Chen Geng, Cheng Lili, Kong Xiaoxian, Zeng Qingtian, Zhang Yu-Dong
College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590 China.
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK.
Mob Netw Appl. 2023;28(5):1950-1963. doi: 10.1007/s11036-023-02165-z. Epub 2023 Jul 28.
Due to buildings blocking GPS and Wi-Fi signals, traditional techniques can't offer the user's required positioning accuracy in resource-constrained underground parking, but the cooperation of agent nodes can provide the exact localization information to improve the positioning accuracy. However, some well-localized agents may not be willing to sacrifice additional power to improve the others' positioning accuracy. To encourage cooperation among nodes and allocate transmission power reasonably, this paper proposes a bidding-auction-based cooperative localization (BACL) algorithm to improve the positioning accuracy of agent nodes by joint node selection incentive and power allocation strategy. Firstly, the contribution of channel parameters and prior localization information of agent nodes for positioning accuracy are quantified and an incentive mechanism of cooperative localization from an economic perspective is proposed. Secondly, a virtual currency incentive rule is developed to compensate agent nodes of cooperative localization reasonably due to the consumption of energy for transmitting their location information. Finally, the simulation results have shown that the proposed BACL algorithm has excellent performance in terms of localization accuracy in resource-constrained scenarios. Compared with the full-power cooperative localization (FPCL) and non-cooperative localization (NCL) algorithms, the proposed BACL algorithm improved the positioning accuracy by 10% and 65%, respectively. Meanwhile, compared with the FPCL algorithm, the proposed algorithm reduced resource consumption by 50%.
由于建筑物阻挡全球定位系统（GPS）和Wi-Fi信号，在资源受限的地下停车场中，传统技术无法提供用户所需的定位精度，但代理节点的协作可以提供精确的定位信息以提高定位精度。然而，一些定位良好的代理节点可能不愿意牺牲额外的功率来提高其他节点的定位精度。为了鼓励节点之间的协作并合理分配传输功率，本文提出了一种基于竞价拍卖的协作定位（BACL）算法，通过联合节点选择激励和功率分配策略来提高代理节点的定位精度。首先，量化了代理节点的信道参数和先验定位信息对定位精度的贡献，并从经济角度提出了一种协作定位激励机制。其次，制定了一种虚拟货币激励规则，以合理补偿协作定位的代理节点因传输其位置信息而消耗的能量。最后，仿真结果表明，所提出的BACL算法在资源受限场景下的定位精度方面具有优异的性能。与全功率协作定位（FPCL）和非协作定位（NCL）算法相比，所提出的BACL算法分别将定位精度提高了10%和65%。同时，与FPCL算法相比，所提出的算法将资源消耗降低了50%。
