Cyber-Social Systems: Modeling, Inference, and Optimal Design

被引:21
作者
Doostmohammadian, Mohammadreza [1 ]
Rabiee, Hamid R. [2 ]
Khan, Usman A. [3 ]
机构
[1] Semnan Univ, Dept Mech Engn, Semnan 3513119111, Iran
[2] Sharif Univ Technol, ICT Innovat Ctr Adv Informat & Commun Technol, Sch Comp Engn, Tehran 1136511155, Iran
[3] Tufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
来源
IEEE SYSTEMS JOURNAL | 2020年 / 14卷 / 01期
关键词
Combinatorial optimization; contraction; linear structure-invariant (LSI) systems; observability and estimation; strongly connected component (SCC); OPINION DYNAMICS; SENSOR NETWORKS; ALGORITHMS; OBSERVABILITY; CONSENSUS; STATE; COST;
D O I
10.1109/JSYST.2019.2900027
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper models the cyber-social system as a cyber-network of agents monitoring states of individuals in a social network. The state of each individual is represented by a social node, and the interactions among individuals are represented by a social link. In the cyber-network, each node represents an agent, and the links represent information sharing among agents. The agentsmake an observation of social states and perform distributed inference. In this direction, the contribution of this paper is threefold: First, a novel distributed inference protocol is proposed that makes no assumption on the rank of the underlying social system. This is significant as most protocols in the literature only work on full-rank systems. Second, a novel agent classification is developed, where it is shown that the connectivity requirement of the cyber-network differs for each type. This is particularly important in finding the minimal number of observations and minimal connectivity of the cyber-network as the next contribution. Third, the cost-optimal design of the cyber-network constraint with distributed observability is addressed. This problem is subdivided into sensing cost optimization and networking cost optimization, where both are claimed to be NP-hard. We solve both the problems for certain types of social networks and find polynomial-order solutions.
引用
收藏
页码:73 / 83
页数:11
相关论文
共 56 条
  • [1] Opinion Dynamics and Learning in Social Networks
    Acemoglu, Daron
    Ozdaglar, Asuman
    [J]. DYNAMIC GAMES AND APPLICATIONS, 2011, 1 (01) : 3 - 49
  • [2] [Anonymous], 2013, AM CONTROL C
  • [3] [Anonymous], 2006, STRUCTURAL THEORY SO
  • [4] Bang-Jensen J, 2009, SPRINGER MONOGR MATH, P1, DOI 10.1007/978-1-84800-998-1_1
  • [5] Battistelli G., 2011, 18th IFAC World Congress, P12477, DOI DOI 10.3182/20110828-6-IT-1002.01998
  • [6] On Krause's Multi-Agent Consensus Model With State-Dependent Connectivity
    Blondel, Vincent D.
    Hendrickx, Julien M.
    Tsitsiklis, John N.
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2009, 54 (11) : 2586 - 2597
  • [7] Structural Analysis of the Partial State and Input Observability for Structured Linear Systems: Application to Distributed Systems
    Boukhobza, Taha
    Hamelin, Frederic
    Martinez-Martinez, Sinuhe
    Sauter, Dominique
    [J]. EUROPEAN JOURNAL OF CONTROL, 2009, 15 (05) : 503 - 516
  • [8] Statistical physics of social dynamics
    Castellano, Claudio
    Fortunato, Santo
    Loreto, Vittorio
    [J]. REVIEWS OF MODERN PHYSICS, 2009, 81 (02) : 591 - 646
  • [9] Chapman A, 2013, P AMER CONTR CONF, P6126
  • [10] Observability preservation under sensor failure
    Commault, Christian
    Dion, Jean-Michel
    Trinh, Do Hieut
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2008, 53 (06) : 1554 - 1559