Article

Coalition Formation for Bearings-Only Localization in Sensor Networks—A Cooperative Game Approach

Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada
IEEE Transactions on Signal Processing (Impact Factor: 2.81). 09/2010; DOI: 10.1109/TSP.2010.2049201
Source: IEEE Xplore

ABSTRACT In this paper, formation of optimal coalitions of nodes is investigated for data acquisition in bearings-only target localization such that the average sleep time allocated to the nodes is maximized. Targets are required to be localized with a prespecified accuracy where the localization accuracy metric is defined to be the determinant of the Bayesian Fisher information matrix (B-FIM). We utilize cooperative game theory as a tool to devise a distributed dynamic coalition formation algorithm in which nodes autonomously decide which coalition to join while maximizing their feasible sleep times. Nodes in the sleep mode do not record any measurements, hence, save energy in both sensing and transmitting the sensed data. It is proved that if each node operates according to this algorithm, the average sleep time for the entire network converges to its maximum feasible value. In numerical examples, we illustrate the tradeoff between localization accuracy and the average sleep time allocated to the nodes and demonstrate the superior performance of the proposed scheme via Monte Carlo simulations.

0 Bookmarks
 · 
107 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Conference code: 93004, Export Date: 21 December 2012, Source: Scopus, Art. No.: 6268747, doi: 10.1109/WPNC.2012.6268747, Language of Original Document: English, Correspondence Address: Hadzic, S.; Instituto de Telecomunica̧ões, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal; email: senka@av.it.pt, References: Lieckfeldt, D., You, J., Timmermann, D., Distributed selection of references for localization in wireless sensor networks Proceedings of the 5th Workshop on Positioning, Navigation and Communication (WPNC), 2008, pp. 31-36;, Sponsors: IEEE
    WPNC'12 - Proceedings of the 2012 9th Workshop on Positioning, Navigation and Communication; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a game-theoretic approach to node activation control in parameter estimation via diffusion least mean squares (LMS). Nodes cooperate by exchanging estimates over links characterized by the connectivity graph of the network. The energy-aware activation control is formulated as a noncooperative repeated game where nodes autonomously decide when to activate based on a utility function that captures the trade-off between individual node's contribution and energy expenditure. The diffusion LMS stochastic approximation is combined with a game-theoretic learning algorithm such that the overall energy-aware diffusion LMS has two timescales: the fast timescale corresponds to the game-theoretic activation mechanism, whereby nodes distributively learn their optimal activation strategies, whereas the slow timescale corresponds to the diffusion LMS. The convergence analysis shows that the parameter estimates weakly converge to the true parameter across the network, yet the global activation behavior along the way tracks the set of correlated equilibria of the underlying activation control game.
    IEEE Journal of Selected Topics in Signal Processing 01/2013; 7(5):821-836. · 3.30 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a decentralized adaptive filtering algorithm where each agent acts selfishly to maximize its payoff. Agents are only aware of the actions of other agents within their coalitions and have no knowledge of the actions of agents outside the coalition. We show that the global behavior of the system converges to the set of correlated equilibria. Thus simple behavior by individual agents can result in sophisticated global behavior.
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011

Full-text (2 Sources)

View
2 Downloads
Available from