SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone

The number of pedestrian accidents continues to keep climbing. Distraction from smartphone is one of the biggest causes for pedestrian fatalities. In this paper, we develop SaferCross, a mobile system based on the embedded sensors of smartphone to improve pedestrian safety by preventing distraction from smartphone. SaferCross adopts a holistic approach by identifying and developing essential system components that are missing in existing systems and integrating the system components into a "fully-functioning" mobile system for pedestrian safety. Specifically, we create algorithms for improving the accuracy and energy efficiency of pedestrian positioning, effectiveness of phone activity detection, and real-time risk assessment. We demonstrate that SaferCross, through systematic integration of the developed algorithms, performs situation awareness effectively and provides a timely warning to the pedestrian based on the information obtained from smartphone sensors and Direct Wi-Fi-based peer-to-peer communication with approaching cars. Extensive experiments are conducted in a department parking lot for both component-level and integrated testing. The results demonstrate that the energy efficiency and positioning accuracy of SaferCross are improved by 52% and 72% on average compared with existing solutions with missing support for positioning accuracy and energy efficiency, and the phone-viewing event detection accuracy is over 90%. The integrated test results show that SaferCross alerts the pedestrian timely with an average error of 1.6sec in comparison with the ground truth data, which can be easily compensated by configuring the system to fire an alert message a couple of seconds early.Comment: Published in IEEE Access, 202

Association of Dietary Total Antioxidant Capacity with Bone Mass and Osteoporosis Risk in Korean Women: Analysis of the Korea National Health and Nutrition Examination Survey 2008-2011

Antioxidant intake has been suggested to be associated with a reduced osteoporosis risk, but the effect of dietary total antioxidant capacity (TAC) on bone health and the risk of osteoporosis remains unclear. We aimed to assess the hypothesis that dietary TAC is positively associated with bone mass and negatively related to the risk of osteoporosis in Korean women. This cross-sectional study was performed using data from the Korea National Health and Nutrition Examination Survey. Dietary TAC was estimated using task automation and an algorithm with 24-h recall data. In total, 8230 pre-and postmenopausal women were divided into four groups according to quartiles of dietary TAC. Dietary TAC was negatively associated with the risk of osteoporosis (odds ratio, 0.73; 95% confidence interval, 0.54–0.99; p-value = 0.045) in postmenopausal women, but not in premenopausal women. Dietary TAC was positively associated with bone mineral content (BMC) and bone mineral density of the femoral neck and lumbar spine in postmenopausal women and BMC of the total femur and lumbar spine in premenopausal women. Our study suggests that dietary TAC is inversely associated with the risk of osteoporosis in postmenopausal women and positively associated with bone mass in both pre-and postmenopausal women

Dynamic Vehicular Route Guidance Using Traffic Prediction Information

We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply

A Novel Mitigation Method for Noise-Induced Temperature Error in CPU Thermal Control

It has been reported that in the thermal control of real-time computing systems, zero-mean thermal sensor noise can induce a significant steady-state error between the target and actual temperatures of a CPU. Unlike the usual case of zero-mean sensor noise resulting in zero-mean temperature fluctuations around the target value, this noise-induced temperature error manifests in the form of a bias, i.e., the mean of the error is not zero. Existing work has analyzed the main cause of this error and produced a solution, known as TCUB-VS. However, this existing solution has a few drawbacks: the transient response is sluggish, and the exact value of the noise standard deviation is necessary in the design stage. In this paper, we propose a novel method of avoiding noise-induced temperature error while overcoming the limitations of the existing work. The proposed method uses an estimated CPU temperature for the part of the controller that is sensitive to noise while using actual measurements for the other part of the controller. In this way, our proposed method eliminates noise-induced temperature error and overcomes the drawbacks of the existing work. To show the efficacy of our proposed method, theoretical results are obtained using a stochastic averaging approach, and experimental results are presented along with simulations.1

Continuous productivity improvement using ioe data for fault monitoring: An automotive parts production line case study

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Supplementary Material of "Stealthy Adversaries against Uncertain Cyber-Physical Systems: Threat of Robust Zero-Dynamics Attack"

This article is a supplementary material for "Stealthy Adversaries against Uncertain Cyber-Physical Systems: Threat of Robust Zero-Dynamics Attack"

Interest Broadcast Suppression Scheme for Named Data Wireless Sensor Networks

Named data networking (NDN) is one of the future networking architectures that communicates content using names, instead of the node addresses. It uses a very simple pull-based communication mechanism to retrieve content by sending an Interest message, and the node that has the required content or producer node replies with the data message. In wireless networks, the interest is flooded in the network to find the data provider node. The directional diffusion method is used to pull further content from the provider node. Due to broadcast nature and without node addresses, interest flooding causes network congestion and wastes network resources, especially bandwidth and battery power. These resources have prime importance in the case of wireless sensor networks (WSNs) because all WSN nodes operate on battery and have limited bandwidth. In this paper, we propose an interest broadcast suppression scheme that considers interest holding time using the distance between forwarder and receiver of the interest, energy, angle, and distance from the beeline between consumer and the spatial region, to avoid broadcasting of unnecessary copies of Interest. The simulation results show that the proposed scheme mitigates the interest broadcast and conserves battery power of the wireless nodes compared with the state-of-the-art scheme in the domain. © 2019 IEEE.1