DMCHS: An energy-accuracy tradeoff scheme for HetNet-based indoor localization framework

Xia, Jiangjiang; Xu, Chaojie; Liu, Zhongling; Yu, Hui

doi:10.1109/wcsp.2015.7341283

Cited by 1 publication

(3 citation statements)

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“…However, the fusion method causes high energy consumption. In order to achieve energy efficient localization, trade-off methods have been adopted to implement energy-efficient localization [16][17][18][19][20]. For instance, in [16,17], the authors assumed that WiFi received signal strength (RSS) follows the log-normal distance path loss (LDPL) model and established mathematical models to build a fusion localization system for energy-accuracy trade-off.…”

Section: Related Workmentioning

confidence: 99%

“…In [19], the authors developed an energy-efficient localization system by adjusting the number of reference localization according to the environment. In [20], the authors proposed a dynamic multicluster header selection method based on an energy model in a HetNetbased indoor localization framework for energy-accuracy trade-off.…”

Section: Related Workmentioning

confidence: 99%

“…In the first point, the Euclidean distance between the WiFi result and the last fusion result is 3.12 m, and the Euclidean distance between the PDR result and the last fusion result is 8.44 m. Because of relatively high accuracy for PDR in the short term and the outliers of WiFi localization, we believe that PDR is relatively more accurate than WiFi localization in the short term. Hence, the fusion weight is calculated as 0.55 by using Equation (20). Consequently, the first fusion location is almost in the middle of the WiFi localization location and the PDR location.…”

Section: Experiments Of Energy-accuracymentioning

confidence: 99%

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A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR

Yan-kan

Huang

et al. 2023

Wireless Communications and Mobile Computing

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Fusing WiFi fingerprint localization and pedestrian dead reckoning (PDR) on smartphones is attractive because of their obvious complementarity in localization accuracy and energy consumption. Although fusion localization algorithms tend to improve localization accuracy, extra hardware and software involved will result in extra computations, such that energy consumption is inevitably increased. Thus, in this study, we propose a novel fusion localization scheme based on fuzzy logic, which aims to achieve ideal localization accuracy by consuming as little energy as possible. Specifically, energy-efficient inertial measurement unit (IMU) sensors are routinely called to provide the displacement of a smartphone user in the manner of PDR, whereas a fuzzy inference system is employed to adaptively schedule energy-hungry WiFi scans to fulfill WiFi fingerprint localization according to a coarse metric for fusion localization errors and the remaining energy of the smartphone, so as to achieve a trade-off between localization accuracy and energy consumption. Moreover, in order to mitigate the effect of drift errors induced by PDR, straight trajectories of the user are further identified using a series of WiFi localization results to calibrate heading estimates of PDR. Extensive experimental results demonstrate that the proposed scheme achieves the same accuracy as the complementary filter, but consumes 38.02% energy than the complementary filter, confirming that the proposed scheme can effectively balance the localization accuracy and energy consumption.

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