Network Operating Systems (NOS) are being widely deployed on edge devices by cloud service providers to perform fast configurations and offer high availability for new network protocols. However, NOS-enabled networks open the door to intruders that can stealthily corrupt less-guarded programmable switches to launch attacks on the entire network. Traditional centralized intrusion detection systems may neglect anomalous events on NOS-equipped switches and fail to detect such attacks. In this paper, we make the first attempt towards intrusion detection for NOS-enabled networks by designingRetriever.Retrieverfeatures a lightweight local anomaly detection module on programmable switches and a central anomaly assessment module on the central server. The local anomaly detection module selectively traces both system and network events on switches, based on which a provenance graph of events is established. Upcoming events unmatched by the provenance graph are aggregated to construct a suspicious subgraph to report to the central server. The central anomaly assessment module extracts semantic representations from reported suspicious subgraphs and computes their anomaly scores. Large-scale experiments show thatRetrievercan achieve high intrusion detection accuracy (nearly 100%) with low overheads.

The rapid development of the Internet of Things (IoT) boosts the spread of intelligent spaces. Biometrics-based user identification has gained great popularity recently, among which gait analysis offers a stable, user-friendly, and economical solution. Thanks to the advancement in wireless sensing technologies, capturing gait characteristics using WiFi signals has become a promising new paradigm. The identification process is contactless, insensitive to lighting conditions, and can reuse the incumbent WiFi infrastructure. In this article, we present a gait-based dual-user identification framework named WiWalk to tackle the difficulty where users walk closely together with mixed effects on WiFi signals. The core of WiWalk is to train a deep neural network that can separate and recover individual signals from the mixed ones. Since the separation process inevitably causes information loss, we carefully design a series of algorithms for interference elimination, segmentation, and feature extraction, to enhance the identification accuracy. We conduct extensive experiments to evaluate WiWalk at different locations and times with users of different ages, genders, clothing, and walking behaviors. WiWalk can reach an accuracy of 94.44%, which is suitable for smart homes or offices with a small user base.

Facial expressions are an essential form of human nonverbal communication. Recognition of this nonverbal sign may enable developers to understand the feedbacks on smart device functionality and advertising. Existing approaches for facial expression recognition are mainly based on cameras or on-body sensors, which are either sensitive to lighting conditions or cumbersome for users to wear devices on their faces. In this paper, we propose a new facial expression recognition system based on Wi-Fi signals, named WiFace. Our fundamental intuition is that facial muscle movements in different expressions will induce distinctive waveform patterns in the time-series of channel state information (CSI) in Wi-Fi signals. We develop a series of algorithms to process the CSI signals and extract the most representative waveform patterns for facial expression classification. We build a fully-functional prototype of WiFace using commercial off-the-shelf devices, which can recognize six typical facial expressions. We conduct extensive experiments to evaluate the performance of WiFace, and the experimental results show that the average recognition accuracy is 94.80 percent.

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we presentDetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver’s psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants,DetectDUIachieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of$2\sim 5mg/dl$. These promising results provide a highly encouraging case for continued development.

With recent advances in the study of biometrics, gait analysis has drawn much attention for its potential use in forensics, surveillance, and legal systems. In this paper, we present WIAGE, a contactless and non-intrusive gait-based age estimation system, which leverages wireless sensing to perform gait analysis to infer the age of individuals. Traditional age estimation systems either require users to carry wearable devices that are inconvenient or rely on image processing that is computationally intensive and sensitive to lighting conditions and occlusion. In contrast, WIAGE utilizes the incumbent WiFi infrastructure to infer the age of users with minimal interference to their activities. We adopt a series of signal processing techniques to recover clear gait patterns from the noisy WiFi signals and extract the most relevant features from steps that can be used for robust age estimation. The experimental results show that WIAGE can achieve an age estimation accuracy of 95.2% for 23 users, which demonstrates the feasibility and effectiveness of our proposed system.

Traditional health insurance schemes make contracts with periodic premiums according to the health conditions of customers. Insurance companies are thus seeking an incentive for a healthy lifestyle of customers. Nowadays, more and more people want to gather metrics associated with physical activity. High quality and quantity information introduced by wearable devices may indicate the potential risks of acute conditions. Therefore, wearable technology brings new opportunities for insurance companies to change the interaction way with customers and to improve risk management for users. In this article, we investigate the problem of incentive mechanism design for the insurance market boosted by wearable technology. Designing an effective incentive mechanism is challenging since both the insurer and the users are selfish and rational parties who try to maximize their own utility. There exists an information asymmetry, i.e., the insurer is unaware of the intrinsic physical fitness of each user (type of users). Inspired by the contract theory, we design a series of optimal insurance contracts with different premium discounts and recommended exercise levels targeting different types of users. We first analyze the scenario where the insurer knows the type of each individual, then move to the more practical case where the insurer has incomplete information of user types. We theoretically prove that the proposed contracts satisfy individual rationality and incentive compatibility, which enables the insurer to achieve utility maximization and each type of user to choose the most appropriate contract. We have conducted extensive simulations to provide insight into the performance under different scenarios. The numerical results verify that our contract-based insurance incentive mechanism maximizes insurer's utility and motivates users to exercise more.

With the explosion of the Internet-of-Things (IoT) technology, numerous IoT terminal devices generate tremendous traffic. Device-to-device (D2D)-enabled caching can greatly relieve the pressure of massive resource-limited terminal devices in the IoT network. This article proposes a novel distributed framework, termed crowdcaching, which motivates selective file caching and cooperative file sharing among terminal devices via short-range (e.g., D2D) communications. After modeling file preference distributions and local connectivities, we optimize the caching strategy for any given coalition of cooperative users to minimize their total delay cost. In particular, if users have homogeneous file preferences and local connectivities, we can mathematically define a popularity index, according to which files are chosen to be cached in user devices. In a more general setting where users have heterogeneous file preferences and local connectivities, we propose a greedy algorithm of low complexity to determine the optimal caching strategy. Based on the cooperative caching strategy for any given coalition, we further investigate users' incentive to form crowdcaching coalitions through the coalitional game theory and propose a distributed algorithm to yield a stable coalition formulation. The simulation results show that crowdcaching can effectively reduce the average delay cost of users by as much as 45.64%.

Nowadays, smartphones have become indispensable for people and many users may browse information on their smartphones during walking. This raises severe concerns about pedestrian safety when distracted users inadvertently walks from the sidewalk onto the street. In this paper, we present WatchOut, a convenient pedestrian alerting system based on fine-grained step classification (e.g., flat, up and down the ramp) and event detection (e.g., entering the street, turn) in an urban environment. In contrast to existing systems that rely on shoe-mounted sensors, Watchout leverages rich sensors on the smartphone without requiring any additional hardwares. With carefully designed realtime data processing and classification algorithms, WatchOut can achieve a high detection accuracy. We develop a fully-functional Android application of WatchOut with user-friendly interface that will issue timely alerts to warn users of unsafe conditions. Extensive experiments with 23 volunteers in real pedestrian environment have confirmed the effectiveness of WatchOut, which achieves a comparable detection accuracy as the shoe-mounted system with off-the-shelf smartphones.