Privacy concerns over the security of personal information have grown in tandem with the spread of smartwatches. However, effective methods for protecting private data on smartwatches are very limited. Personal identity number (PIN) input is the only privacy protection method on off-the-shelf smartwatches, which requires tedious user effort. This is ineffective at securing information such as notifications and attention-grabbing alerts, which may leak personal data to passersby and adversaries, causing embarrassment or revealing sensitive communications. In this work, we propose a novel privacy protection system, TouchTone, that verifies users and secure personal data in a convenient and low-effort manner. Our system employs a challenge-response process to passively capture finger biometrics from an unobtrusive touch gesture using only microphones, speakers, and accelerometer sensors already built in smartwatches. To address smartwatch incompatibility with traditional high-frequency sensing techniques, we develop non-intrusive low-frequency challenge signals and cross-domain sensing techniques (i.e., measuring acoustic signals in the vibration domain) to capture robust and effective features specific to user fingers. A low-cost profile matching-based classifier is designed to enable stand-alone privacy protection on smartwatches. We conduct extensive experiments with 54 participants using varied hardware, environments, noise levels, user motions, and other impact factors, achieving around 97% true positive rate and 2% false positive rate in recognizing participants' identities for privacy protection.

Sound source localization is vital for daily tasks such as communication or navigating environments. However, millions of adults struggle with hearing impairment, which limits their ability to identify the direction and distance of sound sources. Traditional methods for sound spatial sensing, such as microphone arrays, are not suitable for resource-constrained IoT devices like smartphones due to power consumption or hardware complexity. To overcome these limitations, this paper proposes EarCase, an alternative scheme that utilizes commercial smartphones with only two microphones to recognize 3D acoustic spatial information. EarCase draws inspiration from the human auditory system, where two ears amplify minute differences in acoustic signals to help pinpoint sound sources. This ability can be regarded as a response function trained through a large amount of sound source information, which can be used to extract spectral cues from a sound source position to the ears drums. We imitate this effect by designing a smartphone case with perforated mini-structures covering the microphones to help the smartphone infer the location of the sound source. Sound waves that pass through the mini-structure will undergo unique changes in diffraction at the hole, amplifying directional information similar to ears. Our scheme uses the top and bottom microphones to eliminate noises and multi-path effects, making the design robust to different sound sources in varying environments. By using only built-in microphones and low-cost phone cases, EarCase provides an accessible tool to enhance the quality of life for hearing impaired individuals. Extensive experimental results show that EarCase achieves high accuracy in localizing sounds, with a mean error of 3.7° at a distance of 200cm and 96% accuracy for real-world sounds (e.g., car horns).

Finger biometrics are widely used by smartphones as a secure and user-friendly credential for privacy protection. However, this information is difficult to measure without high-resolution images, leaving most works to treat this as an image-domain problem. We demonstrate that low-effort alternatives on smartphones are possible through the use of sound propagation in ubiquitous smartphone cases. Inexpensive and widely adopted, smartphone cases are always in contact with fingers, making them ideal for collecting finger biometrics. We thus design BioCase, an acoustic sensing system that leverages smartphone cases equipped with mini-structures to capture unique biometric-hybrid signatures (i.e., reflections influenced by the user's fingertip physiology and behavior) for smartphone privacy protection. The system generates inaudible structure-borne sound and measure the propagation through the smartphone case, mini-structures, and user finger. The design of the mini-structure controls the behavior of structure-borne sound such that unique responses are produced when different users and fingers touch the smartphone case. This enables low-cost, low-effort privacy protection, merely touching the smartphone case can authenticate users. Comprehensive experiments with 46 users over 10 weeks demonstrate BioCase can differentiate users with over 94% accuracy at a 5% false positive rate.

As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.

As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters$V$, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter$V(t)$and$\Omega$-additive approximation to enhance the robustness of the proposed algorithm within an error$\Omega$. Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the$[O(1/V), O(V)]$performance-backlog tradeoff and is more competitive than benchmarks.

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, called WS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

Mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading schemes. Many existing works focus on offloading tasks to the edge servers while ignoring the heterogeneity and diversity of computation services, which is also important in MEC. In this paper, we investigate the joint problem of online task offloading and service placement-downloading and deploying the service-related resources at edge servers-in the dense MEC network. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Due to the uncertainty of task demands, it is impossible to make an online long-term optimal decision. Therefore, we propose an online algorithm based on the two-timescale Lyapunov optimization without requiring the future information. By making asynchronous decisions on service placement and task offloading, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm is more competitive than benchmarks.

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.