Results 1 - 10 of 37

Ace: exploiting correlation for energy-efficient and continuous context sensing

by Suman Nath - In Proceedings of the 10th international conference on Mobile systems, applications, and services , 2012
"... We propose ACE (Acquisitional Context Engine), a middle-ware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among var-ious c ..."
Abstract - Cited by 38 (1 self) - Add to MetaCart
We propose ACE (Acquisitional Context Engine), a middle-ware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among var-ious context attributes (e.g., whenever the user is Driving, he is not AtHome). ACE exploits these automatically learned relationships for two powerful optimizations. The first is in-ference caching that allows ACE to opportunistically infer one context attribute (AtHome) from another already-known attribute (Driving), without acquiring any sensor data. The second optimization is speculative sensing that enables ACE to occasionally infer the value of an expensive attribute (e.g., AtHome) by sensing cheaper attributes (e.g., Driving). Our experiments with two real context traces of 105 people and a Windows Phone prototype show that ACE can reduce sens-ing costs of three context-aware applications by about 4.2×, compared to a raw sensor data cache shared across applica-tions, with a very small memory and processing overhead.
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Push the limit of wifi based localization for smartphones

by Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, Fan Ye - In MobiCom (2012), ACM
"... Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achie ..."
Abstract - Cited by 37 (4 self) - Add to MetaCart
Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g.,6 ∼ 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFibased methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.
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FM-based indoor localization

by Yin Chen , Dimitrios Lymberopoulos , Jie Liu , Bodhi Priyantha - Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys12 , 2012
"... ABSTRACT The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fadi ..."
Abstract - Cited by 28 (3 self) - Add to MetaCart
ABSTRACT The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this work, we demonstrate through a detailed experimental study in 3 different buildings across the US, that FM radio signal RSSI values can be used to achieve roomlevel indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5%. More importantly, we experimentally demonstrate that the localization errors of FM and WiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83% (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.
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Will: Wireless indoor localization without site survey

by Chenshu Wu, Zheng Yang, Yunhao Liu, Wei Xi - IEEE Trans. Parallel and Distributed Systems , 2013
"... Abstract—Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Si ..."
Abstract - Cited by 17 (2 self) - Add to MetaCart
Abstract—Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches. I.
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Low Cost Crowd Counting using Audio Tones

by Pravein Govindan Kannan, Seshadri Padmanabha Venkatagiri, Mun Choon Chan, Akhihebbal L. An, Li-shiuan Peh
"... With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the crowd. Ideally, a crowd counting solu ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the crowd. Ideally, a crowd counting solution should be easy to deploy, scalable, energy efficient, be minimally intrusive to the user and reasonably accurate. Existing solutions using data communication or RFID do not meet these criteria. In this paper, we propose a crowd counting solution based on audio tones, leveraging the microphones and speaker phones that are commonly available on most phones, tackling all the above criteria. We have implemented our solution on 25 Android phones and run several experiments at a bus stop, aboard a bus, within a cafeteria and a classroom. Experimental evaluations show that we are able to achieve up to 90 % accuracy and consume 81% less energy than the WiFi interface in idle mode.
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SymPhoney: a coordinated sensing flow execution engine for concurrent mobile sensing applications

by Younghyun Ju, Youngki Lee, Jihyun Yu, Chulhong Min, Insik Shin, Junehwa Song - In SenSys ’12
"... Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent s ..."
Abstract - Cited by 10 (5 self) - Add to MetaCart
Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent sensing workloads raise a new challenge incurring severe resource contention among themselves and with other foreground applications. To address the challenge, we develop SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications. As its key approach, we develop a novel sensing-flow-aware coordination. We first introduce the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams. Leveraging the identified frame structures, SymPhoney develops frame-based coordination and scheduling mechanisms, which effectively coordinates the resource use of concurrent contending applications and maximize their utilities even under severe resource contention. We implemented several sensing applications on top of the SymPhoney engine and performed extensive experiments, showing effective coordination capability of
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Crowd++: Unsupervised speaker count with smartphones,”

by Chenren Xu , Sugang Li , † , Gang Liu , Yanyong Zhang , Emiliano Miluzzo , Yih-Farn Chen , Jun Li , † , Bernhard Firner , † Winlab - in ACM UbiComp, , 2013
"... ABSTRACT Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of p ..."
Abstract - Cited by 8 (3 self) - Add to MetaCart
ABSTRACT Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -with an average error distance of 1.5 speakers -through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment.
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Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service

by Liqun Li , Guobin Shen , Chunshui Zhao , Thomas Moscibroda , Feng Jyh-Han Lin , Zhao Microsoft - In MobiCom , 2014
"... ABSTRACT Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approa ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
ABSTRACT Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach. Realizing the fundamental fact that the quality of the location database capturing the actual radio map dictates localization accuracy, in this paper, we propose Modellet, an algorithmic approach that optimally approximates the actual radio map by unifying modelbased and fingerprint-based approaches. Modellet represents the radio map using a fingerprint-cloud that incorporates both measured real fingerprints and virtual fingerprints, which are computed from models with a local support, based on the key concept of the supporting set. We evaluate Modellet with data collected from an office building as well as 13 large-scale deployment venues (shopping malls and airports), located across China, U.S., and Germany. Comparing Modellet with two representative baseline approaches, RADAR and EZPerfect, demonstrates that Modellet effectively adapts to different data densities and environmental conditions, substantially outperforming existing approaches.
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Accurate indoor localization with zero start-up cost

by Swarun Kumar, Stephanie Gil, Dina Katabi, Daniela Rus - In Proceedings of ACM Annual International Conference on Mobile Computing and Networking (MobiCom , 2014
"... Recent years have seen the advent of new RF-localization sys-tems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adopt ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Recent years have seen the advent of new RF-localization sys-tems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aper-ture Radar (SAR). Past work on SAR requires measuring mechan-ically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse’s core contribution is the ability to perform SAR on hand-held devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localiza-tion with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in com-plex indoor settings.

Social-Loc: Improving Indoor Localization with Social Sensing

by Junghyun Jun, Yu Gu, Long Cheng, Banghui Lu, Ting Zhu, Jianwei Niu
"... Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming in-creasingly popular for mobile applications. While GPS pro-vides accurate outdoor locations, accurate indoor localiza-tion schemes still require either additional infrastructure supp ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming in-creasingly popular for mobile applications. While GPS pro-vides accurate outdoor locations, accurate indoor localiza-tion schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In or-der to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both so-cial encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user’s localization accuracy of WiFi fingerprint and dead-reckoning by at least 22 % and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long du-ration of time, and is robust against measurement errors.
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