Identifying “superspreaders” of disease is a pressing concern for society during pandemics such as COVID-19. Superspreaders represent a group of people who have much more social contacts than others. The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people’s ubiquitous mobile devices. This technology offers promise for detecting superspreaders. In this paper, we propose a general framework for WLAN-log-based superspreader detection. In our framework, we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions. Next, we adopt three vertex centrality measurements over the contact graphs to generate three groups of superspreader candidates. Finally, we leverage SEIR simulation to determine groups of superspreaders among these candidates, who are the most critical individuals for the spread of disease based on the simulation results. We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university. Our evaluation shows superspreaders exist on university campuses. They change over the first few weeks of a semester, but stabilize throughout the rest of the term. The data also demonstrate that both symmetric and asymmetric contact tracing can discover superspreaders, but the latter performs better with daily contact graphs. Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i.e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework. We believe our proposed framework and these results can provide timely guidance for public health administrators regarding effective testing, intervention, and vaccination policies.

In this research, we conduct a comprehensive measurement study on the predictability of human mobility with respect to demographic differences. We leverage an extensive WLAN dataset collected on a large university campus. Specifically, our dataset includes over 41 million WLAN entries gathered from over 5,000 students (with demographic information) during a four-month period in 2015. We observed surprising patterns on large increases of long-term mobility entropy by age, and the impact of academic majors on students long-term mobility entropy. The distribution of long-term entropy follows a bimodal distribution, which is different from previous studies. We also find that the predictability of students' short-term (daily or weekly) mobility varies on different days of the week and with student gender. Because of the large campus size, our results can mimic people's mobility patterns in metropolitan areas. We also anticipate that our results will provide insight that guides academic administrators' decisions regarding facilities planning, emergency management, etc. on campus.

In this paper, we study vision-based localization for robots. We anticipate that numerous mobile robots will serve or interact with humans in indoor scenarios such as healthcare, entertainment, and public service. Such scenarios entail accurate and scalable indoor visual robot localization, the subject of this work. Most existing vision-based localization approaches suffer from low localization accuracy and scalability issues due to visual environmental features' limited effective range and detection accuracy. In light of infrastructural cameras' wide indoor deployment, this paper proposes BRIDGELOC, a novel vision-based indoor robot localization system that integrates both robots' and infrastructural cameras. BRIDGELOC develops three key technologies: robot and infrastructural camera view bridging, rotation symmetric visual tag design, and continuous localization based on robots' visual and motion sensing. Our system bridges robots' and infrastructural cameras' views to accurately localize robots. We use visual tags with rotation symmetric patterns to extend scalability greatly. Our continuous localization enables robot localization in areas without visual tags and infrastructural camera coverage. We implement our system and build a prototype robot using commercial off-the-shelf hardware. Our real-world evaluation validates BRIDGELOC's promise for indoor robot localization.

Accurate indoor localization is a key enabling technology for numerous applications such as indoor navigation, mobile social networking, and augmented reality. Despite major effort from the research community, state-of-the-art indoor localization performance remains unsatisfactory. Current approaches using radio frequency entail tedious site surveys and have limited accuracy. While vision-based localization techniques are promising, they struggle with human recognition and changing environments. This paper proposes Flash-Loc, an accurate indoor localization system leveraging flashes of light to localize people carrying mobile phones in areas with deployed surveillance cameras. A person's mobile phone emits a sequence of flashes that uniquely “represents” the person from the cameras' view. Flash-Loc develops three key mechanisms that distinguish people while avoiding long irritating flashes: adaptive-length flash coding, pulse width modulation based flash generation, and image subtraction based flash localization. Further, we design a system in which Flash-Loc cooperates with fingerprinting and dead reckoning for accurate human localization. We implement Flash-Loc on commercial off-the-shelf equipment. Our real-world experiments show Flash-Loc achieves accurate indoor localization by itself and in cooperation with other localization technology. In particular, Flash-Loc can localize a user 45 m away from the camera with sub-meter accuracy.

