zhang, and T. Ma, “A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction,” Transportation Research Part B: Methodological, 2023.
- S. Alsubai, A. K. Dutta, and A. R. W. Sait, “Hybrid deep learning-based traffic congestion control in iot environment using enhanced arithmetic optimization technique,” Alexandria Engineering Journal, vol. 105, pp. 331–340, 2024.
- X. Wang, K. Xie, K. Huang, J. Zeng, and Z. Cai, “Deep reinforcement learning-based traffic signal control using high-resolution event-based data,” Entropy, vol. 21, no. 8, p. 744, 2019.
- X. Liang, X. Du, G. Wang, and Z. Han, “A deep reinforcement learning network for traffic light cycle control,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1243–1253, Feb. 2019.
- W. Genders and S. Razavi, “Asynchronous n-step q-learning adaptive traffic signal control,” Journal of Intelligent Transportation Systems, vol. 23, no. 4, pp. 319–331, 2019.
- M. Hassan et al., “Smart city intelligent traffic control for connected road junction congestion awareness with deep extreme learning machine,” in Proc. 2022 Int. Conf. on Business Analytics for Technology and Security (ICBATS), 2022, pp. 1–4.
- S. Oza and S. Rathod, “Object detection using iot and machine learning to avoid accident and improve road safety,” International Journal of Engineering Research and Technology (IJERT), vol. 9, no. 6, pp. 640–647, June 2020.
- N. Pathik et al., “Ai enabled accident detection and alert system using iot and deep learning for smart cities,” Sustainability, vol. 14, no. 13, p. 7701, June 2022.
- S. Uma and R. Eswari, “Accident prevention and safety assistance using iot and machine learning,” Journal of Reliable Intelligent Environments, vol. 8, pp. 79–103, 2022.
- W. Xu et al., “Energy harvesting-based smart transportation mode detection system via attention-based lstm,” IEEE Access, vol. 7, pp. 66423–66434, 2019.
- X. Geng et al., “Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, pp. 1–8.
- J. Li et al., “A traffic prediction enabled double rewarded value iteration network for route planning,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4170–4181, May 2019.
- R. Kariapper, “Attendance system using rfid, iot and machine learning: A two-factor verification approach,” Systematic Reviews in Pharmacy, vol. 12, no. 3, pp. 314–321, 2021.
- F. Benita, D. Virupaksha, and E. Wilhelm, “A smart learning ecosystem design for delivering data-driven thinking in stem education,” Smart Learning Environments, vol. 8, no. 11, 2021.
- F. Alahmari, A. Naim, and H. Alqahtani, “E-learning modeling technique and convolution neural networks in online education,” in IoT-Enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023, pp. 261–295.
- S. Qiu, “Improving performance of smart education systems by integrating machine learning on edge devices and cloud in educational institutions,” Journal of Grid Computing, vol. 22, no. 41, pp. 1–15, 2024.
- R. Meylani, “Transforming education with the internet of things: A journey into smarter learning environments,” International Journal of Research in Education and Science, vol. 10, no. 1, pp. 161–178, 2024.
- A. Alam, “Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: Reforming education in the age of artificial intelligence,” in Advanced Computing and Intelligent Technologies, R. Shaw, Ed. Springer, 2022, pp. 395–406.
- Y.-H. Hu, J. Fu, and H.-C. Yeh, “Developing an early-warning system through robotic process automation: Are intelligent tutoring robots as effective as human teachers?” Interactive Learning Environments, vol. 32, no. 6, pp. 2803–2816, 2023.
- A. Ni and A. Cheung, “Understanding secondary students’ continuance intention to adopt ai-powered intelligent tutoring system for english learning,” Education and Information Technologies, vol. 28, no. 3, pp. 3191–3216, 2023.
- J. Niyogisubizo, L. Liao, E. Nziyumva, E. Murwanashyaka, and P. Nshimyumukiza, “Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization,” Computers, Education and Artificial Intelligence, vol. 3, 2021.
- K. Sixhaxa, A. Jadhav, and R. Ajoodha, “Predicting students’ performance in exams using machine learning techniques,” in Proceedings of the 2022 12th International Conference on Cloud Computing and Data Science Engineering (Conflu), 2022, pp. 635–640.
- A. Alhothali, M. Albsisi, H. Assalahi, and T. Aldosemani, “Predicting student outcomes in online courses using machine learning techniques: A review,” Sustainability, vol. 14, no. 10, pp. 1–23, 2022.
- G. Hafeez et al., “A novel accurate and fast converging deep learning-based model for electrical energy consumption forecasting in a smart grid,” Energies, vol. 13, no. 9, p. 2244, May 2020.
- M. Babar, M. U. Tariq, and M. A. Jan, “Secure and resilient demand side management engine using machine learning for iot-enabled smart grid,” Sustainable Cities and Society, vol. 62, 2020.
- P. Pawar, M. TarunKumar, and P. K. Vittal, “An iot based intelligent smart energy management system with accurate forecasting and load strategy for renewable generation,” Measurement, vol. 152, pp. 107–187, 2020.
- R. Eini, L. Linkous, N. Zohrabi, and S. Abdelwahed, “Smart building management system: Performance specifications and design requirements,” Journal of Building Engineering, vol. 39, p. 102222, 2021.
