Detecting how a vehicle is steered and then alarming drivers in real time is of utmost importance to the vehicle and the driver's safety, since fatal accidents are often caused by dangerous steering. Existing solutions for detecting dangerous maneuvers are implemented either in only high-end vehicles or on smartphones as mobile applications. However, most of them rely on the use of cameras, the performance of which is seriously constrained by their high visibility requirement. Moreover, such an over/sole-reliance on the use of cameras can be a distraction to the driver.To alleviate these problems, we develop a vehicle steering detection middleware called V-Sense which can run on commodity smartphones without additional sensors or infrastructure support. Instead of using cameras, the core of V-Sense senses a vehicle's steering by only utilizing nonvision sensors on the smartphone. We design and evaluate algorithms for detecting and differentiating various vehicle maneuvers, including lane-changes, turns, and driving on curvy roads. Since V-Sense does not rely on use of cameras, its detection of vehicle steering is not affected by the (in)visibility of road objects or other vehicles. We first detail the design, implementation and evaluation of V-Sense and then demonstrate its practicality with two prevalent use cases: camera-free steering detection and fine-grained lane guidance. Our extensive evaluation results show that VSense is accurate in determining and differentiating various steering maneuvers, and is thus useful for a wide range of safety-assistance applications without additional sensors or infrastructure.

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Multiple-In-Multiple-Out (MIMO) offers great potential for increasing network capacity by exploiting spatial diversity with multiple antennas. Multiuser MIMO (MU-MIMO) further enables Access Points (APs) with multiple antennas to transmit multiple data streams concurrently to several clients. In MU-MIMO, clients need to estimate Channel State Information (CSI) and report it to APs in order to eliminate interference between them. We explore the vulnerability in clients' plaintext feedback of estimated CSI to the APs and propose two advanced attacks that malicious clients can mount by reporting forged CSI: (1) sniffing attack that enables concurrently transmitting malicious clients to eavesdrop other ongoing transmissions;(2) power attack that enables malicious clients to enhance their own capacity at the expense of others'. We have implemented and evaluated these two attacks in a WARP testbed. Based on our experimental results, we suggest a revision of the current CSI feedback scheme and propose a novel CSI feedback system, called the CSIsec, to prevent CSI forging without requiring any modification at the client side, thus facilitating its deployment.

Purpose of the study: Work addiction risk is a growing public health concern with potential deleterious health-related outcomes. Perception of work (job demands and job control) may play a major role in provoking the risk of work addiction in employees. We aimed to explore the link between work addiction risk and health-related outcomes using the framework of job-demand-control model. Methods: Data were collected from 187 out of 1580 (11.8%) French workers who agreed to participate in a cross-sectional study using the WittyFit software online platform. The self-administered questionnaires were the Job Content Questionnaire by Karasek, the Work Addiction Risk Test, the Hospital Anxiety and Depression scale and socio-demographics. Data Analysis: Statistical analyses were performed using the Stata software (version 13). Results: There were five times more workers with a high risk of work addiction among those with strong job demands than in those with low job demands (29.8% vs. 6.8%, p = 0.002). Addiction to work was not linked to job control (p = 0.77), nor with social support (p = 0.22). We demonstrated a high risk of work addiction in 2.6% of low-strain workers, in 15.0% of passive workers, in 28.9% of active workers, and in 33.3% of high-strain workers (p = 0.010). There were twice as many workers with a HAD-Depression score ≥11 compared with workers at low risk (41.5% vs. 17.7%, p = 0.009). Sleep quality was lower in workers with a high risk of work addiction compared with workers with a low risk of work addiction (44.0 ± 27.3 vs. 64.4 ± 26.8, p < 0.001). Workers with a high risk of work addiction exhibited greater stress at work (68.4 ± 23.2 vs. 47.5 ± 25.1) and lower well-being (69.7 ± 18.3 vs. 49.3 ± 23.0) compared with workers at low risk (p < 0.001). Conclusions: High job demands are strongly associated with the risk of work addiction. Work addiction risk is associated with greater depression and poor quality of sleep. Preventive strategies should benefit from identifying more vulnerable workers to work addiction risk.

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