Location information is attracting more and more attention due to the increasing awareness of its importance for a wide range of applications. Whether it is for tracking valuable assets in the industrial context or monitoring the motion of elderly people and patients to prevent accidents or offering a better shopping experience using proximity and positioning marketing, an accurate and efficient indoor positioning has become crucial. Aware of this increasing need, we present in this paper “EIPSO” a new indoor 3D positioning algorithm based on Game Theory. An emphasis has been put on the energy efficiency of the proposed solution using “mGAPS” a hybrid micro-genetic and pattern search optimization process to reach the Nash equilibrium. A series of real experiments using Zigbee technology have been conducted on a testbed in LaRINa laboratory at the University of Carthage to evaluate the performances of the proposed solution. The obtained results prove the dynamic behavior of EIPSO which adapts the transmit power to both the signal quality and the battery level of the considered nodes. Moreover, a significant improvement of more than 40% of the network lifetime has been observed when using EIPSO while maintaining the positioning accuracy at a high level (≥ 90%).

由于人们越来越意识到位置信息对广泛应用的重要性，因此位置信息正吸引越来越多的关注。无论是在工业环境中跟踪有价值的资产，还是监测老年人和患者的运动以防止事故发生，还是使用邻近和定位营销提供更好的购物体验，准确高效的室内定位都变得至关重要。意识到这种日益增长的需求，我们在本文中提出了一种基于博弈论的新室内 3D 定位算法“EIPSO”。使用“mGAPS”一种混合微遗传和模式搜索优化过程来达到纳什均衡的建议解决方案的能源效率得到了强调。一系列使用 Zigbee 技术的真实实验已经在迦太基大学 LaRINa 实验室的测试台上进行，以评估所提出解决方案的性能。获得的结果证明了 EIPSO 的动态行为，它使发射功率适应信号质量和所考虑节点的电池电量。此外，在使用 EIPSO 时，网络寿命显着提高了 40% 以上，同时保持高水平的定位精度（≥ 90%）。