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: 3.2). 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: We consider learning correlated equilibria in noncooperative repeated games where players form clusters. In each cluster, players observe the action profile of cluster members and receive local payoffs, associated to performing localized tasks within clusters. Players also acquire global payoffs due to global interaction with players outside cluster, however, are oblivious to actions of those players. A novel adaptive learning algorithm is presented which generates trajectories of empirical frequency of joint plays that converge almost surely to the set of correlated ϵ-equilibria. Thus, sophisticated rational global behavior is achieved by individual player’s simple local behavior.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The articles of this volume will be reviewed individually.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Multivariate data sources with components of different information value seem to appear frequently in practice. Models in which the components change their homogeneity at different times are of significant importance. The fact whether any changes are influential for the whole process is determined not only by the moments of the change, but also depends on which coordinates. This is particularly important in issues such as reliability analysis of complex systems and the location of an intruder in surveillance systems. In this paper we developed a mathematical model for such sources of signals with discrete time having the Markov property given the times of change. The research also comprises a multivariate detection of the transition probabilities changes at certain sensitivity level in the multidimensional process. Additionally, the observation of the random vector is depicted. Each chosen coordinate forms the Markov process with different transition probabilities before and after some unknown moment. The aim of statisticians is to estimate the moments based on the observation of the process. The Bayesian approach is used with the risk function depending on measure of chance of a false alarm and some cost of overestimation. The moment of the system's disorder is determined by the detection of transition probabilities changes at some coordinates. The overall modeling of the critical coordinates is based on the simple game.

Preview (2 Sources)

Available from