No need to war-drive: unsupervised indoor localization

By He Wang, Souvik Sen, Ahmed Elgohary, M. Farid, Moustafa Youssef, Romit Roy Choudhury. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2012

870 citations recorded by the source corpus.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c40810216fe036cb5889933e08b7293810fa675e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Lionel Ming-shuan Ni
2. Zimu Zhou
3. Yunhao Liu
4. Zheng Yang
5. S. Chan
6. Suining He
7. Feng Zhao
8. Dimitrios Lymberopoulos

In Electrical and Electronic Engineering

1. Yunhao Liu
2. Zheng Yang
3. Chenshu Wu
4. Zimu Zhou
5. Jie Xiong
6. Kyle Jamieson
7. Lionel Ming-shuan Ni
8. Dina Katabi

In Ocean Engineering

1. Yunhao Liu
2. Zheng Yang
3. Zimu Zhou
4. Chenshu Wu
5. Jie Xiong
6. Kyle Jamieson
7. Lionel Ming-shuan Ni
8. S. Chan

Citations in the corpus

• Accurate indoor localization with zero start-up cost (2014, background): “Several RF localization papers use mobile motion sensors as hints for localization, either coupled with Wi-Fi signal strength for fingerprinting [36, 3, 26] or with GPS measurements [6, 31].”
• Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, background): “The magnetic field anomalies are also used to discriminate indoor and outdoor environment in [18], and as indoor landmarks in [12].”
• Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings (2013, background): “speakers [37], WiFi [15, 50, 20], geo-magnetism [21], HVAC ductwork [41], or a combination of these [49].”
• SpotFi: Decimeter Level Localization Using WiFi (2015, background): “…idea of these approaches is that one would collect fingerprints such as the vector of RSSIs from a specific location to all the APs in range, and if in the future a target were at the same location, it would exhibit the same fingerprint, enabling us to localize [14, 15, 16, 17, 18, 19, 20, 21, 22].”
• UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, methodology): “In [8], authors categorised approaches according to the technique used for localization into several paradigms, including calibration-free localization [9], WLAN based techniques [10], Dead-reckoning [11], simultaneous localization and mapping (SLAM) [12] and multi-modal sensing [13].”
• Phaser: enabling phased array signal processing on commodity WiFi access points (2014, methodology): “To build the radio map without human involvement, follow-on work uses crowdsourcing, step counting, smartphone inertial sensors [18, 26, 33], and GPS [6].”
• Smartphones Based Crowdsourcing for Indoor Localization (2015, background): “Both of them are unreasonable for smartphone-based localization [36].”
• Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, methodology): “Similar to UnLoc, MapCraft leverages the signal landmarks to constrain the location estimation.”
• WiGest: A ubiquitous WiFi-based gesture recognition system (2014, background): “In addition, activities conducted by multiple individuals can be detected simultaneously with good accuracy utilizing multiple receivers and simple features and classifier systems [31], which can be further used in novel application domains, such as automatic indoor semantic identification [34]–[36]”
• Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “As an example, UnLoc [126] looks for certain structure in the buildings-stairs, elevators, escalators, entrances-that force the user to have predictable motion patterns.”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “Unloc [Wang et al. 2012] is another hybrid indoor positioning system that eases the burden of site survey.”
• SenseGen: A deep learning architecture for synthetic sensor data generation (2017, background): “, mobile apps, and other cloud-based big-data analytics) rely on the collection of personal sensory data from devices such as smartphones, wearables, and home IoT devices to provide services such as remote health monitoring [1], location tracking [2], automatic indoor map construction and navigation”
• Locating in fingerprint space: wireless indoor localization with little human intervention (2012, background): “Both of them are unreasonable for smartphone-based localization [32].”
• Walkie-Markie: Indoor Pathway Mapping Made Easy (2013, background): “These motion states can be discriminated using accelerometer with advanced detection mechanisms [20,35], and can thus be excluded in the WiFi-Mark detection.”
• ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, neutral): “Unloc [50] uses internal landmarks with specific RF signatures to recalibrate dead reckoning schemes.”