- S. Li et al., “Electricity theft detection in power grids with deep learning and random forests,” Journal of Electrical and Computer Engineering, vol. 2019, p. 4136874, Oct. 2019.
- L. Haghnegahdar and Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural Computing and Applications, vol. 32, no. 13, pp. 9427–9441, Jul. 2020.
- S. Dharmadhikari et al., “A smart grid incorporated with ml and iot for a secure management system,” Microprocessors and Microsystems, vol. 83, 2021.
- S. Liu et al., “A data-driven and data-based framework for online voltage stability assessment using partial mutual information and iterated random forest,” Energies, vol. 14, no. 3, p. 715, Jan. 2021.
- A. Alaerjan, R. Jabeur, H. B. Chikha, M. Karray, and M. Ksantini, “Improvement of smart grid stability based on artificial intelligence with fusion methods,” Symmetry, vol. 16, no. 4, p. 459, 2024.
- M. M. Dar Oghaz, M. Razaak, H. Kerdegari, V. Argyriou, and P. Remagnino, “Scene and environment monitoring using aerial imagery and deep learning,” in Proc. Int. Conf. Distrib. Comput. Sens. Syst., 2019, pp. 362–369.
- M. Samir, C. Assi, S. Sharafeddine, D. Ebrahimi, and A. Ghrayeb, “Age Of Information Aware Trajectory Planning Of UAVs In Intelligent Transportation Systems: A Deep Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12382–12395, Nov. 2020.
- M. Wazid, B. Bera, A. Mitra, A. K. Das, and R. Ali, “Private blockchain-envisioned security framework for AI-enabled IOT-based drone-aided healthcare services,” in Proc. ACM MobiCom Workshop Drone Assist. Wireless Commun. 5G Beyond, 2020, pp. 37–42.
- R. Gupta et al., “Vahak: A Blockchain-Based Outdoor Delivery Scheme Using UAV For Healthcare 4.0 Services,” in Proc. IEEE Conf. Comput. Commun. Workshops, 2020, pp. 255–260.
- S. P. Ardakani and A. Cheshmehzangi, “Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming,” Telecom, vol. 2, no. 3, pp. 255–270, 2021.
- C. Rottondi, F. Malandrino, A. Bianco, C. F. Chiasserini, and I. Stavrakakis, “Scheduling of Emergency Tasks for Multi-Service UAVS In Post-Disaster Scenarios,” Computer Networks, vol. 184, p. 107644, Jan. 2021.
- X. Chen, X. Liu, Y. Chen, L. Jiao, and G. Min, “Deep Q-Network Based Resource Allocation for Uav-Assisted Ultra-Dense Networks,” Computer Networks, vol. 196, p. 108249, Sep. 2021.
- R. Sugano, R. Shinkuma, T. Nishio, S. Itahara, and N. B. Mandayam, “Watch from Sky: Machine-Learning-Based Multi-UAV Network for Predictive Police Surveillance,” arXiv:2203.02892, 2022.
- W. J. Yun et al., “Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance Via Autonomous Multi-UAV Control,” IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7086–7096, Oct. 2022.
- H. Bi, J. Liu, and N. Kato, “Deep Learning-Based Privacy Preservation and Data Analytics For IOT Enabled Healthcare,” IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4798–4807, Jul. 2022.
- A. Saleem, K. Al Jabri, A. Al Maashri, et al., "Obstacle-Avoidance Algorithm Using Deep Learning Based on RGBD Images and Robot Orientation," in Proc. 2020 International Conference on Electrical and Electronic Engineering (ICEEE), 2020, doi: 10.1109/ICEEE49618.2020.9102526.
- M. M. Ajaykumar, R. M. Jose, and S. Khan, “Automated operating room cleaning robot,” International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, vol. 5, no. 04, April 2021.
- A. Joon and W. Kowalczyk, “Design of autonomous mobile robot for cleaning in the environment with obstacles,” Appl. Sci., vol. 11, p. 8076, 2021.
- M. F. R. Lee and S. H. Yusuf, “Mobile Robot Navigation Using Deep Reinforcement Learning,” Processes, vol. 10, no. 12, p. 2748, Dec. 2022, doi: 10.3390/pr10122748.
- U. Khalid et al., “Smart floor cleaning robot (clear),” Faculty of Electronic Engineering, GIK Institute, Tech. Rep., 2023.
- M. Kaur and P. Abrol, “Design and development of floor cleaner robot (automatic and manual),” International Journal of Computer Applications, vol. 97, no. 19, July 2023.
- R. Haeb-Umbach et al., "Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques," IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 111-124, Oct. 2019, doi: 10.1109/MSP.2019.2918706.
- U. Khalid et al., “Voice-controlled IoT system with natural language processing and machine learning,” Faculty of Electronic Engineering, GIK Institute, Tech. Rep., 2021.
- X. Sun, J. Fu, B. Wei, et al., “A Self-Attentional ResNet-LightGBM Model for IoT-Enabled Voice Liveness Detection,” IEEE Internet of Things Journal, vol. 10, no. 9, pp. 7832–7845, May 2023, doi: 10.1109/JIOT.2022.3230992.
Identifiers
Download this PDF file
Statistics
How to Cite
Copyright and Licensing

This work is licensed under a Creative Commons Attribution 4.0 International License.





