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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This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements. Our detection system leverages the existing car stereo infrastructure, in particular, the speakers and Bluetooth network. Our acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. The beeps are spaced in time across the left, right, and if available, front and rear speakers. After sampling the beeps, we use a sequential change-point detection scheme to time their arrival, and then use a differential approach to estimate the phone's distance from the car's center. From these differences a passenger or driver classification can be made. To validate our approach, we experimented with two kinds of phones and in two different cars. We found that our customized beeps were imperceptible to most users, yet still playable and recordable in both cars. Our customized beeps were also robust to background sounds such as music and wind, and we found the signal processing did not require excessive computational resources. In spite of the cars' heavy multi-path environment, our approach had a classification accuracy of over 90%, and around 95% with some calibrations. We also found we have a low false positive rate, on the order of a few percent.

This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90% with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95%) with a lower false positive rate.

Abstract-Secret key generation among wireless devices using physical layer information of radio channel has been an attractive alternative for ensuring security in mobile environments. Received signal strength (RSS) based secret key extraction gains much attention due to its easy accessibility in wireless infrastructure. However, the problem of using RSS to generate keys among multiple devices to ensure secure group communication in practice remains open. In this work, we propose a framework for collaborative key generation among multiple wireless devices leveraging RSS. To deal with mobile devices not within each other's communication range, we employ relay nodes to achieve reliable key extraction. To enable secure group communication, two protocols are developed to perform collaborative group key generation via star and chain topologies respectively. We further provide the theoretic analysis on the achievable secrecy rate for both star and chain topologies in the presence of an eavesdropper. Our prototype development using MICAz motes and extensive experiments using fading trend based key extraction demonstrate the feasibility of using RSS for group key generation in both indoor and outdoor environments, and concurrently achieving a lower bit mismatch rate compared to existing studies.

Abstract-This material is a supplement to the paper "Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing". Section 1 offers related literature review on cooperative spectrum sensing and consensus algorithms. Section 2 presents related notations and models of the consensus-based graph theory. Section 3 offers further analysis of the proposed spectrum sensing scheme including detection threshold settings and convergence properties in terms of detection performance. Section 4 presents the proofs for the convergence of the proposed consensus algorithm, and discusses the convergence of the proposed algorithm under random link failure network models. Section 5 shows additional simulation results.Index Terms-Cooperative spectrum sensing, Weighted average consensus, Cognitive radio networks. ! RELATED LITERATURE REVIEW Related Work in Cooperative Spectrum SensingThe main advantage of cooperative spectrum sensing is to enhance the sensing performance by exploiting the observation diversity of spatially located SUs [1]. By cooperation, CR users can share their sensing information to make a combined decision which is more accurate than individual decisions. Cooperative sensing usually contains two stages: sensing and fusion. In the sensing stage, each SU makes the measurement using appropriate detecting techniques. Among all types of detectors, energy detector is widely applied because it requires lower design complexity and no priori knowledge of primary users, compared to other techniques such as matched filter detection or cyclostationary detection [2]. In the fusion stage, the SU network cooperatively combines the detecting statistics throughout the network and the final decision is made using global information. The key element of cooperative sensing is the cooperation scheme, which decides the SU network structure and the detecting performance. Centralized cooperative sensing and relay-assisted cooperative sensing are two major schemes in literature [1]. Centralized cooperative sensing [5] lets all SUs report their measurement information to a centralized fusion center, then a global decision is made at the fusion center according to certain measurement combining methods. Relay-assisted cooperative sensing [1][6] is a multi-hop cooperation scheme which makes use of the strong sensing channels and strong reporting channels among the SU network in order to improve the overall performance. Relay-assisted sensing can be either centralized with a fusion center, or distributed without a fusion center. Centralized cooperative spectrum sensing requires the entire received data be gathered at one place which may be difficult due to communication constraints [7]. The multi-hop communication of the relay-assisted sensing may bring extra power cost than one-hop communication, since all SUs' sensing data need to be relayed from the network nodes to the fusion center or detection node. In addition, the multi-hop communication paths may degrade the sensing data quality and affect the detection performance sig...

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We investigate using smartphone WiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person's waiting and service times. We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a 10 second resolution.