Compressive Sensing Based Positioning Using RSS of WLAN Access Points

Feng, Chen; Au, Wain Sy Anthea; Valaee, Shahrokh; Zhang, Tan

doi:10.1109/infcom.2010.5461981

Cited by 112 publications

(94 citation statements)

References 18 publications

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“…From each grid, we collected 35 RSS values through 5-s intervals, periodically. Discussions in [24,25] showed that using numerous signal values at each grid helps to make the machine easily adapt to the Wi-Fi signal fluctuations. During RSS collections, we also record BSSIDs, which help to identify the RSS values uniquely.…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

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Section: Experimental Data Acquisitionmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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“…Other research activities have been carried out to design robust RSS-based localization algorithms using probabilistic models and calibration enhancements [27], [28], [29], [30]. Compressive sensing based localization has been addressed in [20], [21] for sparse target estimation via 1 -minimization program.…”

Section: A Rss-based Localizationmentioning

confidence: 99%

“…We exploit the use of compressive sensing (CS [19]) techniques to reduce complexity since they allow the recovery of sparse signals with far fewer noisy measurements than that predicted by the Shannon-Nyquist sampling theorem. Unlike several existing solutions [20], [21], we aim to provide an online CS scheme to recover sparse signals by reading and handling dynamic amounts of noisy measurements at runtime in vehicular networks. Feng et al [20] used CS to localize only one mobile target from multiple stable reference nodes, which is vastly simpler than the problem we attempt to solve, which requires looking up multiple targets from a single mobile vehicle.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Lookup of Open WiFi Using Crowdsensing

Liu

et al. 2016

IEEE/ACM Trans. Networking

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Abstract-Open WiFi access points (APs) are demonstrating that they can provide opportunistic data services to moving vehicles. We present CrowdWiFi, a novel system to look up roadside WiFi APs located outdoors or inside buildings. CrowdWiFi consists of two components: online compressive sensing and offline crowdsourcing. Online compressive sensing (CS) presents an efficient framework for the coarse-grained estimation of nearby APs along the driving route, where received signal strength (RSS) values are recorded at runtime, and the number and location of APs are recovered immediately based on limited RSS readings and adaptive CS operations. Offline crowdsourcing assigns the online CS tasks to crowd-vehicles and aggregates answers on a bipartite graphical model. Crowd-server also iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results, and then refines the estimation of APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce computation cost and energy consumption of roadside WiFi lookup, while maintaining satisfactory localization accuracy.

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“…Network based navigation systems use technologies such as GPS/A-GPS, Bluetooth [19], Ultra Wide Band (UWB) [17,18], Wi-Fi [20,22,23,33,34], Radio Frequency Identification (RFID) [21,24], infrared [25] or NFC [5]. Positioning accuracy varies according to the technology implemented.…”

Section: Literature Reviewmentioning

confidence: 99%