On information filtering in social sensing

For decades, from the invention of Sensor Networks, people envisioned a global sensing platform with millions of sensors deployed globally. The platform has finally become real recently with the advent of multiple online social network services where humans act as sensors and the social networks act as sensor networks, a practice named Social Sensing. Social sensing was born with the advances of high-level semantics sensing (since humans are the “sensors” with texts or photos as the sensing data) and (almost) zero-cost real-time data infrastructure, which makes this new sensing paradigm very promising in multiple real-world applications including disaster response and global event discovery. However, its global scale results in a massive amount of data generated and collected in applications that far exceeds normal people’s cognitive capability of information consumption, thus we desire a system that can filter the massive sensing data and delivers only information and intelligence to the users with a human-consumable amount. In this thesis, I focus on designing an information filtering system for social sensing; specifically, I focus on three levels of information filtering. In the first level, we focus on untruthful information removal, also known as fact-finding, where the challenge lies in the unknown reliability of each individual social sensor (i.e. human) a prior. In the second level, we focus on event-level information summary, also known as event detection, where the challenge lies in de-multiplexing different event instances and fusing social events detected in multiple social networks that previous approaches do not perform well. In the third level, we focus on information-maximizing data delivery to social sensing users, especially on redundancy removal by diversifying the information feed, where the challenge lies in algorithm design that not only works well empirically but also has a theoretical performance guarantee. We address the above challenges by algorithm design and system implementation and real-world data evaluations verify the efficiency of our proposed solutions

Interference Management in Dense 802.11 Networks

Wireless networks are growing at a phenomenal rate. This growth is causing an overcrowding of the unlicensed RF spectrum, leading to increased interference between co-located devices. Existing decentralized medium access control (MAC) protocols (e.g. IEEE 802.11a/b/g standards) are poorly designed to handle interference in such dense wireless environments. This is resulting in networks with poor and unpredictable performance, especially for delay-sensitive applications such as voice and video. This dissertation presents a practical conflict-graph (CG) based approach to designing self-organizing enterprise wireless networks (or WLANs) where interference is centrally managed by the network infrastructure. The key idea is to use potential interference information (available in the CG) as an input to algorithms that optimize the parameters of the WLAN.We demonstrate this idea in three ways. First, we design a self-organizing enterprise WLAN and show how the system enhances performance over non-CG based schemes, in a high fidelity network simulator. Second, we build a practical system for conflict graph measurement that can precisely measure interference (for a given network configuration) in dense wireless environments. Finally, we demonstrate the practical benefits of the conflict graph system by using it in an optimization framework that manages associations and traffic for mobile VoIP clients in the enterprise. There are a number of contributions of this dissertation. First, we show the practical application of conflict graphs for infrastructure-based interference management in dense wireless networks. A prototype design exhibits throughput gains of up to 50% over traditional approaches. Second, we develop novel schemes for designing a conflict graph measurement system for enterprise WLANs that can detect interference at microsecond-level timescales and with little network overhead. This allows us to compute the conflict graph up to 400 times faster as compared to the current best practice proposed in the literature. The system does not require any modifications to clients or any specialized hardware for its operation. Although the system is designed for enterprise WLANs, the proposed techniques and corresponding results are applicable to other wireless systems as well (e.g. wireless mesh networks). Third, our work opens up the space for designing novel fine-grained interference-aware protocols/algorithms that exploit the ability to compute the conflict graph at small timescales. We demonstrate an instance of such a system with the design and implementation of an architecture that dynamically manages client associations and traffic in an enterprise WLAN. We show how mobile clients sustain uninterrupted and consistent VoIP call quality in the presence of background interference for the duration of their VoIP sessions

Enabling Context-Awareness in Mobile Systems via Multi-Modal Sensing

<p>The inclusion of rich sensors on modern smartphones has changed mobile phones from simple communication devices to powerful human-centric sensing platforms. Similar trends are influencing other personal gadgets such as the tablets, cameras, and wearable devices like the Google glass. Together, these sensors can provide</p><p>a high-resolution view of the user's context, ranging from simple information like locations and activities, to high-level inferences about the users' intention, behavior, and social interactions. Understanding such context can help solving existing system-side</p><p>challenges and eventually enable a new world of real-life applications. </p><p>In this thesis, we propose to learn human behavior via multi-modal sensing. The intuition is that human behaviors leave footprints on different sensing dimensions - visual, acoustic, motion and in cyber space. By collaboratively analyzing these footprints, the system can obtain valuable insights about the user. We show that the</p><p>analysis results can lead to a series of applications including capturing life-logging videos, tagging user-generated photos and enabling new ways for human-object interactions. Moreover, the same intuition may potentially be applied to enhance existing</p><p>system-side functionalities - offloading, prefetching and compression.</p>Dissertatio

