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A Semi Distributed Approach for Feasible Min-Max Fair Agent-assignment Problem with Privacy Guarantees
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0001-9810-3478
2016 (English)In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. PP, no 99, article id 7565611Article in journal (Refereed) Published
Abstract [en]

In cyberphysical systems, a relevant problem is assigning agents to slots by distributed decisions capable to preserve agent's privacy. For example, in future intelligent transportation systems, city-level coordinators may optimally assign cars (the agents) to parking slots depending on the cars' distance to final destinations so to ensuring social fairness and without disclosing or even using the car's destination information. Unfortunately, these assignment problems are combinatorial, whereas traditional solvers are exponentially complex, are not scalable, and do not ensure privacy of the agents' intended destinations. Moreover, no emphasis is placed to optimise the agents' social benefit. In this paper, the aggregate social benefit of the agents is considered by an agent-slot assignment optimization problem whose objective function is the fairness among the agents. Due to the problem's complexity, the problem is solved by an approximate approach based on Lagrange duality theory that allows to develop an iterative semi-distributed algorithm. It is shown that the proposed algorithm is gracefully scalable compared to centralised methods, and that it preserves privacy in the sense that an eavesdropper will not be able to discover the destination of any agent during the algorithm iterations. Numerical results illustrate the performance and trade-off of the proposed algorithm compared to the ideal optimal assignment and a greedy method.

Place, publisher, year, edition, pages
IEEE Press, 2016. Vol. PP, no 99, article id 7565611
Keywords [en]
algorithms, Intelligent transportation systems, optimization methods, privacy, Combinatorial optimization, Data privacy, Economic and social effects, Embedded systems, Intelligent systems, Intelligent vehicle highway systems, Numerical methods, Optimization, Transportation, Assignment problems, Cyber physical systems (CPSs), Distributed decision, Objective functions, Optimal assignment, Optimization method, Optimization problems, Iterative methods
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-201798DOI: 10.1109/TCNS.2016.2609151ISI: 000427871900030Scopus ID: 2-s2.0-84991811254OAI: oai:DiVA.org:kth-201798DiVA, id: diva2:1075183
Note

QC 20170217

Available from: 2017-02-17 Created: 2017-02-17 Last updated: 2018-05-29Bibliographically approved

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