Many mobile robots are expected to work for or interact with humans indoors in applications such as guided shopping, policing, and senior care. Mobile robots' sensors alone are insufficient in order to realize these applications, infrastructural support is needed. Existing support for mobile robots requires heavy or expensive infrastructures with limited scalability or deployment of unsightly lines or magnetic strips. This paper presents S-Mirror, a novel approach that "reflects" various ambient signals towards mobile robots, greatly extending their sensing abilities. S-Mirror forms a network of S-Mirror nodes that mainly reflect visual signals (as well as electronic and acoustic signals) to assist mobile robots. To illustrate the advantages of S-Mirror, we develop a localization approach for mobile robots that integrates S-Mirror and robots' on-board motion sensors. We implement S-Mirror and a mobile robot prototype on commercial off-the-shelf hardware. Our real-world experimental validation shows that S-Mirror achieves accurate timely localization with low network bandwidth consumption as well as robustness and scalability to many mobile robots.

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. Crowds of smart devices offer opportunities to collectively sense and perform computing tasks at an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowd sensing data with differential privacy guarantees. Crowd-ML endows a crowd sensing system with the ability to learn classifiers or predictors online from crowd sensing data privately with minimal computational overhead on devices and servers, suitable for practical large-scale use of the framework. We analyze the performance and scalability of Crowd-ML and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions.

Barrier coverage in visual camera sensor networks (visual barrier coverage) has important real-world applications like battlefield surveillance, environmental monitoring, and protection of government property. Cost-effective deployment, a fundamental issue of visual barrier coverage, considers how to deploy the fewest camera sensors along the barrier to detect intruders (e.g., capture faces) with desirable performance. Existing visual barrier coverage approaches like full-view coverage require numerous camera sensors for capturing intruders' faces deterministically for any trajectory and facing angle. However, intruders' trajectories and facing angles are bounded and deterministic intruder detection requires many camera sensors for rare intrusion cases. Certain practical applications can tolerate limited intrusion mis-detection given budget limitations. This paper proposes local face-view barrier coverage, a novel concept that achieves statistical barrier coverage in camera sensor networks leveraging intruders' trajectory lengths ℓ along the barrier and head rotation angles δ. Using (ℓ, δ) and other parameters, we derive a rigorous probability bound for intruder detection for local face-view barrier coverage via a feasible deployment pattern. Our detection probability bound and deployment pattern can guide practical camera sensor network deployments with camera sensor budgets. Extensive evaluations show that local face-view barrier coverage requires up to 50% fewer camera sensors than full-view barrier coverage.

Electronic channel sounding plays a vital role in developing wireless communication systems. It is critical for transceivers' equalization and filtering operations. However, current pure electronic channel sounding techniques are not well-suited for emerging scenarios such as opportunistic spectrum access, channel impulse response (CIR) based wireless positioning, and wireless security applications, which demand rapid, high-resolution, and spectrum agile channel measurements on commercial off-the-shelf (COTS) devices. To address these critical issues, this paper proposes EV-Sounding, a novel methodology for visual assisted electronic channel sounding. Based on frequency domain channel sounding, EV-Sounding leverages cameras for visual estimation of sparsity locations to reduce the number of frequency samples, thus speeding up the sounding process. EV-Sounding achieves both high-resolution CIR measurements and spectrum agility. We prototype an EV-Sounding system on COTS devices. Our real-world experimental results and extensive simulations validate EV-Sounding's performance.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device-based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular "turfs,â abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

