Abstract
Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g., due to the cost and effort required, or to protect their interests. In response, this article introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model.
- [1] . 2010. The internet of things: A survey. Computer Networks 54, 15 (Oct. 2010), 2787–2805.Google Scholar
Digital Library
- [2] . 2017. Corroboration via provenance patterns. In Proceedings of the 9th USENIX Workshop on the Theory and Practice of Provenance. USENIX Association.Google Scholar
Digital Library
- [3] . 2017. TMCoI-SIOT: A trust management system based on communities of interest for the social internet of things. In Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference. 747–752.Google Scholar
Cross Ref
- [4] . 2017. CTMS-SIOT: A context-based trust management system for the social internet of things. In Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference. 1903–1908.Google Scholar
Cross Ref
- [5] . 2013. Trust management system design for the internet of things: A context-aware and multi-service approach. Computers & Security 39, B (2013), 351–365.
DOI: Google ScholarDigital Library
- [6] . 2013. Audit games. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. AAAI Press, 41–47.Google Scholar
- [7] . 2001. Rational and convergent learning in stochastic games. In Proceedings of the 17th International Joint Conference on Artificial Intelligence. 1021–1026.Google Scholar
Digital Library
- [8] . 2013. Stereotypical trust and bias in dynamic multiagent systems. ACM Transactions on Intelligent Systems and Technology 4, 2 (2013), 26.Google Scholar
Digital Library
- [9] . 2016. Trust management for SOA-Based IoT and its application to service composition. IEEE Transactions on Services Computing 9, 3 (2016), 482–495.Google Scholar
Cross Ref
- [10] . 2019. Trust architecture and reputation evaluation for internet of things. Journal of Ambient Intelligence and Humanized Computing 10, 8 (2019), 3099–3107.Google Scholar
Cross Ref
- [11] . 2019. A decade in hindsight: The missing bridge between multi-agent systems and the world wide web. In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems. 5.Google Scholar
- [12] . 1961. Incentive systems: A theory of organizations. Administrative Science Quarterly 6, 2 (1961), 129–166.Google Scholar
Cross Ref
- [13] . 2020. Trust and reputation in the internet of things: State-of-the-art and research challenges. IEEE Access 8 (2020), 60117–60125.Google Scholar
Cross Ref
- [14] . 2000. Can we trust trust? Trust: Making and Breaking Cooperative Relations13, (2000), 213–237.Google Scholar
- [15] . 2011. When and why incentives (don’t) work to modify behavior. Journal of Economic Perspectives 25, 4 (2011), 191–210.Google Scholar
Cross Ref
- [16] and (Eds.). 2010. Agent-Based Service-Oriented Computing. Springer.Google Scholar
Cross Ref
- [17] . 2013. An architecture for justified assessments of service provider reputation. In Proceedings of the 10th IEEE International Conference on e-Business Engineering. 345–352.Google Scholar
Digital Library
- [18] . 2010. Interacting with the SOA-based internet of things: Discovery, query, selection, and on-demand provisioning of web services. IEEE Transactions on Services Computing 3, 3 (2010), 223–235.Google Scholar
Digital Library
- [19] . 2017. A survey of trust computation models for service management in internet of things systems. Computer Communications. 97, C (2017), 1–14.
DOI: Google ScholarDigital Library
- [20] . 2010. Reputation in multi agent systems and the incentives to provide feedback. In Proceedings of the Multiagent System Technologies.
Lecture Notes in Computer Science , Vol. 6251. 40–51.Google ScholarCross Ref
- [21] . 2010. Punish, but not too hard: How costly punishment spreads in the spatial public goods game. New Journal of Physics 12, 8 (2010), 083005.Google Scholar
Cross Ref
- [22] . 2009. A survey of attack and defense techniques for reputation systems. ACM Computing Surveys 42, 1 (2009), 1–31.Google Scholar
Digital Library
- [23] . 2006. An integrated trust and reputation model for open multi-agent systems. Journal of Autonomous Agents and Multi-Agent Systems 13, 2 (2006), 119–154.Google Scholar
Digital Library
- [24] . 2016. Revisiting service-oriented architecture for the IoT: A middleware perspective. In Proceedings of the Service-Oriented Computing, , , , and (Eds.). 3–17.Google Scholar
Digital Library
- [25] . 2002. The beta reputation system. In Proceedings of the 15th Bled Electronic Commerce Conference.Google Scholar
- [26] . 2007. A survey of trust and reputation systems for online service provision. Decision Support Systems 43, 2 (2007), 618–644.Google Scholar
Digital Library
- [27] . 2009. Smart cheaters do prosper: Defeating trust and reputation systems. In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems. 993–1000.Google Scholar
- [28] . 1995. Integrating hypermedia and information retrieval with conceptual graphs formalism. In Proceedings of the Hypertext - Information Retrieval - Multimedia: Synergieeffekte elektronischer Informationssysteme. 47–60.Google Scholar
- [29] . 2016. A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems. Autonomous Agents and Multi-Agent Systems 30, 4 (2016), 581–600.Google Scholar
Digital Library
- [30] . 2012. Efficient norm emergence through experiential dynamic punishment. In Proceedings of the 20th European Conference on Artificial Intelligence. IOS Press, 576–581.Google Scholar
Digital Library
- [31] . 2009. RATEWeb: Reputation assessment for trust establishment among web services. The VLDB Journal 18, 4 (2009), 885–911.Google Scholar
Digital Library
- [32] . 2005. Agent-based trust model involving multiple qualities. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems. 519–526.Google Scholar
Digital Library
- [33] . 2015. Incorporating mitigating circumstances into reputation assessment. In Proceedings of the Advances in Social Computing and Multiagent Systems - 6th International Workshop on Collaborative Agents Research and Development, CARE 2015 and Second International Workshop on Multiagent Foundations of Social Computing. 77–93.Google Scholar
Digital Library
- [34] . 2010. The foundations for provenance on the web. Foundations and Trends in Web Science 2, 2–3 (
Nov. 2010), 99–241.Google ScholarDigital Library
- [35] . 2011. The open provenance model core specification (v1.1). Future Generation Computer Systems 27, 6 (
June 2011), 743–756.Google ScholarDigital Library
- [36] . 2015. The rationale of PROV. Journal of Web Semantics 35, (2015), 235–257.Google Scholar
Digital Library
- [37] . 2015. Autonomic trust management in cloud-based and highly dynamic IoT applications. In Proceedings of the 2015 ITU Kaleidoscope: Trust in the Information Society. 1–8.
