Smart City Gnosys

Smart city article details

Title	An Adaptive Explainable Ai Framework For Securing Consumer Electronics-Based Iot Applications In Fog-Cloud Infrastructure
ID_Doc	7373
Authors	Tripathy S.S.; Guduri M.; Chakraborty C.; Bebortta S.; Pani S.K.; Mukhopadhyay S.
Year	2025
Published	IEEE Transactions on Consumer Electronics, 71, 1
DOI	http://dx.doi.org/10.1109/TCE.2024.3424189
Abstract	A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sensitive data. Yet, these devices commonly utilize public channels for data access and sharing, requiring consistent communication protocols and an Intrusion Detection System (IDS) with the aid of AI. However, most of them involve high computation and communication costs. They are not fully reliable, either. Also, AI-based IDS solutions are viewed as black boxes because they cannot justify their decisions. To resolve these issues, we have proposed a framework based on explainable artificial intelligence (XAI) for securing consumer IoT applications in smart cities. At the beginning of the protocol execution, the participants exchange authenticated data through the blockchain-based AKA procedure. Meanwhile, we adopt the Python-based Shapley Additive Explanation (SHAP) framework to explain and interpret the core features guiding decision-making. The working model of this framework depicts its validation with recent benchmark methods. © 1975-2011 IEEE.
Author Keywords	consumer electronic devices; deep reinforcement learning; energy efficiency; Explainable AI; fog computing; Internet of Things; latency; task offloading

Similar Articles

Id	Similarity	Authors	Title	Published
12582	0.918	Kumar R.; Javeed D.; Aljuhani A.; Jolfaei A.; Kumar P.; Islam A.K.M.N.	Blockchain-Based Authentication And Explainable Ai For Securing Consumer Iot Applications	IEEE Transactions on Consumer Electronics, 70, 1 (2024)
53141	0.885	Mohammad A.A.S.; Mohammad S.I.S.; Al Oraini B.; Vasudevan A.; Hindieh A.; Altarawneh A.; Alshurideh M.T.; Ali I.	Strategies For Applying Interpretable And Explainable Ai In Real World Iot Applications	Discover Internet of Things, 5, 1 (2025)
19455	0.874	Celik A.F.; Saglam B.; Demirci S.	Developing Explainable Intrusion Detection Systems For Internet Of Things	16th International Conference on Information Security and Cryptology, ISCTURKEY 2023 - Proceedings (2023)
31926	0.863	Khan B.U.I.; Goh K.W.; Khan A.R.; Zuhairi M.F.; Chaimanee M.	Integrating Ai And Blockchain For Enhanced Data Security In Iot-Driven Smart Cities	Processes, 12, 9 (2024)
25365	0.863	Kabir M.H.; Hasan K.F.; Hasan M.K.; Ansari K.	Explainable Artificial Intelligence For Smart City Application: A Secure And Trusted Platform	Studies in Computational Intelligence, 1025 (2022)
4502	0.86	Ayub K.; Alshawa R.	A Secure Iot Framework For Smart Cities: Integrating Servicenow Irm/Grc With Blockchain And Ai-Driven Threat Detection	2024 International Conference on Computer and Applications, ICCA 2024 (2024)
12508	0.857	Ajao L.A.; Apeh S.T.	Blockchain Integration With Machine Learning For Securing Fog Computing Vulnerability In Smart City Sustainability	1st International Conference in Advanced Innovation on Smart City, ICAISC 2023 - Proceedings (2023)
7065	0.856	Fares N.Y.; Nedeljkovic D.; Jammal M.	Ai-Enabled Iot Applications: Towards A Transparent Governance Framework	2023 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2023 (2023)
36080	0.856	Ahanger T.A.; Ullah I.; Algamdi S.A.; Tariq U.	Machine Learning-Inspired Intrusion Detection System For Iot: Security Issues And Future Challenges	Computers and Electrical Engineering, 123 (2025)