With smartphones' meteoric growth in recent years, leaking sensitive information from them has become an increasingly critical issue. Such sensitive information can originate from smartphones themselves (e.g., location information) or from many Internet sources (e.g., bank accounts, emails). While prior work has demonstrated information flow tracking's (IFT's) effectiveness at detecting information leakage from smartphones, it can only handle a limited number of sensitive information sources. This paper presents a novel IFT tagging strategy using differentiated and dynamic tagging. We partition information sources into differentiated classes and store them in fixed-length tags. We adjust tag structure based on time-varying received information sources. Our tagging strategy enables us to track at runtime numerous information sources in multiple classes and rapidly detect information leakage from any of these sources. We design and implement D2Taint, an IFT system using our tagging strategy on real-world smartphones. We experimentally evaluate D2Taint's effectiveness with 84 real-world applications downloaded from Google Play. D2Taint reports that over 80% of them leak data to third-party destinations; 14% leak highly sensitive data. Our experimental evaluation using a standard benchmark tool illustrates D2Taint's effectiveness at handling many information sources on smartphones with moderate runtime and space overhead.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter protocol that encodes topics to fit in SDP attributes and achieves a low false-positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

The ability to track and locate physical entities is a fundamental requirement for Cyber-Physical Systems (CPSs), especially in an ad-hoc wireless environment. In Heterogeneous Wireless Networks (HWNs), hybrid localization schemes are needed due to the coexistence of both accurate and coarse measurement mechanisms. However, current localization schemes cannot fully satisfy HWNs' accuracy requirements. Therefore, we propose a universal measurement metric called Direct Proportion Distance (DPD) that can leverage most existing measurement mechanisms such as TOA/TDOA, RSS, AOA, Link Diagnosis (LD) and Signal Coverage Detection (SCD). We also prove that DPD is directly proportional to the physical distance between two wireless nodes. Based on this metric, we present three new localization algorithms and compare them with classical methods. The experiments verify that our method performs better than previous localization algorithms when both accurate and coarse measurements are fully utilized.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular “turfs,” abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

Malicious shellcodes are segments of binary code disguised as normal input data. Such shellcodes can be injected into a target process's virtual memory. They overwrite the process's return addresses and hijack control flow. Detecting and filtering out such shellcodes is vital to prevent damage. In this paper, we propose a new malicious shellcode detection methodology in which we take snapshots of the process's virtual memory before input data are consumed, and feed the snapshots to a malicious shellcode detector. These snapshots are used to instantiate a runtime environment that emulates the target process's input data consumption to monitor shellcodes' behaviors. The snapshots can also be used to examine the system calls that shellcodes invoke, these system call parameters, and the process's execution flow. We implement a prototype system in Debian Linux with kernel version 2.6.26. Our extensive experiments with real traces and thousands of malicious shellcodes illustrate our system's performance with low overhead and few false negatives and few false positives.

Smartphones have been widely used in recent years due to their capabilities of supporting many applications from simple Short Message Service messages to complicated Location-based services. It is challenging for smartphones to enable their end users to manage all applications in all possible use cases to protect privacy or sensitive data. However, the security model for smartphone users is still a two-state model in which they can do anything or absolutely nothing, and it is no longer suitable. In this paper, we propose DiffUser, a differentiated user access control model to enhance smartphone security and user privacy. DiffUser classifies smartphone users based on certain sets of user access privileges. We implement a prototype of DiffUser on real-world T-Mobile G1 smartphones. The evaluation results show that our system is lightweight and flexible.

The current 802.11i standard can provide data confidentiality, integrity and mutual authentication in enterprise Wireless Local Area Networks (WLANs). However, secure communication can only be provided after successful authentication and a robust security network association is established. In general, the wireless link layer is not protected by the current standard in WLANs, which leads to many possible attacks, especially in public open-access wireless networks. We argue that regardless of the type of network under consideration, link-layer protection and data confidentiality are of great importance in wireless applications. In this paper, we first identify and analyze the security issues ignored by the current 802.11 security standard. Then we propose our solution to patch the current 802.11i standard and address all those issues with a new dummy authentication key-establishment algorithm. Dummy means no real authentication for a user. In dummy authentication, we apply public-key cryptography's key-establishment technique to the 802.11 MAC protocol. Our solution can provide link-layer data encryption in open-access wireless networks, separate session encryption keys for different users, and protection for important frames such as management and null data frames as well as Extensible Authentication Protocol (EAP) messages.