DOI: Google ScholarCross Ref
- [38] . 2008. Punishment and counter-punishment in public good games: Can we really govern ourselves? Journal of Public Economics 92, 1–2 (2008), 91–112.Google Scholar
Cross Ref
- [39] . 2014. Trustworthiness management in the social internet of things. IEEE Transactions on Knowledge and Data Engineering 26, 5 (2014), 1253–1266.Google Scholar
Digital Library
- [40] . 2012. A subjective model for trustworthiness evaluation in the social internet of things. In Proceedings of the 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications. 18–23.Google Scholar
Cross Ref
- [41] . 2010. A mechanism that provides incentives for truthful feedback in peer-to-peer systems. Electronic Commerce Research 10, 3 (2010), 331–362.Google Scholar
Digital Library
- [42] . 2013. Computational trust and reputation models for open multi-agent systems: A review. Artificial Intelligence Review 40, 1 (2013), 1–25.Google Scholar
Digital Library
- [43] . 2006. Bayesian reputation modeling in e-marketplaces sensitive to subjecthity, deception and change. In Proceedings of the 21st National Conference on Artificial Intelligence. 1206–1212.Google Scholar
Digital Library
- [44] . 2007. Cooperative interactions: An exchange values model. In Proceedings of the Coordination, Organizations, Institutions, and Norms in Agent Systems II.
Lecture Notes in Computer Science , Vol. 4386. 356–371.Google ScholarDigital Library
- [45] . 2004. Evaluating the ReGreT system. Applied Artificial Intelligence 18, 9–10 (2004), 797–813.Google Scholar
Cross Ref
- [46] . 2005. Review on computational trust and reputation models. Artificial Intelligence Review 24, 1 (2005), 33–60.Google Scholar
Digital Library
- [47] . 2001. REGRET: A reputation model in gregarious societies. In Proceedings of the 4th Workshop on Deception, Fraud and Trust in Agent Societies. 61–69.Google Scholar
- [48] . 2009. Social norm emergence in virtual agent societies. In Proceedings of the Declarative Agent Languages and Technologies VI
Lecture Notes in Computer Science , Vol. 5397. 18–28.Google ScholarDigital Library
- [49] . 2016. Stage: Stereotypical trust assessment through graph extraction. Computational Intelligence 32, 1 (2016), 72–101.
DOI: Google ScholarDigital Library
- [50] . 2016. Informed truthfulness in multi-task peer prediction. In Proceedings of the 2016 ACM Conference on Economics and Computation. ACM, New York, NY, 179–196.Google Scholar
Digital Library
- [51] . 2005. A survey of data provenance in e-science. SIGMOD Record 34, 3 (2005), 31–36.Google Scholar
Digital Library
- [52] . 2014. A survey of internet-of-things: Future vision, architecture, challenges and services. In Proceedings of the 2014 IEEE World Forum on Internet of Things. 287–292.
DOI: Google ScholarCross Ref
- [53] . 2012. An efficient and versatile approach to trust and reputation using hierarchical bayesian modelling. Artificial Intelligence 193 (2012), 149–185.Google Scholar
Digital Library
- [54] . 2005. Coping with inaccurate reputation sources: Experimental analysis of a probabilistic trust model. In Procedings of the 4th International Conference on Autonomous Agents and Multiagent Systems. 997–1004.Google Scholar
Digital Library
- [55] . 2011. Service oriented middleware for the internet of things: A perspective. In Proceedings of the Towards a Service-Based Internet, , , , , and (Eds.). 220–229.Google Scholar
Digital Library
- [56] . 2011. Dynamic sanctioning for robust and cost-efficient norm compliance. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence.Google Scholar
- [57] . 2013. PROV Model Primer. Retrieved January 2021 from http://www.w3.org/TR/prov-primer/.Google Scholar
- [58] . 2007. Formal trust model for multiagent systems. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. 1551–1556.Google Scholar
Digital Library
- [59] . 2004. Filtering out unfair ratings in bayesian reputation systems. In Proceedings of the 7th International Workshop on Trust in Agent Societies, Vol. 6. 106–117.Google Scholar
- [60] . 2011. Incentive-compatible escrow mechanisms. In Proceedings of the 25th AAAI Conference on Artificial Intelligence. 751–757.Google Scholar
Cross Ref
- [61] . 2007. Reputation-enhanced QoS-based web services discovery. In Proceedings of the IEEE International Conference on Web Services. 249–256.Google Scholar
Cross Ref
- [62] . 2012. Combining trust modeling and mechanism design for promoting honesty in e-marketplaces. Computational Intelligence 28, 4 (2012), 549–578.Google Scholar
Digital Library
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A Reputation-based Framework for Honest Provenance Reporting
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