{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:16:10Z","timestamp":1770963370186,"version":"3.50.1"},"reference-count":191,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Background: Explainable Artificial Intelligence (XAI) is deployed in Internet of Things (IoT) ecosystems for smart cities and precision agriculture, where opaque models can compromise trust, accountability, and regulatory compliance. Objective: This survey investigates how XAI is currently integrated into distributed and federated IoT architectures and identifies systematic gaps in evaluation under real-world resource constraints. Methods: A structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar targeted publications related to XAI, IoT, edge\/fog computing, smart cities, smart agriculture, and federated learning. Relevant peer-reviewed works were synthesized along three dimensions: deployment tier (device, edge\/fog, cloud), explanation scope (local vs. global), and validation methodology. Results: The analysis reveals a persistent resource\u2013interpretability gap: computationally intensive explainers are frequently applied on constrained edge and federated platforms without explicitly accounting for latency, memory footprint, or energy consumption. Only a minority of studies quantify privacy\u2013utility effects or address causal attribution in sensor-rich environments, limiting the reliability of explanations in safety- and mission-critical IoT applications. Contribution: To address these shortcomings, the survey introduces a hardware-centric evaluation framework with the Computational Complexity Score (CCS), Memory Footprint Ratio (MFR), and Privacy\u2013Utility Trade-off (PUT) metrics and proposes a hierarchical IoT\u2013XAI reference architecture, together with the conceptual Internet of Things Interpretability Evaluation Standard (IOTIES) for cross-domain assessment. Conclusions: The findings indicate that IoT\u2013XAI research must shift from accuracy-only reporting to lightweight, model-agnostic, and privacy-aware explanation pipelines that are explicitly budgeted for edge resources and aligned with the needs of heterogeneous stakeholders in smart city and agricultural deployments.<\/jats:p>","DOI":"10.3390\/fi18020083","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T10:02:57Z","timestamp":1770199377000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Next-Gen Explainable AI (XAI) for Federated and Distributed Internet of Things Systems: A State-of-the-Art Survey"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-6511","authenticated-orcid":false,"given":"Aristeidis","family":"Karras","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9413-8841","authenticated-orcid":false,"given":"Anastasios","family":"Giannaros","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4581-8247","authenticated-orcid":false,"given":"Natalia","family":"Amasiadi","sequence":"additional","affiliation":[{"name":"Department of Public Health, School of Medicine, University of Patras, 26500 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-7661","authenticated-orcid":false,"given":"Christos","family":"Karras","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alotaibi, A., Aldawghan, H., and Aljughaiman, A. (2025). A review of the authentication techniques for internet of things devices in smart cities: Opportunities, challenges, and future directions. Sensors, 25.","DOI":"10.3390\/s25061649"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/COMST.2025.3541165","article-title":"Identification techniques in the internet of things: Survey, taxonomy and research frontier","volume":"28","author":"Saadouni","year":"2025","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s43926-024-00083-4","article-title":"Integrating large language models with internet of things: Applications","volume":"5","author":"Zong","year":"2025","journal-title":"Discov. Internet Things"},{"key":"ref_4","first-page":"8455","article-title":"Internet of things technology, research, and challenges: A survey","volume":"84","author":"Vishwakarma","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., \u0141obodzi\u0144ska, A., Soko\u0142owska, S., and Nowy, A. (2025). Integrating artificial intelligence agents with the internet of things for enhanced environmental monitoring: Applications in water quality and climate data. Electronics, 14.","DOI":"10.3390\/electronics14040696"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s00521-024-10437-2","article-title":"Unlocking the black box: An in-depth review on interpretability, explainability, and reliability in deep learning","volume":"37","author":"Arslan","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3527448","article-title":"Explainable deep reinforcement learning: State of the art and challenges","volume":"55","author":"Vouros","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1007\/s13347-021-00477-0","article-title":"Transparency and the black box problem: Why we do not trust AI","volume":"34","year":"2021","journal-title":"Philos. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kalmykov, V.L., and Kalmykov, L.V. (2024). XXAI: Towards Explicitly Explainable Artificial Intelligence. arXiv.","DOI":"10.1016\/j.inffus.2025.103352"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bie, Y., Luo, L., Chen, Z., and Chen, H. (2024). XCoOP: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-Guided Context Optimization. arXiv.","DOI":"10.1007\/978-3-031-72390-2_72"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Phillips, P.J., Hahn, C.A., Fontana, P.C., Yates, A.N., Greene, K., Broniatowski, D.A., and Przybocki, M.A. (2021). Four Principles of Explainable Artificial Intelligence, NIST Interagency\/Internal Report (NISTIR).","DOI":"10.6028\/NIST.IR.8312"},{"key":"ref_12","first-page":"98","article-title":"Smart Agriculture System Using IoT Technology","volume":"7","author":"Naresh","year":"2019","journal-title":"Int. J. Recent Technol. