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Energy efficiency and sensing accuracy have both been attractive research fields in sensor networks. Achieving both objectives is possible in a compromise model. In this paper we formulate one such problem and use a game theoretic approach for its solution. The interaction between sensor nodes is modeled as a cooperative bargaining game, where individual sensors cooperate for achieving the application sensing requirements while minimizing and balancing the energy consumption. We use Kalai-Smordinsky Bargaining Solution to find a distribution rule that optimizes the trade-off in the compromise problem. Based on the distribution rule, we propose a lightweight distributed algorithm in order to schedule nodes for performing the sensing task. Simulation shows a superiority in terms of scalability over a similar earlier work, while a comparable achievement in network lifetime improvement is obtained at the same time.