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.

Device-free passive (DfP) localization is proposed to localize human subjects indoors by observing how the subject disturbs the pattern of the radio signals without having the subject wear a tag. In our previous work, we have proposed a probabilistic classification based DfP technique, which we call PC-DfP in short, and demonstrated that PC-DfP can classify which cell (32 cells in total) is occupied by the stationary subject with an accuracy as high as 97.2% in a one-bedroom apartment. In this poster, we focus on extending PC-DfP to track a mobile subject in indoor environments by taking into consideration that a human subject's locations should form a continuous trajectory. Through experiments in a 10 × 15 meters open plan office, we show that we can achieve better accuracies by exploiting the property of continuous mobility trajectories.

Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood.

Device-free passive localization (DfP) techniques can localize human subjects without wearing a radio tag. Being convenient and private, DfP can find many applications in ubiquitous/pervasive computing. Unfortunately, DfP techniques need frequent manual recalibration of the radio signal values, which can be cumbersome and costly. We present SenCam, a sensor-camera collaboration solution that conducts automatic recalibration by leveraging existing surveillance camera(s). When the camera detects a subject, it can periodically trigger recalibration and update the radio signal data accordingly. This technique requires camera access occasionally each month, minimizing computational costs and reducing privacy concerns when compared to localization techniques solely based on cameras. Through experiments in an open indoor space, we show that this scheme can retain good localization results while avoiding manual recalibration.

In the emerging paradigm of pervasive computing, applications change their behaviors in response to their environmental context, which is provided by the smart objects in the Internet of Things (IoT). Due to the inherent heterogeneity of physical world objects, realizing the IoT requires service layers to fill the gap between the low level interfaces of networked objects and the applications which use them. In this paper, we show that the MobilityFirst Future Internet Architecture is an ideal platform for realizing pervasive computing in an IoT framework. In particular, MobilityFirst's identity based routing, overloaded identities, content caching and in-network compute plane are excellent building blocks for IoT applications. We then present a detailed example of a location based service built using MobilityFirst.

In this paper, we present the design, implementation and evaluation of a RF-based device-free passive localization strategy using active RFID nodes. Patterns of the measured power on multiple radio links are used to determine the location of a person in a room in a home environment. We develop an adaptive algorithm and training technique to minimize multi-path effects. With experimental deployment in a 5 x 8 meters room, we demonstrate that our system can successfully localize an individual to a 30-inch grid square with an 97.2% accuracy and 0.36 meters average error distance.

This paper studies the effects of user mobility and handoff path rerouting on the traffic distributions in a mobile network environment. In mobile ATM networks, extra traffic load may be added to network links due to user mobility and handoff path rerouting. This requires higher network link capacity and possible topology reengineering in order to support the same quality of service (QoS) for mobile services. To capture the dynamic variations in mobile ATM networks, we propose to use a flow model. The model represents the mobile-generated traffic as a set of stochastic flows over a set of origin-destination (OD) pairs. The user mobility is defined by transfer probabilities of the flows and the handoff path rerouting algorithm is modeled by a transformation between the routing functions for traffic flows. The analysis shows that user mobility may cause temporal variations as well as smoothing effects on the network traffic. Using the flow network model, typical handoff path rerouting algorithms are evaluated through both analytical and experimental approaches. The evaluation methodology can be used for either redesigning the network topology for a given path rerouting algorithm or selecting a path rerouting algorithm for a given network topology under a specific mobile service scenario.

This paper studies mobile-generated traffic distributions in mobile ATM networks and evaluates the performance of path rerouting algorithms for handoff control. In mobile ATM networks, user mobility and handoff path rerouting may produce extra traffic load over network links, requiring larger network capacity to support the same QoS. We propose a flow model for mobile ATM networks. The model represents the mobile-generated traffic as a set of stochastic flows over a set of OD (origin-destination) pairs. The user mobility is defined by transfer probabilities of the flows and the handoff path rerouting algorithm is modeled by a transformation between the routing functions for traffic flows. The analysis shows that user mobility may cause temporal variations as well as smoothing effects on the network traffic. Using the flow network model, typical handoff path rerouting algorithms are evaluated through both analytical and experimental approaches. The evaluation methodology can be used for either redesigning the network topology for a given path rerouting algorithm or selecting a path rerouting algorithm for a given network topology under a specific mobile service scenario.

This paper studies handoff control mechanisms based on a 'mobile ATM' concept. Mobile ATM refers to an ATM infrastructure which supports ubiquitous mobile services through extending ATM signaling functions within the ATM backbone. In order to meet the performance criteria required by a diverse set of mobile services, we propose a unified handoff control protocol which is independent of wireless access technologies (ATM/nonATM) and path rerouting algorithms. Such a protocol can provide (1) a common software architecture for system implementation, (2) a common measurement base for performance evaluation and (3) a common signaling syntax for standardization. A prototype system is implemented based on this protocol, which provides an IP-over-ATM service with WaveLAN access.