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1109\/OJCOMS.2022.3215676","article-title":"Explainable AI over the Internet of Things (IoT): Overview, state-of-the-art and future directions","volume":"3","author":"Jagatheesaperumal","year":"2022","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/3387166","article-title":"A multidisciplinary survey and framework for design and evaluation of explainable AI systems","volume":"11","author":"Mohseni","year":"2021","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lopes, P., Silva, E., Braga, C., Oliveira, T., and Rosado, L. (2022). XAI systems evaluation: A review of human and computer-centred methods. Appl. Sci., 12.","DOI":"10.3390\/app12199423"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1080\/10447318.2022.2126812","article-title":"Explainable AI: The effect of contradictory decisions and explanations on users\u2019 acceptance of AI systems","volume":"39","author":"Ebermann","year":"2023","journal-title":"Int. J. Hum.-Interact."},{"key":"ref_17","unstructured":"Gilpin, L.H., Paley, A.R., Alam, M.A., Spurlock, S., and Hammond, K.J. (2022). \u201cExplanation\u201d is Not a Technical Term: The Problem of Ambiguity in XAI. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Adeyinka, T.I., Adeyinka, K.I., and Emmanuel, A.A. (2025). Security, Privacy, and Trust of AI-IoT Convergent Smart System. Humans and Generative AI Tools for Collaborative Intelligence, IGI Global Scientific Publishing.","DOI":"10.4018\/979-8-3693-8332-2.ch006"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taherisadr, M., Stavroulakis, S.A., and Elmalaki, S. (2023, January 9\u201312). AdaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems. Proceedings of the 8th ACM\/IEEE Conference on Internet of Things Design and Implementatio, San Antonio, TX, USA.","DOI":"10.1145\/3576842.3582325"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23888","DOI":"10.1038\/s41598-025-07844-3","article-title":"Enhancing smart city sustainability with explainable federated learning for vehicular energy control","volume":"15","author":"Almaazmi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Maralapalle, V., Muktinutalapati, J., Tammineni, G., Krishna, M.V.N., and Marlapalle, S. (2025). Smart Cities, Smarter Solutions: AI for Urban Transformation. Intelligent Systems for Sustainable Infrastructure: AI Solutions Shaping a Green Future\u2014Leveraging AI Innovations for Eco Friendly Infrastructure and Environmental Resilience, Springer Nature.","DOI":"10.1007\/978-3-031-95240-1_1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4013214","DOI":"10.1109\/TIM.2025.3584508","article-title":"You Can Monitor Your Hydration Level Using Your Smartphone Camera","volume":"74","author":"Alaslani","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","unstructured":"Gunning, D. (2017). Explainable Artificial Intelligence (XAI)."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"168412","DOI":"10.1109\/ACCESS.2024.3492973","article-title":"XAI-powered smart agriculture framework for enhancing food productivity and sustainability","volume":"12","author":"Martin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., \u0141obodzi\u0144ska, A., and \u015anieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now\u2014A Systematic Review of Smart Sensing Technologies. Sensors, 25.","DOI":"10.3390\/s25123583"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"71024","DOI":"10.1109\/ACCESS.2024.3402446","article-title":"XAI-IoT: An explainable AI framework for enhancing anomaly detection in IoT systems","volume":"12","author":"Gummadi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kaur, N., and Gupta, L. (2025). Securing the 6G\u2013IoT Environment: A Framework for Enhancing Transparency in Artificial Intelligence Decision-Making Through Explainable Artificial Intelligence. Sensors, 25.","DOI":"10.3390\/s25030854"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"57128","DOI":"10.1109\/ACCESS.2024.3382709","article-title":"A survey on security, privacy, trust, and architectural challenges in IoT systems","volume":"12","author":"Adam","year":"2024","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Al Khatib, I., Shamayleh, A., and Ndiaye, M. (2024). Healthcare and the internet of medical things: Applications, trends, key challenges, and proposed resolutions. Informatics, 11.","DOI":"10.3390\/informatics11030047"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lim, K.S., Ooi, S.Y., Sayeed, M.S., Chew, Y.J., and Ahmad, N.M. (2025). Securing the Internet of Things: Systematic Insights into Architectures, Threats, and Defenses. Electronics, 14.","DOI":"10.3390\/electronics14203972"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Eren, H., Karaduman, \u00d6., and Gen\u00e7o\u011flu, M.T. (2025). Security and Privacy in the Internet of Everything (IoE): A Review on Blockchain, Edge Computing, AI, and Quantum-Resilient Solutions. Appl. Sci., 15.","DOI":"10.3390\/app15158704"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sebestyen, H., Popescu, D.E., and Zmaranda, R.D. (2025). A literature review on security in the Internet of Things: Identifying and analysing critical categories. Computers, 14.","DOI":"10.3390\/computers14020061"},{"key":"ref_33","unstructured":"Corcuera B\u00e1rcena, J.L., Daole, M., Ducange, P., Marcelloni, F., Renda, A., Ruffini, F., and Schiavo, A. (December, January 28). Fed-XAI: Federated Learning of Explainable Artificial Intelligence Models. Proceedings of the XAI.it @ AI*IA Workshop, Udine, Italy."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Karras, A., Karras, C., Giotopoulos, K.C., Tsolis, D., Oikonomou, K., and Sioutas, S. (2022, January 23\u201325). Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices. Proceedings of the 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932980"},{"key":"ref_35","unstructured":"Bedewy, S.F. (2024). The impact of data security and privacy concerns on the implementation of integrated. Smart Cities: Foundations and Perspectives, IntechOpen Limited."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bures, M., Bellekens, X., Frajtak, K., and Ahmed, B.S. (2018, January 21\u201323). A Comprehensive View on Quality Characteristics of the IoT Solutions. Proceedings of the 3rd EAI International Conference on IoT in Urban Space, Guimar\u00e3es, Portugal.","DOI":"10.1007\/978-3-030-28925-6_6"},{"key":"ref_37","unstructured":"Alur, R., Berger, E., Drobnis, A.W., Fix, L., Fu, K., Hager, G.D., Lopresti, D., Nahrstedt, K., Mynatt, E., and Patel, S. (2016). Systems Computing Challenges in the Internet of Things, Computing Community Consortium. Computing Community Consortium (CCC) Report."},{"key":"ref_38","first-page":"103678","article-title":"Securing the Future: Proactive Threat Hunting for Sustainable IoT Ecosystems","volume":"138","author":"Ghasemshirazi","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_39","first-page":"2399","article-title":"A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security","volume":"20","author":"Mohamed","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Elkhawaga, G., Abuelkheir, M., and Reichert, M. (2022). XAI in the Context of Predictive Process Monitoring: Too Much to Reveal. arXiv.","DOI":"10.3390\/a15060199"},{"key":"ref_41","unstructured":"Oliveira, R.M.B.d., Goethals, S., Brughmans, D., and Martens, D. (2023). Unveiling the Potential of Counterfactuals Explanations in Employability. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bertoglio, R., Corbo, C., Renga, F.M., and Matteucci, M. (2021). The Digital Agricultural Revolution: A Bibliometric Analysis Literature Review. Agronomy, 10.","DOI":"10.1109\/ACCESS.2021.3115258"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bai, Y., Zhao, J., Wei, T., Cai, Q., and He, L. (2024). A Survey of Explainable Knowledge Tracing. arXiv.","DOI":"10.1007\/s10489-024-05509-8"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Javed, A.R., Ahmed, W., Pandya, S., Maddikunta, P.K.R., Alazab, M., and Gadekallu, T.R. (2023). A survey of explainable artificial intelligence for smart cities. Electronics, 12.","DOI":"10.3390\/electronics12041020"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"98349","DOI":"10.1109\/ACCESS.2025.3572492","article-title":"XAI based Photovoltaic Energy Management Framework for Smart Cities","volume":"13","author":"Alagarsamy","year":"2025","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ghonge, M.M., Pradeep, N., Jhanjhi, N.Z., and Kulkarni, P.M. (2024). Advances in Explainable AI Applications for Smart Cities, IGI Global.","DOI":"10.4018\/978-1-6684-6361-1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"37225","DOI":"10.1007\/s11042-023-17978-z","article-title":"Analyzing and assessing explainable AI models for smart agriculture environments","volume":"83","author":"Cartolano","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tripathy, B.K., and Seetha, H. (2024). Adaptation of XAI for Smart Agriculture Systems. Explainable, Interpretable, and Transparent AI Systems, CRC Press.","DOI":"10.1201\/9781003442509"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"72883","DOI":"10.1109\/ACCESS.2025.3563202","article-title":"Ontologies for Smart Agriculture: A Path Toward Explainable AI\u2013A Systematic Literature Review","volume":"13","author":"Grati","year":"2025","journal-title":"IEEE Access"},{"key":"ref_50","first-page":"2579","article-title":"A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts","volume":"37","author":"Schwalbe","year":"2023","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_51","first-page":"3503","article-title":"Explainable Artificial Intelligence: A Comprehensive Review","volume":"55","author":"Vilone","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_52","unstructured":"Runck, B.C., Schulz, B., Bishop, J., Carlson, N., Chantigian, B., Deters, G., Erdmann, J., Ewing, P.M., Felzan, M., and Fu, X. (2024). Real-Time Geoinformation Systems to Improve the Quality, Scalability, and Cost of Internet of Things for Agri-Environment Research. Frontiers in Sustainable Food Systems, Frontiers."},{"key":"ref_53","unstructured":"Islam, M.A., Mridha, M.F., Jahin, M.A., and Dey, N. (2024). A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications. arXiv."},{"key":"ref_54","unstructured":"Kondapaneni, N., Marks, M., Aodha, O.M., and Perona, P. (2024). Less is More: Discovering Concise Network Explanations. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci., 12.","DOI":"10.3390\/app12073396"},{"key":"ref_56","unstructured":"Vermesan, O., and Friess, P. (2013). Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, River Publishers."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of Things (IoT): A vision, architectural elements, and future directions","volume":"29","author":"Gubbi","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1109\/JIOT.2017.2767291","article-title":"Evaluating critical security issues of the IoT world: Present and future challenges","volume":"5","author":"Frustaci","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"82721","DOI":"10.1109\/ACCESS.2019.2924045","article-title":"A survey on IoT security: Application areas, security threats, and solution architectures","volume":"7","author":"Hassija","year":"2019","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Malhotra, P., Singh, Y., Anand, P., Bangotra, D.K., Singh, P.K., and Hong, W.C. (2021). Internet of things: Evolution, concerns and security challenges. Sensors, 21.","DOI":"10.3390\/s21051809"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kanellopoulos, D., Sharma, V.K., Panagiotakopoulos, T., and Kameas, A. (2023). Networking architectures and protocols for IoT applications in smart cities: Recent developments and perspectives. Electronics, 12.","DOI":"10.3390\/electronics12112490"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bellini, P., Nesi, P., and Pantaleo, G. (2022). IoT-enabled smart cities: A review of concepts, frameworks and key technologies. Appl. Sci., 12.","DOI":"10.3390\/app12031607"},{"key":"ref_63","unstructured":"Zhang, N., Demetriou, S., Mi, X., Diao, W., Yuan, K., Zong, P., Qian, F., Wang, X., Chen, K., and Tian, Y. (2017). Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pimenow, S., Pimenowa, O., Prus, P., and Niklas, A. (2025). The Impact of Artificial Intelligence on the Sustainability of Regional Ecosystems: Current Challenges and Future Prospects. Sustainability, 17.","DOI":"10.3390\/su17114795"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Al Jasem, M.S., De Clark, T., and Shrestha, A.K. (2025). Toward decentralized intelligence: A systematic literature review of blockchain-enabled AI systems. Information, 16.","DOI":"10.3390\/info16090765"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Donta, P.K., Murturi, I., Casamayor Pujol, V., Sedlak, B., and Dustdar, S. (2023). Exploring the potential of distributed computing continuum systems. Computers, 12.","DOI":"10.3390\/computers12100198"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Allioui, H., and Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23.","DOI":"10.3390\/s23198015"},{"key":"ref_68","first-page":"1","article-title":"Privacy Concerns and Security Challenges in IoT Systems","volume":"51","author":"Demetriou","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_69","unstructured":"Zakaie Far, A., Zakaie Far, M., Gharibzadeh, S., Kazemi Naeini, H., Amini, L., Zangeneh, S., Rahimi, M., and Asadi, S. (2024). Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Methnani, L., Dignum, V., and Theodorou, A. (2023). Clash of the Explainers: Argumentation for Context-Appropriate Explanations. arXiv.","DOI":"10.1007\/978-3-031-50396-2_1"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Almadani, B., Kaisar, H., Thoker, I.R., and Aliyu, F. (2025). A systematic survey of distributed decision support systems in healthcare. Systems, 13.","DOI":"10.3390\/systems13030157"},{"key":"ref_72","unstructured":"Kumar, I.A., Wu, Y., Jirotka, M., and Bussone, B. (2020). Explainable Artificial Intelligence: Foundations, Taxonomy and Challenges. arXiv."},{"key":"ref_73","first-page":"48","article-title":"Explainable AI: Current Status and Future Directions","volume":"8","author":"Gohel","year":"2021","journal-title":"J. Big Data"},{"key":"ref_74","unstructured":"Heskes, T., Sijben, E., Bucur, I.G., and Claassen, T. (2020). Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.3390\/smartcities7030057","article-title":"Artificial intelligence in smart cities\u2014Applications, barriers, and future directions: A review","volume":"7","author":"Wolniak","year":"2024","journal-title":"Smart Cities"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.3390\/smartcities3040065","article-title":"Explainable artificial intelligence for developing smart cities solutions","volume":"3","author":"Thakker","year":"2020","journal-title":"Smart Cities"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Musa, A.A., Malami, S.I., Alanazi, F., Ounaies, W., Alshammari, M., and Haruna, S.I. (2023). Sustainable traffic management for smart cities using internet-of-things-oriented intelligent transportation systems (ITS): Challenges and recommendations. Sustainability, 15.","DOI":"10.3390\/su15139859"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Mishra, P., and Singh, G. (2023). Energy management systems in sustainable smart cities based on the internet of energy: A technical review. Energies, 16.","DOI":"10.3390\/en16196903"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Alahi, M.E.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K., and Mukhopadhyay, S.C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: Recent advancements and future trends. Sensors, 23.","DOI":"10.3390\/s23115206"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yeong, D.J., Panduru, K., and Walsh, J. (2025). Exploring the unseen: A survey of multi-sensor fusion and the role of explainable ai (xai) in autonomous vehicles. Sensors, 25.","DOI":"10.20944\/preprints202501.1423.v1"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Nastoska, A., Jancheska, B., Rizinski, M., and Trajanov, D. (2025). Evaluating trustworthiness in AI: Risks, metrics, and applications across industries. Electronics, 14.","DOI":"10.3390\/electronics14132717"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Kabir, S., Hossain, M.S., and Andersson, K. (2025). A review of explainable artificial intelligence from the perspectives of challenges and opportunities. Algorithms, 18.","DOI":"10.3390\/a18090556"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"121326","DOI":"10.1109\/ACCESS.2025.3575022","article-title":"Effectiveness of Explainable Artificial Intelligence (XAI) Techniques for Improving Human Trust in Machine Learning Models: A Systematic Literature Review","volume":"13","author":"Wiratsin","year":"2025","journal-title":"IEEE Access"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"41111","DOI":"10.1109\/ACCESS.2025.3546681","article-title":"A Literature review on applications of explainable artificial intelligence (XAI)","volume":"13","author":"Kalasampath","year":"2025","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Naveed, S., Stevens, G., and Robin-Kern, D. (2024). An overview of the empirical evaluation of explainable ai (xai): A comprehensive guideline for user-centered evaluation in xai. Appl. Sci., 14.","DOI":"10.20944\/preprints202410.0098.v1"},{"key":"ref_86","unstructured":"Shikonde, S., and Nkongolo, M.W. (2025). A Proactive Insider Threat Management Framework Using Explainable Machine Learning. arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Latif, R.M.A., Ullah, F., Jamal, N., Zhao, Y., Jabbar, S., and Khan, M.A. (2025, January 20\u201322). Explainable AI for Big Data Analytics in Urban Mobility Forecasting. Proceedings of the 2025 IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI), Loudi, China.","DOI":"10.1109\/PRMVAI65741.2025.11108680"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zheng, B., Xue, J., and Zhou, Y. (2025). Explainable and trust-aware AI-driven network slicing framework for 6G IoT using deep learning. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2025.3619970"},{"key":"ref_89","unstructured":"Tao, W., Tao, J., and Jiang, M. (2024). XAI Methods for Cross-Selling Prediction. arXiv."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Li, P., Mavromatis, I., and Khan, A. (2024). Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the Umbrella IoT Testbed. Sensors, 24.","DOI":"10.1109\/PerComWorkshops59983.2024.10502658"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"128598","DOI":"10.1016\/j.eswa.2025.128598","article-title":"Exploiting open data for CO2 estimation via artificial intelligence and eXplainable AI","volume":"291","author":"Bilotta","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Chauncey, S.A., and McKenna, H.P. (2024). Creativity and innovation in civic spaces supported by cognitive flexibility when learning with AI chatbots in smart cities. Urban Sci., 8.","DOI":"10.3390\/urbansci8010016"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"110261","DOI":"10.1016\/j.compeleceng.2025.110261","article-title":"Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing","volume":"123","author":"Piran","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.future.2024.07.035","article-title":"Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems","volume":"161","author":"Aminifar","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Shajalal, M., Boden, A., Stevens, G., Du, D., and Kern, D.-R. (2024). Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments. World Conference on Explainable Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-031-63803-9_23"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1007\/s11119-024-10164-7","article-title":"Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change","volume":"25","author":"Delfani","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Choi, J.W., Hidayat, M.S., Cho, S.B., Hwang, W.H., Lee, H., Cho, B.K., Kim, M.S., Baek, I., and Kim, G. (2025). Recent Trends in Machine Learning, Deep Learning, Ensemble Learning, and Explainable Artificial Intelligence Techniques for Evaluating Crop Yields Under Abnormal Climate Conditions. Plants, 14.","DOI":"10.3390\/plants14182841"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Mohan, R.J., Rayanoothala, P.S., and Sree, R.P. (2025). Next-gen agriculture: Integrating AI and XAI for precision crop yield predictions. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1451607"},{"key":"ref_99","first-page":"4669","article-title":"Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications","volume":"8","author":"Fizza","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_100","first-page":"104348","article-title":"Towards sustainable energy management: Leveraging explainable Artificial Intelligence for transparent and efficient decision-making","volume":"78","author":"Talaat","year":"2025","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Ngo, Q.H., Kechadi, T., and Le-Khac, N.A. (2022, January 13\u201315). OAK4XAI: Model towards out-of-box eXplainable artificial intelligence for digital agriculture. Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK.","DOI":"10.1007\/978-3-031-21441-7_17"},{"key":"ref_102","unstructured":"Ashir, D.M.N.A., Ahad, M.T., Talukder, M., and Rahman, T. (2022). Internet of Things (IoT) Based Smart Agriculture Aiming to Achieve Sustainable Goals. Sustainability, 14."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"24348","DOI":"10.1109\/ACCESS.2024.3365586","article-title":"Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities","volume":"12","author":"Kisten","year":"2024","journal-title":"IEEE Access"},{"key":"ref_104","first-page":"107742","article-title":"Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation","volume":"207","author":"Rogers","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_105","first-page":"2887","article-title":"Web of Things and Trends in Agriculture: A Systematic Literature Review","volume":"104","author":"Farooq","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Kwon, Y.D., Chauhan, J., and Mascolo, C. (2022, January 4\u20136). Yono: Modeling multiple heterogeneous neural networks on microcontrollers. Proceedings of the 2022 21st ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Virtual.","DOI":"10.1109\/IPSN54338.2022.00030"},{"key":"ref_107","unstructured":"Kadir, M.A., Addluri, G., and Sonntag, D. (2024). Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors. arXiv."},{"key":"ref_108","first-page":"1156","article-title":"When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances","volume":"114","author":"Vascotto","year":"2025","journal-title":"Mach. Learn."},{"key":"ref_109","unstructured":"Luss, R., and Dhurandhar, A. (2024). When Stability Meets Sufficiency: Informative Explanations That Do Not Overwhelm. arXiv."},{"key":"ref_110","unstructured":"Khadivpour, F., Banerjee, A., and Guzdial, M. (2022). Responsibility: An example-based explainable AI approach via training process inspection. arXiv."},{"key":"ref_111","unstructured":"Sokol, K., Hepburn, A., Santos-Rodriguez, R., and Flach, P. (2019). bLIMEy: Surrogate prediction explanations beyond LIME. arXiv."},{"key":"ref_112","unstructured":"Yang, Y.D., Kwon, J., Chauhan, C., and Mascolo, C. (2024). YONO: You Only Need One Task on Microcontrollers. arXiv."},{"key":"ref_113","unstructured":"Das, A., and Rad, P. (2020). Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey. arXiv."},{"key":"ref_114","first-page":"1","article-title":"ShapG: New Feature Importance Method Based on the Shapley Value","volume":"26","author":"Zhao","year":"2025","journal-title":"J. Mach. Learn. Res."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Lu, C., Zeng, J., Xia, Y., Cai, J., and Luo, S. (2025). Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution. arXiv.","DOI":"10.1109\/TPAMI.2025.3626404"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Dineen, J., Kridel, D., Dolk, D., and Castillo, D. (2024). Unified Explanations in Machine Learning Models: A Perturbation Approach. arXiv.","DOI":"10.24251\/HICSS.2023.100"},{"key":"ref_117","unstructured":"Liao, Q.V., and Varshney, K.R. (2021). Human-centered explainable ai (xai): From algorithms to user experiences. arXiv."},{"key":"ref_118","first-page":"5674","article-title":"Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset","volume":"33","author":"Tjoa","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"33160","DOI":"10.1038\/s41598-025-15146-x","article-title":"Explainable artificial intelligence-based cyber resilience in internet of things networks using hybrid deep learning with improved chimp optimization algorithm","volume":"15","author":"Alzakari","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s43926-025-00155-z","article-title":"Strategies for applying interpretable and explainable AI in real world IoT applications","volume":"5","author":"Mohammad","year":"2025","journal-title":"Discov. Internet Things"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Watson, D. (2022, January 21\u201324). Rational Shapley Values. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT \u201922), Seoul, Republic of Korea.","DOI":"10.1145\/3531146.3533170"},{"key":"ref_122","unstructured":"Sairam, S., Srinivasan, S., Marafioti, G., Subathra, B., Mathisen, G., and Bekiroglu, K. (2020). Explainable Incipient Fault Detection Systems for Photovoltaic Panels. arXiv."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Takahashi, D., Shimizu, S., and Tanaka, T. (July, January 30). Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan.","DOI":"10.1109\/IJCNN60899.2024.10650130"},{"key":"ref_124","first-page":"22378","article-title":"SECOE: Alleviating Sensors Failure in Machine Learning-Coupled IoT Systems","volume":"9","author":"AlShehri","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_125","first-page":"96","article-title":"Towards an Accountable Internet of Things: A Call for Reviewability","volume":"5","author":"Norval","year":"2021","journal-title":"Data Inf. Manag."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Kaczmarek, E., Miguel, O.X., Bowie, A.C., Ducharme, R., Dingwall-Harvey, A.L.J., Hawken, S., Armour, C.M., Walker, M.C., and Dick, K. (2023). CAManim: Animating End-to-End Network Activation Maps. arXiv.","DOI":"10.1371\/journal.pone.0296985"},{"key":"ref_127","first-page":"239","article-title":"A Grounded Interaction Protocol for Explainable Artificial Intelligence","volume":"33","author":"Madumal","year":"2019","journal-title":"Auton. Agents Multi-Agent Syst."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Steinecker, T., Luettel, T., and Maehlisch, M. (2024). Collision Probability Distribution Estimation via Temporal Difference Learning. arXiv.","DOI":"10.1109\/ITSC58415.2024.10920103"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Roy, S., Rezazadeh, F., Chergui, H., and Verikoukis, C. (June, January 28). Joint Explainability and Sensitivity-Aware Federated Deep Learning for Transparent 6G RAN Slicing. Proceedings of the ICC 2023\u2014IEEE International Conference on Communications, Rome, Italy.","DOI":"10.1109\/ICC45041.2023.10279790"},{"key":"ref_130","first-page":"2946","article-title":"How Much Informative is Your XAI? A Decision-Making Assessment Task to Objectively Measure the Goodness of Explanations","volume":"8","author":"Matarese","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Luo, Z., Zhao, S., Lu, Z., Sagduyu, Y.E., and Xu, J. (2020). Adversarial Machine Learning Based Partial-Model Attack in IoT. arXiv.","DOI":"10.1145\/3395352.3402619"},{"key":"ref_132","first-page":"779","article-title":"Security Risk Assessment in Internet of Things Systems","volume":"5","author":"Nurse","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"5652","DOI":"10.14778\/3773731.3773740","article-title":"Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams","volume":"18","author":"Shaowang","year":"2025","journal-title":"Proc. VLDB Endow."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, C., Zhao, Q., Zou, H., Lasaulce, S., Valenzise, G., He, Z., and Debbah, M. (2024). Generative AI for RF Sensing in IoT Systems. arXiv.","DOI":"10.1109\/IOTM.001.2400107"},{"key":"ref_135","first-page":"45678","article-title":"Interoperability and Explicable AI-Based Zero-Day Attacks Detection Process in Smart Community","volume":"12","author":"Sayduzzaman","year":"2024","journal-title":"IEEE Access"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1016\/j.egyr.2025.01.063","article-title":"Smart buildings: Federated learning-driven secure, transparent and smart energy management system using XAI","volume":"13","author":"Khan","year":"2025","journal-title":"Energy Rep."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"116246","DOI":"10.1016\/j.enbuild.2025.116246","article-title":"Explainable AI framework for reliable and transparent automated energy management in buildings","volume":"347","author":"Teixeira","year":"2025","journal-title":"Energy Build."