Governance-of-Things (GoT): A Next-Generation Framework for Ethical, Intelligent, and Autonomous Web Data Acquisition
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P113Keywords:
Governance-Of-Things, Autonomous Acquisition, Compliance-Aware Agents, Dynamic Policy Enforcement, Federated Governance, Semantic Classification, Integrity-Preserving Automation, Adaptive Monitoring, AI-Enhanced Java Frameworks, Distributed AnalyticsAbstract
The unstoppably increasing number of the Internet of Things (IoT), autonomous agents, and massive distributed web ecosystems have made data acquisition a complicated, risk-prone, and a very sensitive process. Regulation Web data collection is a fixed pipeline that is strictly regulated by established rules and legal limits, and reactive policy audits to operate in traditional forms of governance. Nevertheless, the contemporary digital ecosystem requires a decentralized system of governance that could identify unpredictable streams of data, the shifting web framework, loosely distributed computing individuals, and shifting conditions of regulation. This paper will present Governance-of-Things (GoT), an emerging conceptual and architectural design that will address these issues and show how to smoothly integrate ethical intelligence, regulatory and laws compliance, semantic awareness, and integrity assurance within autonomous systems of web data acquisition. GoT suggests a view where governance follows a first-class computation i.e. embedded, adaptive, intelligent and context-aware. In contrast to traditional approaches of governing IoT, GoT regards any acquisition agent as ethics-regulated, compliance-aware, and self-regulating. Agents do not simply pull information, they negotiate access rights, authenticate provenance, reason about risk, and implement multi-jurisdictional policies all by themselves. The framework combines dynamic enforcement of policies, federated governance, semantic classification pipelines, AI-enhanced agent frameworks built on Java and distributed analytics to create an ecosystem, producing an automated acquisition that is compatible with responsible, transparent, and audit-friendly behaviours. Fairness, legality, transparency, explainability and accountability are the principles of ethical autonomy which are expounded in the paper. GoT has the aspect of federated ethical rule orchestration where the governance layers among organizations in various stakeholders share without necessarily providing the raw information. The system incorporates automation using structural integrity that guarantees cryptographic validation and review trails that are not tampered with. The given adaptive monitoring model promotes the constant policy updating, data flows redirection and the detection of threats. Furthermore, GoT involves semantic intelligence so that data classification, contextual labeling, entity recognition, and domain mapping take place before storing or processing data- therein avoiding compliance violation at its early phases. GoT architecturally has a multi-layer stack that is organized and includes Perception Layer, Autonomous Agent Layer, Governance Core, Distributed Analytics Layer, and Compliance Ledger Layer. The primitives of computational governance are embedded in each layer, making it highly modular and allowing run-time updates of rules and cooperating across agents. Java frameworks boosted with AI facilitate interoperability with legacy enterprise systems and with current base systems. Using experimental simulation, it was found that GoT enhances compliance accuracy, governance throughput, policy adaptation latency and decision explainability on varying scenarios of acquisitions. This article is in the pre-2021 academic style, has extensive literature review, methodological description, architectural schematics, theoretical framework, and profound results discussion. It ends by establishing GoT as an innovative paradigm which is able to influence the future of web data governance, autonomous systems, and distributed analytics
References
[1] Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A survey on security and privacy issues in Internet-of-Things. IEEE Internet of things Journal, 4(5), 1250-1258.
[2] Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080.
[3] Weber, R. H. (2010). Internet of Things–New security and privacy challenges. Computer law & security review, 26(1), 23-30.
[4] Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI magazine, 36(4), 105-114.
[5] Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE intelligent systems, 21(4), 18-21.
[6] Dwork, C. (2008, April). Differential privacy: A survey of results. In International conference on theory and applications of models of computation (pp. 1-19). Berlin, Heidelberg: Springer Berlin Heidelberg.
[7] Standard, O. A. S. I. S. (2013). extensible access control markup language (xacml) version 3.0. A:(22 January 2013). URl: http://docs. oasis-open. org/xacml/3.0/xacml-3.0-core-spec-os-en. html.
[8] Hu, V. C., Ferraiolo, D., Kuhn, R., Schnitzer, A., Sandlin, K., Miller, R., & Scarfone, K. (2014). Guide to attribute based access control (ABAC) definition and considerations. NIST special publication, 800(162), 1-54.
[9] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), 637-646.
[10] Sandhu, R. S. (1998). Role-based access control. In Advances in computers (Vol. 46, pp. 237-286). Elsevier.
[11] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.
[12] Zyskind, G., & Nathan, O. (2015, May). Decentralizing privacy: Using blockchain to protect personal data. In 2015 IEEE security and privacy workshops (pp. 180-184). IEEE.
[13] Hossein, K. M., Esmaeili, M. E., & Dargahi, T. (2019, May). Blockchain-based privacy-preserving healthcare architecture. In 2019 IEEE Canadian conference of electrical and computer engineering (CCECE) (pp. 1-4). IEEE.
[14] Moldrich, D. (2017). Records management and the governance of things. IQ: The RIMPA Quarterly Magazine, 33(2), 10-11.
[15] Ferraiolo, D., Kuhn, D. R., & Chandramouli, R. (2003). Role-based access control. Artech house.
[16] Brynskov, M., Facca, F. M., & Hrasko, G. (2018). Next Generation Internet of Things. H2020 Coordination and Support Action (CSA), NGIoT Consortium, 2021, 2019.
[17] Fortino, G., Savaglio, C., Spezzano, G., & Zhou, M. (2020). Internet of things as system of systems: A review of methodologies, frameworks, platforms, and tools. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 223-236.
[18] Daly, A., Hagendorff, T., Hui, L., Mann, M., Marda, V., Wagner, B., ... & Witteborn, S. (2019). Artificial intelligence governance and ethics: global perspectives. arXiv preprint arXiv:1907.03848.
[19] Singh, J., Bacon, J., & Eyers, D. (2014, May). Policy enforcement within emerging distributed, event-based systems. In Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems (pp. 246-255).
[20] Cao, L., Gorodetsky, V., & Mitkas, P. A. (2009). Agent mining: The synergy of agents and data mining. IEEE intelligent systems, 24(3), 64-72.
