DAISY Data Analysis and Information SecuritY Lab Push the Limit of WiFi based Localization for Smartphones Presenter: Yingying Chen Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen Department of Electrical and Computer Engineering Stevens Institute of Technology Fan Ye IBM T. J. Watson Research Center MobiCom 2012 August 25, 2012 1 The Need for High Accuracy Smartphone Localization Help users navigation inside large and complex indoor environment, e.g., airport, train station, shopping mall. Understand customers visit and stay patterns for business Train Station Shopping Mall 2 Airport Smartphone Indoor Localization - What has been done? Contributions in academic research RADAR [INFOCOM’00], Horus [MobiSys’05], Chen et.al[Percom’08] WiFi indoor localization High accuracy indoor localization Cricket [Mobicom’00], WALRUS [Mobisys’05], DOLPHIN [Ubicomp’04], Gayathri et.al [SECON’09] WiFi enabled smartphone indoor localization SurroundSense [MobiCom’09], Escort [MobiCom’10], WILL[INFOCOM’12], Virtual Compass [Pervasive’10] Commercial productsto achieve high accuracy localization Is it possible using most prevalent WiFi infrastructure? Google Map Shopkick Localization error up to 10 meters Locate at the granularity of stores 3 Root Cause of Large Localization Errors Received Signal Strenth (dBm) Am I here? 45 ~ 2 meters 40 I am around here. 35 30 25 WiFi as-is is not a suitable candidate for high accurate 20 localization due to large errors 15 10 5 0 6 - 8 meters Is it possible to address this fundamental AP 1 limit AP 2without AP 3 the need of additional hardware or infrastructure? AP 4 32: Permanent [ -22dB, -36dB, -29dB, -43dB ] such as furniture placement and walls. environmental settings, Physically distant locations share similar WiFi Received Signal Strength ! 48: Transient [ -24dB, -35dB, factors, -27dB, such as -40dB] dynamic obstacles and interference. Orientation, holding position, time of day, number of samples 4 Inspiration from Abundant Peer Phones in Public Place Increasing density of smartphones in public spaces Peer 1 Peer 2 How to capture the physical constraints? Provide physical constraints from nearby peer phones Target Peer 3 5 Basic Idea Peer 2 Peer 1 Peer 3 Target Exploit acoustic signal/ranging to construct peer constraints Interpolated Received Signal Strength Fingerprint Map WiFi Position Estimation 6 Acoustic Ranging System Design Goals and Challenges Peer assisted localization Exactly what is the algorithm to search for the best fit position and quantify the signal similarity so that to reduce large errors? Fast and concurrent acoustic ranging of multiple phones How to design and detect acoustic signals? Ease of use Need to complete in short time. Not annoy or distract users from their regular activities. 7 System Work Flow WiFi position estimation Peer recruiting & ranging Rigid graph construction Peer recruiting & ranging Peer assisted localization 16 – 20 KHz the impact activities Minimizing Identify nearby peerson users’ regular HTC EVO ADP2 Only phones close enough can detect recruiting signal Fast ranging Peer phones willing to help send their IDs to the server Unobtrusive to human ears Sound signal design Beep emission strategy Robust to noise Employ virtual synchronization based onMall time-multiplexting Airport Train Station scheme Shopping Lab Deploy extra timing buffers to accommodate variations in the reception Change point detection of the schedule at different phones, e.g., 20 ms Acoustic signal detection Correlation method 8 System Work Flow WiFi position estimation Peer recruiting & ranging Rigid graph construction Peer assisted localization Rigid graph construction Construct the graph G and G’ based on initial WiFi position estimation and the acoustic ranging measurements. Rigid Graph G’ based on acoustic ranging Graph G based on WiFi position estimation 9 System Work Flow WiFi position estimation Rigid graph construction Peer recruiting & ranging Peer assisted localization Acoustic ranging graph WiFi based graph Translational Movement Graph Orientation Estimation 10 Peer assisted localization Prototype and Experimental Evaluation Prototype Devices HTC EVO ADP 2 Trace-driven statistical test Feed the training data as WiFi samples Perturb distances with errors following the same distribution in real environments 11 Localization Accuracy Localization performance across different real-world environments (5 peers) 90% error Median error Lab Train Station Shopping Mall Airport Peer assisted method is robust to noises in different environments 12 Overall Latency and Energy Consumption Overall Latency Pose little more latency than required in the original WiFi localization about 1.5 ~ 2 sec Energy Consumption Negligible impact on the battery life • e.g., with additional power consumption at about 320mW on HTC EVO - lasts 12.7 hours with average power of 450mW 13 Discussion Peer Involvement Use incentive mechanism to encourage and compensate peers that help a target’s localization Movements of users Do not pose more constraints on movements than existing WiFi methods Affect the accuracy only during sound-emitting period • Happens concurrently and shorter than WiFi scanning Triggering peer assistance Provides the technology for peer assistance Up to users to decide when they desire such help 14 Conclusion Leverage abundant peer phones in public spaces to reduce large localization errors Aim at the most prevalent WiFi infrastructure Do not require any special hardware Exploit minimum auxiliary COTS sound hardware readily available on smartphones Utilize much more accurate distance estimate through acoustic ranging to capture unique physical constraints Demonstrate our approach successfully pushes further the limit of WiFi localization accuracy Lightweight in computation on smartphones In time not much longer than original WiFi scanning With negligible impact on smartphone’s battery life time 15 Related Work RADAR [INFOCOM’00]: P. Bahl and V. N. Padmanabhan. RADAR: An In-building RF-based User Location and Tracking System. INFOCOM’00. Cricket [Mobicom’00]: N. Priyantha, A. Chakraborty, and H. Balakrishnan. The Cricket Location-support System. MobiCom’00. DOLPHIN [Ubicomp’04]: M. Minami, Y. Fukuju, K. Hirasawa, and S. Yokoyama. DOLPHIN: A Practical Approach for Implementing A Tully Distributed Indoor Ultrasonic Positioning System. Ubicomp’04. WALRUS [Mobisys’05]: G. Borriello, A. Liu, T. Offer, C. Palistrant, and R. Sharp. WALRUS: Wireless Acoustic Location with Room-level Resolution Using Utrasound. MobiSys’05. Horus [MobiSys’05]: M. Youssef and A. Agrawala. The Horus WLAN Location Determination System. MobiSys’05. Beepbeep [Sensys’07]: C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. Beepbeep: A High Accuracy Acoustic Ranging System Using Cots Mobile Devices. Sensys’07. Chen et.al [Percom’08]: S. Chen, Y. Chen and W. Trappe. Exploiting Environmental Properties for Wireless Localization and Location Aware Applications. PerCom’08. Gayathri et.al [SECON’09]: G. Chandrasekaran, M. A. Ergin, J. Yang, S. Liu, Y. Chen, Marco Gruteser and Rich Martin. Empirical Evaluation of the Limits on Localization Using Signal Strength. SECON’09. SurroundSense [MobiCom’09]: M. Azizyan, I. Constandache, and R. R. Choudhury. Surroundsense: Mobile Phone Localization via Ambience Fingerprinting. MobiCom’09. Escort [MobiCom’10]: I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury. Did You See Bob? Using Mobile Phones to Locate People. MobiCom’10. Virtual Compass [Pervasive’10]: N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner. Virtual compass: relative positioning to sense mobile social interactions. Pervasive’10. WILL [INFOCOM’12]: C. Wu, Z. Yang, Y. Liu, and W. Xi. WILL: Wireless Indoor Localization Without Site Survey. INFOCOM’12. 16 Thanks & Questions? 17