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Naveen, P., and Vinodkumar, S. (2025). Enhancing power system management with XAI. Explainable Artificial Intelligence and Solar Energy Integration, IGI Global.","DOI":"10.4018\/979-8-3693-7822-9.ch014"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Alshkeili, H.M.H.A., Almheiri, S.J., and Khan, M.A. (2025). Privacy-Preserving Interpretability: An Explainable Federated Learning Model for Predictive Maintenance in Sustainable Manufacturing and Industry 4.0. AI, 6.","DOI":"10.3390\/ai6060117"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"97567","DOI":"10.1109\/ACCESS.2025.3571340","article-title":"A Federated Explainable AI Framework for Smart Agriculture: Enhancing Transparency, Efficiency, and Sustainability","volume":"13","author":"Tahir","year":"2025","journal-title":"IEEE Access"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"2507180","DOI":"10.1080\/09540091.2025.2507180","article-title":"eXING-IoT conceptual framework for explainability integration in next generation-IoT","volume":"37","year":"2025","journal-title":"Connect. Sci."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Watson, D.S. (2021). Rational Shapley Values. arXiv.","DOI":"10.1145\/3531146.3533170"},{"key":"ref_143","first-page":"3932","article-title":"Towards a Secure Behavior Modeling for IoT Networks Using Blockchain","volume":"7","author":"Ali","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_144","first-page":"2867","article-title":"Digital Twins and Blockchain for IoT Management","volume":"10","author":"Samaniego","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Karras, A., Karras, C., Giotopoulos, K.C., Tsolis, D., Oikonomou, K., and Sioutas, S. (2023). Federated edge intelligence and edge caching mechanisms. Information, 14.","DOI":"10.3390\/info14070414"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Karras, A., Giannaros, A., Theodorakopoulos, L., Krimpas, G.A., Kalogeratos, G., Karras, C., and Sioutas, S. (2023). FLIBD: A federated learning-based IoT big data management approach for privacy-preserving over Apache Spark with FATE. Electronics, 12.","DOI":"10.3390\/electronics12224633"},{"key":"ref_147","first-page":"3456","article-title":"Privacy-Preserving Cyberattack Detection in Blockchain-Based IoT Systems Using AI and Homomorphic Encryption","volume":"19","author":"Manh","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_148","unstructured":"Yan, K., Cui, S., Wuerkaixi, A., Zhang, J., Han, B., Niu, G., Sugiyama, M., and Zhang, C. (2024). Balancing Similarity and Complementarity for Federated Learning. arXiv."},{"key":"ref_149","unstructured":"Fu, A. (2023). Leveraging Learning Metrics for Improved Federated Learning. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Suffian, M., Khan, M.Y., and Bogliolo, A. (2022). Towards Human Cognition Level-Based Experiment Design for Counterfactual Explanations (XAI). arXiv.","DOI":"10.1109\/MAJICC56935.2022.9994203"},{"key":"ref_151","first-page":"6736","article-title":"Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things","volume":"16","author":"Ferdowsi","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Baniecki, H., and Biecek, P. (2024). Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey. arXiv.","DOI":"10.1016\/j.inffus.2024.102303"},{"key":"ref_153","first-page":"98765","article-title":"Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Schlegel, U., and Keim, D.A. (2023, January 26\u201328). A deep dive into perturbations as evaluation technique for time series XAI. Proceedings of the World Conference on Explainable Artificial Intelligence, Lisbon, Portugal.","DOI":"10.1007\/978-3-031-44070-0_9"},{"key":"ref_155","unstructured":"Haag, F., Hopf, K., Vasconcelos, P.M., and Staake, T. (2022). Augmented cross-selling through explainable AI\u2013a case from energy retailing. arXiv."},{"key":"ref_156","unstructured":"Hameed, I., Sharpe, S., Barcklow, D., Au-Yeung, J., Verma, S., Huang, J., Barr, B., and Bruss, C.B. (2022). BASED-XAI: Breaking ablation studies down for explainable artificial intelligence. arXiv."},{"key":"ref_157","unstructured":"Amiri, S.S., Weber, R.O., Goel, P., Brooks, O., Gandley, A., Kitchell, B., and Zehm, A. (2020). Data representing ground-truth explanations to evaluate xai methods. arXiv."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Alufaisan, Y., Marusich, L.R., Bakdash, J.Z., Zhou, Y., and Kantarcioglu, M. (2021, January 19\u201321). Does explainable artificial intelligence improve human decision-making?. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.31234\/osf.io\/d4r9t"},{"key":"ref_159","first-page":"102520","article-title":"Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing","volume":"103","author":"Kakogeorgiou","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_160","unstructured":"Brankovic, A., Cook, D., Rahman, J., Huang, W., and Khanna, S. (2023). Evaluation of popular XAI applied to clinical prediction models: Can they be trusted?. arXiv."},{"key":"ref_161","first-page":"342","article-title":"A Fog-Based Smart Agriculture System to Detect Animal Intrusion","volume":"69","author":"Miao","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_162","first-page":"1832","article-title":"Network Traffic Shaping for Enhancing Privacy in IoT Systems","volume":"68","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_163","first-page":"6234","article-title":"Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement","volume":"45","author":"Weber","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_164","first-page":"2109","article-title":"Trainable Noise Model as an XAI Evaluation Method: Application on Sobol for Remote Sensing Image Segmentation","volume":"15","author":"Shreim","year":"2023","journal-title":"Remote Sens."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Karras, A., Karras, C., Drakopoulos, G., Tsolis, D., Mylonas, P., and Sioutas, S. (2022, January 17\u201320). SAF: A peer to peer IoT LoRa system for smart supply chain in agriculture. