Wi-Fi-on devices such as smartphones search for network availability by periodically broadcasting probe requests which encapsulate MAC addresses as device identifiers. To protect identity privacy, modern devices embed random MAC addresses in probe frames, the so-called MAC address randomization. Such randomization disrupts the frame association, inadvertently frustrating identity-oblivious statistical analytic efforts such as people counting and trajectory inference. To address that, we propose Cappuccino , a novel privacy-preserving approach that captures the association of probe requests under MAC address randomization. Cappuccino first estimates pairwise frame correlation and then associates frames over time. For frame correlation, it employs a self-supervised estimator that jointly considers multiple modalities, i.e., information elements, sequence number, and received signal strength. For multiple frame association, Cappuccino formulates frames as a minimum-cost flow optimization. To the best of our knowledge, this is the first piece of work that leverages self-supervised learning to estimate frame correlation based on multiple modalities and formulates the probe request association problem as the network flow optimization. We have conducted extensive experiments in a leading and crowded shopping mall for more than three months. Cappuccino achieves remarkable performance in terms of V-measure scores ( $>0.85$ ).

Locality classification is an important component to enable location-based services. It entails two sequential queries: 1)whether a target is within the site or not, i.e., inside/outside region decision, and 2)if so, which area in the region the target is located, i.e., area classification. Locality classification is hence more coarse-grained and efficient as compared with pinpointing the exact target location in the region. The classification problem is challenging, because fingerprints may not exist outside the region for training. Furthermore, the target may sample an incomplete RSSI vector due to, say, random signal noise, momentary occulsion or scanning duration. The algorithm also has to be computationally efficient.

In fingerprint-based indoor localization, fusing radio frequency (RF) and geomagnetic signals has been shown to achieve promising results. To efficiently collect fingerprints, implicit crowdsourcing can be used, where signals sampled by pedestrians are automatically labeled with their locations on a map. Previous work on crowdsourced fingerprinting is often based on a single signal, which is susceptible to signal bias and labeling error. We study, for the first time, implicit multimodal crowdsourcing for joint RF and geomagnetic fingerprinting. The scheme, termed UbiFin, exploits the spatial correlation among RF, geomagnetic, and motion signals to mitigate the impact of sensor noise, leading to highly accurate and robust fingerprinting without the need for any explicit manual intervention. Using clustering and dynamic programming, UbiFin correlates spatially different signals and filters effectively mislabeled signals. We conduct extensive experiments on our campus and a large multi-story shopping mall. Efficient and simple to implement, UbiFin outperforms other state-of-the-art crowdsourcing schemes to construct RF and geomagnetic fingerprints in terms of accuracy and robustness (cutting fingerprint error by 40 percent in general).

Target tracking refers to positioning mobile objects over time. The targets may be hospital patients, park visitors, mall shoppers, warehouse assets, etc. We consider a novel cooperative system to track targets, where a target carries low-cost RF tag which not only beacons its ID, but also receives and rebroadcasts beacons of tags within a certain hop away. Mobile sensors, equipped with localization and communication modules, are used to capture and forward the beacons to a server to track the targets. Such multi-hop approach greatly extends the sensing range of the mobile sensors, or equivalently, the beaconing range of the tags, leading to cost-effective deployment. We propose Mosent, a highly accurate multi-hop system using mobile sensors for target tracking. To account for complex signal propagation in different indoor and outdoor environment, we represent the received signal strength (RSS) matrix overcoming the assumption on propagation model. Given sensor locations, beacons detected by the sensors and RSS matrix, Mosent jointly considers temporal and spatial information to track targets using a modified particle filter. Mosent has an optional, independent and offline module to learn spatial signal propagation in terms of RSS matrix using cooperative mobile sensors equipped with beaconing transceivers. We have implemented Mosent and conducted extensive experiments. Our results show that Mosent achieves 4.37m and 9.46m tracking error in the campus and the shopping mall, respectively, which outperforms other state-of-the-art approaches with significantly lower tracking error (often by more than 30%).

The geomagnetic field has been wildly advocated as an effective signal for fingerprint-based indoor localization due to its omnipresence and local distinctive features. Prior survey-based approaches to collect magnetic fingerprints often required surveyors to walk at constant speeds or rely on a meticulously calibrated pedometer (step counter) or manual training. This is inconvenient, error-prone, and not highly deployable in practice. To overcome that, we propose Maficon, a novel and efficient pedometer-free approach for geo ma gnetic fi ngerprint database con struction. In Maficon, a surveyor simply walks at casual (arbitrary) speed along the survey path to collect geomagnetic signals. By correlating the features of geomagnetic signals and accelerometer readings (user motions), Maficon adopts a self-learning approach and formulates a quadratic programming to accurately estimate the walking speed in each signal segment and label these segments with their physical locations. To the best of our knowledge, Maficon is the first piece of work on pedometer-free magnetic fingerprinting with casual walking speed. Extensive experiments show that Maficon significantly reduces walking speed estimation error (by more than 20%) and hence fingerprint error (by 35% in general) as compared with traditional and state-of-the-art schemes.