Abstract
In this article, we investigate multi-agent techniques to install autonomy and adaptation in IoT-based smart environment settings, like smart home scenarios. We particularly make use of the smart environment configuration problem (SECP) framework, and map it to a distributed optimization problem (DCOP). This consists in enabling smart objects to coordinate and self-configure as to meet both user-defined requirements and energy efficiency, by operating a distributed constraint reasoning process over a computation graph. As to cope with the dynamics of the environment and infrastructure (e.g., by adding or removing devices), we also specify the k-resilient distribution of graph-structured computations supporting agent decisions, over dynamic and physical multi-agent systems. We implement a self-organizing distributed repair method, based on a distributed constraint optimization algorithm to adapt the distribution as to ensure the system still performs collective decisions and remains resilient to upcoming changes. We provide a full stack of mechanisms to install resilience in operating stateless DCOP solution methods, which results in a robust approach using a fast DCOP algorithm to repair any stateless DCOP solution methods at runtime. We experimentally evaluate the performances of these techniques when operating stateless DCOP algorithms to solve SECP instances.
- [1] . 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509–512.Google ScholarCross Ref
- [2] . 2017. Max-sum revisited: The real power of damping. In Proceedings of the Autonomous Agents and Multiagent Systems. Springer International Publishing, 111–124.Google ScholarCross Ref
- [3] . 2003. Constraint Processing. Morgan Kaufmann.Google Scholar
- [4] . 2013-11. Dynamic constraint reasoning in smart environments. In Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. 167–174.Google ScholarDigital Library
- [5] . 2008. Decentralised coordination of low-power embedded devices using the Max-sum algorithm. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. pp. 639–646.Google Scholar
- [6] . 2014. Improving DPOP with branch consistency for solving distributed constraint optimization problems. In Proceedings of the Principles and Practice of Constraint Programming (Cham). 307–323.Google ScholarCross Ref
- [7] . 2017. A multiagent system approach to scheduling devices in smart homes. In Proceedings of the International Workshop on Artificial Intelligence for Smart Grids and Smart Buildings. 7 pages.Google Scholar
- [8] . 2018-03-29. Distributed constraint optimization problems and applications: A survey. Journal of Artificial Intelligence Research 61, 1 (2018-03-29), 623–698.Google ScholarDigital Library
- [9] . 2003. Distributed coordination through anarchic optimization. In Proceedings of the Distributed Sensor Networks. Springer, 257–295.Google ScholarCross Ref
- [10] . 2016. Proactive dynamic distributed constraint optimization. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. 597–605.Google ScholarDigital Library
- [11] . 2017. Infinite-horizon proactive dynamic DCOPs. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. 212–220.Google ScholarDigital Library
- [12] . 2013. DNS-SD. Accessed on 28 Feb., 2022.Google Scholar
- [13] . 2013. mDNS. Accessed on 28/02/2022.Google Scholar
- [14] . 2013-05-01. Coordinating the web of services for a smart home. ACM Transactions on the Web 7, 2 (2013-05-01), 1–40.Google ScholarDigital Library
- [15] . 2018. A near-optimal node-to-agent mapping heuristic for GDL-based DCOP algorithms in multi-agent systems. In Roceedings of the International Conference on Autonomous Agents and Multiagent Systems. 1613–1621.Google Scholar
- [16] . 2017. A realistic dataset for the smart home device scheduling problem for DCOPs. In Proceedings of the International Workshop on Optimisation in Multi-Agent Systems. 125–142.Google ScholarCross Ref
- [17] . 2008. Dynamic distributed constraint reasoning. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence. 1466–1469.Google Scholar
- [18] . 2004. Distributed algorithms for DCOP: A graphical-game-based approach. In Proceedings of the ISCA 17th International Conference on Parallel and Distributed Computing Systems. 432–439.Google Scholar
- [19] . 2017. Resilient distributed constraint optimization problems. In Proceedings of the International Workshop on Optimisation in Multi-Agent Systems. 8 pages.Google Scholar
- [20] . 2011. Data replication. In Proceedings of the Principles of Distributed Database Systems, 3rd Edition. 459–495.Google ScholarCross Ref
- [21] . 2012. Using constraint-based optimization and variability to support continuous self-adaptation. In Proceedings of the 27th Annual ACM Symposium on Applied Computing. 486.Google ScholarDigital Library
- [22] . 2007-12-12. DCOP for smart homes: A case study. Computational Intelligence 23, 4 (2007-12-12), 395–419.Google ScholarCross Ref
- [23] . 2004. A distributed, complete method for multi-agent constraint optimization. In Proceedings of the 5th International Workshop on Distributed Constraint Reasoning. 15.Google Scholar
- [24] . 2005. Superstabilizing, fault-containing distributed combinatorial optimization. In Proceedings of the National Conference on Artificial Intelligence, Vol. 20. 449.Google Scholar
- [25] . 2007-11. Optimal solution stability in dynamic, distributed constraint optimization. In Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. 321–327.Google ScholarDigital Library
- [26] . 2021. Latency-aware local search for distributed constraint optimization. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. 1019–1027.Google ScholarDigital Library
- [27] . 2021. Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs. Applied Intelligence 51, 1 (2021), 1733–1746.Google Scholar
- [28] . 2016. Artificial Intelligence: A Modern Approach (third edition, global edition ed.). Pearson.Google Scholar
- [29] . 2016. Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, (Ed.), AAAI Press, 468–474.Google Scholar
- [30] . 2017. On the deployment of factor graph elements to operate max-sum in dynamic ambient environments. In Proceedings of the Autonomous Agents and Multiagent Systems – AAMAS 2017 Workshops, Best Papers, Sao Paulo, Brazil, May 8-12, 2017, Revised Selected Papers(
Lecture Notes in Artificial Intelligence (LNAI) , Vol. 10642). Springer, 116–137.Google ScholarCross Ref - [31] . 2019. pyDCOP, a DCOP library for IoT and dynamic systems. In Proceedings of the International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019).Google Scholar
- [32] . 2020. Resilient distributed constraint optimization in physical multi-agent systems. In Proceedings of the European Conference on Artificial Intelligence. 195–202.Google Scholar
- [33] . 2014-03. Generalized quadratic multiple knapsack problem and two solution approaches. Computers & Operations Research 43 (2014-03), 78–89. https://www.sciencedirect.com/science/article/pii/S0305054813002244.Google ScholarDigital Library
- [34] . 2013. Self-adaptation with end-user preferences: Using run-time models and constraint solving. In Proceedings of the Model-Driven Engineering Languages and Systems, Vol. 8107. 555–571.Google ScholarDigital Library
- [35] . 1974. Photometry and Radiometry for Engineers.
OCLC: 833226111 .Google Scholar - [36] . 2014. Applying graph theory to the internet of things. In Proceedings of the 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013. 2354–2361.Google Scholar
- [37] . 2015-12. Incremental DCOP search algorithms for solving dynamic DCOP problems. In Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 257–264.Google ScholarDigital Library
- [38] . 1992. Distributed constraint satisfaction for formalizing distributed problem solving. In Proceedings of the 12th International Conference on Distributed Computing Systems. 614–621.Google ScholarCross Ref
- [39] . 2005. Distributed stochastic search and distributed breakout: Properties, comparison and applications to constraint optimization problems in sensor networks. Artificial Intelligence 161, 1–2 (2005), 55–87.Google ScholarCross Ref
Index Terms
- Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments
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