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Crete, Greece.","DOI":"10.1007\/978-3-031-08337-2_4"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Karras, A., Karras, C., Giannaros, A., Giotopoulos, K.C., Tsolis, D., Oikonomou, K., and Sioutas, S. (2023). TinyML-based Event Detection: An Edge-Cloud Approach for Smart Agriculture over LoRa WSNs. Proceedings of the 2023 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece, 10\u201312 November 2023, IEEE.","DOI":"10.1109\/SEEDA-CECNSM61561.2023.10470881"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Misahlidou, V., Karras, A., Giannoukou, I., and Sioutas, S. (2025, January 25\u201327). Optimizing Data Transmission in LoRa-Based IoT Systems: A Performance Evaluation of Compression Algorithms. Proceedings of the The International Conference on Innovations in Computing Research, London, UK.","DOI":"10.1007\/978-3-031-95652-2_42"},{"key":"ref_168","first-page":"3456","article-title":"Heterogeneous GNN-RL Based Task Offloading for UAV-Aided Smart Agriculture","volume":"23","author":"Pamuklu","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_169","first-page":"78","article-title":"Agricultural On-Demand Networks for 6G Enabled by THz Communication","volume":"31","author":"Lindenschmitt","year":"2024","journal-title":"IEEE Wirel. Commun."},{"key":"ref_170","first-page":"108065","article-title":"Advanced Machine Learning Framework for Efficient Plant Disease Prediction","volume":"211","author":"Muthuselvam","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_171","first-page":"32","article-title":"Deep Learning for Smart Agriculture: Concepts, Tools, Applications, and Opportunities","volume":"11","author":"Zhu","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Karras, A., Karras, C., Giannoukou, I., Giotopoulos, K.C., Tsolis, D., Karydis, I., and Sioutas, S. (2023, January 5). Decentralized algorithms for efficient energy management over cloud-edge infrastructures. Proceedings of the International Symposium on Algorithmic Aspects of Cloud Computing, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-031-49361-4_12"},{"key":"ref_173","first-page":"5226","article-title":"Exploring Data and Knowledge Combined Anomaly Explanation of Multivariate Industrial Data","volume":"34","author":"Ding","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_174","first-page":"3236","article-title":"Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response","volume":"19","author":"Islam","year":"2020","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_175","first-page":"382","article-title":"Anomaly Detection Through Transfer Learning in Agriculture and Manufacturing IoT Systems","volume":"60","author":"Abdallah","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_176","first-page":"511","article-title":"A Comprehensive Review of Data Mining Techniques in Smart Agriculture","volume":"12","author":"Aoudjit","year":"2019","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_177","first-page":"34","article-title":"LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions","volume":"7","author":"Aldhaheri","year":"2024","journal-title":"IEEE Internet Things Mag."},{"key":"ref_178","first-page":"147","article-title":"Ontology Based Approach for Precision Agriculture","volume":"157","author":"Ngo","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_179","first-page":"6006","article-title":"Tamperproof IoT with Blockchain","volume":"9","author":"Yu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., and M\u00fcller, K.-R. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer Nature.","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_182","unstructured":"Luss, R., and Dhurandhar, A. (2021). Towards Better Model Understanding with Path-Sufficient Explanations. arXiv."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"7632","DOI":"10.1038\/s41598-025-90420-6","article-title":"Explainable artificial intelligence for botnet detection in internet of things","volume":"15","author":"Saied","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Dobrovolskis, A., Kazanavi\u010dius, E., and Ki\u017eauskien\u0117, L. (2023). Building XAI-based agents for IoT systems. Appl. Sci., 13.","DOI":"10.3390\/app13064040"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Roshan, K., and Zafar, A. (2023). Using Kernel SHAP XAI Method to Optimize the Network Anomaly Detection Model. arXiv.","DOI":"10.23919\/INDIACom54597.2022.9763241"},{"key":"ref_186","unstructured":"Biessmann, F., and Treu, V. (2021). A Turing Test for Transparency. arXiv."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Ivanovic, M., Autexier, S., and Kokkonidis, M. (2022). AI Approaches in Processing and Using Data in Personalized Medicine. Front. Med., 9.","DOI":"10.1007\/978-3-031-15740-0_2"},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Ferrario, A., and Loi, M. (2021). A Series of Unfortunate Counterfactual Events: The Role of Time in Counterfactual Explanations. arXiv.","DOI":"10.1109\/ACCESS.2022.3196917"},{"key":"ref_189","first-page":"38","article-title":"Knowledge-Powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Commun. Mag."},{"key":"ref_190","unstructured":"Chuang, Y.N., Wang, G., Yang, F., Liu, Z., Cai, X., Du, M., and Hu, X. (2023). Efficient XAI Techniques: A Taxonomic Survey. arXiv."},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Velmurugan, M., Ouyang, C., Xu, Y., Sindhgatta, R., Wickramanayake, B., and Moreira, C. (2024). Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence Using Tabular Data. arXiv.","DOI":"10.1016\/j.engappai.2024.109772"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/2\/83\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:25:47Z","timestamp":1770960347000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/2\/83"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,4]]},"references-count":191,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["fi18020083"],"URL":"https:\/\/doi.org\/10.3390\/fi18020083","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,4]]}}}