Hande Hong

PiLoc: a self-calibrating participatory indoor localization system

While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths. We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

Accuracy-aware wireless indoor localization

Fingerprint-based indoor localization has attracted extensive research efforts due to its potential for deployment without extensive infrastructure support. However, the accuracies of these different systems vary and it is difficult to compare and evaluate these systems systematically. In this work, we propose a Gaussian process based approach that takes the radio map and the localization algorithm as an input, and outputs the expected accuracy of the localization system. With an efficient error estimation algorithm, many applications such as landmark detection, localization algorithm selection and access point subset selection can be performed. Our evaluations show that our approach provides sufficient accuracy and can serve as a useful tool for system evaluation and performance tuning when developing fingerprint-based indoor localization systems.

iMap: Automatic inference of indoor semantics exploiting opportunistic smartphone sensing

Indoor environment inference is of great importance to mobile and pervasive computing. As high-level metadata of indoor environment, floor maps contain rich information and are widely required in many pervasive systems. However, despite significant research progress, automatic inference of indoor maps has been less studied. In this paper, we present iMap, a smartphone-based opportunistic sensing system that automatically constructs the indoor maps by merging crowdsourced walking trajectories from smart-phone users. Most importantly, indoor semantics, such as stairs, escalators, elevators and doors are also automatically detected and annotated to the constructed map in the same inference process. The evaluation result shows that iMap can accurately detect different indoor semantics and be applied to different indoor environments. With the capability of generating semantic-annotated indoor maps without requiring any prior knowledge of the indoor environment, iMap has the potential to be widely deployed in practice.

SocialProbe: Understanding Social Interaction Through Passive WiFi Monitoring

In this paper, we present an approach to extract social behavior and interaction patterns of mobile users by passively monitoring WiFi probe requests and null data frames that are sent by smartphones for network control/management purposes. By analyzing the temporal and spatial correlations of the Receive Signal Strength Indicators (RSSI) of packets from these low rate transmissions, we are able to discover proximity relationships, occupancy patterns, and social interactions among users. We evaluate the SocialProbe system using commodity off-the-shelf smartphones and WiFi Access Points in two locations, a research lab and a public dining area. The result shows that the proposed approach is able to obtain reliable social relationships and interactions in a non-intrusive way.

MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing

While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.

Demonstration abstract: automatic radio map construction exploiting annotated walking trajectories

In this demonstration, we show a radio map construction system that utilizes opportunistically sensed data contributed by users. Our free-of-infrastructure system releases users from tedious site survey and does not require prior knowledge as floor plan. By merging trajectories collected by different users, the system can automatically generate an indoor floor plan and respective radio map.

CrowdProbe: Non-invasive Crowd Monitoring with Wi-Fi Probe

Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes. Such information can be captured with inexpensive monitors and used to extract user behavior. To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months. We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 1.7 million opportunistically obtained probe request frames. However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior. In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints. With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses.

LiPS: Learning Social Relationships in Probe Space

Understanding users’ social relationships plays an important role in many disciplines including marketing, management science, etc., and is the fundamental context information required in many context-aware applications. However, despite significant research progress in social learning, sensing and capturing users’ daily social relationships in an accurate and non-obtrusive way is still a challenging open problem. In this paper, we propose LiPS, a social learning system that exploits wireless probes emitted by the smartphones carried by users to learn their social relationships. A novel probe filtering and Skipgram-based learning algorithm is adopted to automatically construct the social graph of users in an unobtrusive way. The evaluation results show that the LiPS system is able to accurately reflect the social relationships among smartphone users.

From mapping to indoor semantic queries

Understanding indoor environment in an automatic way is of great importance to mobile and pervasive computing. In this paper, we present Zeus, a smartphone-based opportunistic sensing system that automatically constructs indoor maps by merging crowdsourced walking trajectories captured through smartphone inertial sensing. Most importantly, widely used indoor semantics, such as stairs, escalators, elevators and doors, are also automatically detected and annotated to the constructed maps in the same inference process. Since the final inferred maps provide locations of the different indoor semantics together with localization database, Zeus enables real-time location-based semantic queries. The evaluation result shows that Zeus accurately infers semantic-annotated indoor maps, and provide accurate semantic query in different indoor environments.

Poster abstract: Long-term observation with passive Wi-Fi scanning

Mobile devices, in particular, smartphones are ubiquitous. Many of these devices are able to communicate using the Wi-Fi network. In order to support fast service discovery, Wi-Fi devices broadcast probe request frames to actively look for Wi-Fi Access Points (APs) nearby. These probe requests provide information that allows one to extract useful observation about user behavior. In this paper, we present our results obtained from a long-term passive scanning of Wi-Fi probe request packets collected in a popular outdoor food court in Singapore. The collection was done over a 32-month duration. We present measurements on the breakdown in device vendors, probe intervals, Service Set Identifier (SSID) leakage and a simple counting application.