Distributed Indexing Schemes for k-Dominant Skyline Analytics on Uncertain Edge-IoT Data

Skyline queries typically search a Pareto-optimal set from a given data set to solve the corresponding multiobjective optimization problem. As the number of criteria increases, the skyline presumes excessive data items, which yield a meaningless result. To address this curse of dimensionality, we proposed a k-dominant skyline in which the number of skyline members was reduced by relaxing the restriction on the number of dimensions, considering the uncertainty of data. Specifically, each data item was associated with a probability of appearance, which represented the probability of becoming a member of the k-dominant skyline. As data items appear continuously in data streams, the corresponding k-dominant skyline may vary with time. Therefore, an effective and rapid mechanism of updating the k-dominant skyline becomes crucial. Herein, we proposed two time-efficient schemes, Middle Indexing (MI) and All Indexing (AI), for k-dominant skyline in distributed edge-computing environments, where irrelevant data items can be effectively excluded from the compute to reduce the processing duration. Furthermore, the proposed schemes were validated with extensive experimental simulations. The experimental results demonstrated that the proposed MI and AI schemes reduced the computation time by approximately 13% and 56%, respectively, compared with the existing method.Comment: 13 pages, 8 figures, 12 tables, to appear in IEEE Transactions on Emerging Topics in Computin

Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan

This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS). A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC) of a receiver operator characteristic (ROC) curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking

ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based on Li-Ion Battery and Solar Energy Supply

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system

ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based on Li-Ion Battery and Solar Energy Supply

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system

ZigBee Wireless Sensor Nodes with Hybrid Energy Storage System Based on Li-Ion Battery and Solar Energy Supply

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system

The effects of postintubation hypertension in severe traumatic brain injury

Introduction. The effect of post-intubation hypertension in severe traumatic brain injury (TBI) patients remains uncertain. We aimed to determine the relationship between post-intubation hypertension (mean arterial pressure (MAP) > 110mmHg) and outcomes in severe TBI. Methods. In this retrospective cohort study, adults who presented with isolated TBI and a MAP 70mmHg were assessed. Data were retrieved from our institutional trauma registry and the admission list of our neurosurgical intensive care unit (ICU). Results. We enrolled 126 patients, 81 of whom had a MAP 110 mmHg after intubation and were assigned to group 1; 45 patients who had a MAP > 110 mmHg were assigned to group 2. Only age (P = 0.008), heart rate (HR; P = 0.036), and MAP before intubation (P 110 mmHg, P < 0.034, OR 3.119, 95% CI 1.087–8.953). Conclusion. Post-intubation hypertension was associated with longer ventilator-dependent and ICU stays in patients with severe TBI

EFFECT OF DIFFERENT TIBIA ANGLES TO LOADING OF KNEE DURING SPLIT SQUAT

The aim of this study was to investigate the difference of knee joint force and moment during split squats of different front tibia angles. Twelve healthy male college students performed six repetitions of four different split squat types with a standard additional load of 25% BW added using a barbell. Using 10 camera 3D motion capture system and a force plate to collect data. The peak force and moment of knee flexion (sagittal plane) were calculated by using self-designed MATLAB programs. One-way ANOVA test was undertaken using SPSS 20.0 statistical software. The analysis results of the study indicated that all kinetic parameters of the four types split squats were achieved high significant differences (p less than .000). A better understanding of different loading in specific joints and correct exercise execution during training will help protecting practitioners from sport injury