One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage quality by turning off redundant sensors and adjusting the direction of the active sensor nodes. In this paper, we address the problem of maximizing network lifetime with adjustable ranges (MNLAR) and propose a new game theory-based algorithm in which sensor nodes try to adjust their working direction and sensing range in a distributed manner to achieve the desired coverage. For this purpose, we formulate this problem as a multiplayer repeated game in which each sensor as a player tries to maximize its utility function which is designed to capture the tradeoff between target coverage and energy consumption. To achieve an efficient action profile, we present a distributed payoff-based learning algorithm. The performance of the proposed algorithm is evaluated via simulations and compared to some existing methods. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of network lifetime. Manuscript profile