Physical layer security for IoT applications

The increasing demands for Internet of things (IoT) applications and the tremendous increase in the volume of IoT generated data bring novel challenges for the fifth generation (5G) network. Verticals such as e-Health, vehicle to everything (V2X) and unmanned aerial vehicles (UAVs) require solutions that can guarantee low latency, energy efficiency,massive connectivity, and high reliability. In particular, finding strong security mechanisms that satisfy the above is of central importance for bringing the IoT to life. In this regards, employing physical layer security (PLS) methods could be greatly beneficial for IoT networks. While current security solutions rely on computational complexity, PLS is based on information theoretic proofs. By removing the need for computational power, PLS is ideally suited for resource constrained devices. In detail, PLS can ensure security using the inherit randomness already present in the physical channel. Promising schemes from the physical layer include physical unclonable functions (PUFs), which are seen as the hardware fingerprint of a device, and secret key generation (SKG) from wireless fading coefficients, which provide the wireless fingerprint of the communication channel between devices. The present thesis develops several PLS-based techniques that pave the way for a new breed of latency-aware, lightweight, security protocols. In particular, the work proposes: i) a fast multi-factor authentication solution with verified security properties based on PUFs, proximity detection and SKG; ii) an authenticated encryption SKG approach that interweaves data transmission and key generation; and, iii) a set of countermeasures to man-in-the-middle and jamming attacks. Overall, PLS solutions show promising performance, especially in the context of IoT applications, therefore, the advances in this thesis should be considered for beyond-5G networks

Eyes-Off Physically Grounded Mobile Interaction

This thesis explores the possibilities, challenges and future scope for eyes-off, physically grounded mobile interaction. We argue that for interactions with digital content in physical spaces, our focus should not be constantly and solely on the device we are using, but fused with an experience of the places themselves, and the people who inhabit them. Through the design, development and evaluation of a series ofnovel prototypes we show the benefits of a more eyes-off mobile interaction style.Consequently, we are able to outline several important design recommendations for future devices in this area.The four key contributing chapters of this thesis each investigate separate elements within this design space. We begin by evaluating the need for screen-primary feedback during content discovery, showing how a more exploratory experience can be supported via a less-visual interaction style. We then demonstrate how tactilefeedback can improve the experience and the accuracy of the approach. In our novel tactile hierarchy design we add a further layer of haptic interaction, and show how people can be supported in finding and filtering content types, eyes-off. We then turn to explore interactions that shape the ways people interact with aphysical space. Our novel group and solo navigation prototypes use haptic feedbackfor a new approach to pedestrian navigation. We demonstrate how variations inthis feedback can support exploration, giving users autonomy in their navigationbehaviour, but with an underlying reassurance that they will reach the goal.Our final contributing chapter turns to consider how these advanced interactionsmight be provided for people who do not have the expensive mobile devices that areusually required. We extend an existing telephone-based information service to support remote back-of-device inputs on low-end mobiles. We conclude by establishingthe current boundaries of these techniques, and suggesting where their usage couldlead in the future

LVMM: The Localized Vehicular Multicast Middleware - a Framework for Ad Hoc Inter-Vehicles Multicast Communications

This thesis defines a novel semantic for multicast in vehicular ad hoc networks (VANETs) and it defines a middleware, the Localized Vehicular Multicast Middleware (LVMM) that enables minimum cost, source-based multicast communications in VANETs. The middleware provides support to find vehicles suitable to sustain multicast communications, to maintain multicast groups, and to execute a multicast routing protocol, the Vehicular Multicast Routing Protocol (VMRP), that delivers messages of multicast applications to all the recipients utilizing a loop-free, minimum cost path from each source to all the recipients. LVMM does not require a vehicle to know all other members: only knowledge of directly reachable nodes is required to perform the source-based routing

Group-based secure communication for wireless sensor networks

Wireless Sensor Networks (WSNs) are a newly developed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. Continuous growth in the use of Wireless Sensor Networks (WSN) in sensitive applications such as military or hostile environments and also generally has resulted m a requirement for effective security mechanisms in the system design In order to protect the sensitive data and the sensor readings, shared keys should be used to encrypt the exchanged messages between communicating nodes. Many key management schemes have been developed recently and a serious threat highlighted in all of these schemes is that of node capture attacks, where an adversary gains full control over a sensor node through direct physical access. This can lead an adversary to compromise the communication of an entire WSN. Additionally ignoring security issues related to data aggregation can also bring large damage to WSNs. Furthermore, in case an aggregator node, group leader or cluster head node fails there should be a secure and efficient way of electing or selecting a new aggregator or group leader node in order to avoid adversary node to be selected as a new group leader. A key management protocol for mobile sensor nodes is needed to enable them to securely communicate and authenticate with the rest of the WSN