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: 2.79). 09/2010; 58(8):4322 - 4338. DOI: 10.1109/TSP.2010.2049201
Source: IEEE Xplore


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.

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    • "Resource management problem of multiagent systems can be addressed nicely with the framework of a cooperative game, which is appropriate for range-only localization where localization is essentially achieved by multilateration [14]. Cooperative game theory suggests that a necessary condition for coalition formation is that the coalition is stable, in the sense that no members of the coalition have any incentive to walk away from it [15]. The best-known solution concept formalizing this idea is the core. "
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    ABSTRACT: In this paper, power allocation in distributed multiple-input multiple-output (MIMO) radar is investigated for range-only target tracking such that the determinant of Bayesian Fisher information matrix (B-FIM) is maximized. First, the B-FIM is derived for a signal model that incorporates the propagation path loss, the target reflectivity, the transmitted power and target prior density. Then we model the problem as a cooperative game and exploit the solution concept of the Shapley value to distribute a given power budget among all transmitting radars for target tracking. In numerical examples, it is shown that uniform power allocation is not in general optimal. We illustrate the effects of the radar network geometry configuration, target prior density and number of antenna upon the power allocation results, and further demonstrate the superior performance of the proposed optimal power allocation scheme via Monte Carlo simulations.
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    • "Node selection strategies have also been dealt with cooperative games that require agreements between devices. The idea of forming the best group of anchor nodes for positioning was addressed in [11]. The work presents a distributed, cooperative, game-theoretic scheme for energy-efficient data acquisition in bearings-only localization. "
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    • "The motivation for our algorithms stems from sensor networks. Consider a bearings-only multi-target tracking scenario [4] where localization is necessarily achieved by triangularization amongst angle measurements of the sensors. Sensors can decide whether to be " active " and take measurements or go to " sleep " . "
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    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.
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