Mixed-Criticality Scheduling on Multiprocessors using Task Grouping

Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor real-time performance for the low-criticality tasks

An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.Comment: 10 figures, 19 page

Mitochondrial nutrients improve immune dysfunction in the type 2 diabetic Goto-Kakizaki rats.

The development of type 2 diabetes is accompanied by decreased immune function and the mechanisms are unclear. We hypothesize that oxidative damage and mitochondrial dysfunction may play an important role in the immune dysfunction in diabetes. In the present study, we investigated this hypothesis in diabetic Goto-Kakizaki rats by treatment with a combination of four mitochondrial-targeting nutrients, namely, R-alpha-lipoic acid, acetyl-L-carnitine, nicotinamide and biotin. We first studied the effects of the combination of these four nutrients on immune function by examining cell proliferation in immune organs (spleen and thymus) and immunomodulating factors in the plasma. We then examined, in the plasma and thymus, oxidative damage biomarkers, including lipid peroxidation, protein oxidation, reactive oxygen species, calcium and antioxidant defence systems, mitochondrial potential and apoptosis-inducing factors (caspase 3, p53 and p21). We found that immune dysfunction in these animals is associated with increased oxidative damage and mitochondrial dysfunction and that the nutrient treatment effectively elevated immune function, decreased oxidative damage, enhanced mitochondrial function and inhibited the elevation of apoptosis factors. These effects are comparable to, or greater than, those of the anti-diabetic drug pioglitazone. These data suggest that a rational combination of mitochondrial-targeting nutrients may be effective in improving immune function in type 2 diabetes through enhancement of mitochondrial function, decreased oxidative damage, and delayed cell death in the immune organs and blood

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by both simulations with real datasets and system implementation in a real testbed. Evaluation results show that the performance of the proposed algorithm with distributed inference framework is promising, by reaching the accuracies of attribute recognition and person identification up to 92.9% and 96.6% respectively, and significantly reducing the inference delay by at least 40.6% compared with existing methods

Comparative studies on the multi-component pharmacokinetics of Aristolochiae Fructus and honey-fried Aristolochiae Fructus extracts after oral administration in rats

PK parameters of 7-OH AA I in rats after oral administration of AF and HAF. (DOC 37 kb

A Combination of Nutriments Improves Mitochondrial Biogenesis and Function in Skeletal Muscle of Type 2 Diabetic Goto–Kakizaki Rats

BACKGROUND: Recent evidence indicates that insulin resistance in skeletal muscle may be related to reduce mitochondrial number and oxidation capacity. However, it is not known whether increasing mitochondrial number and function improves insulin resistance. In the present study, we investigated the effects of a combination of nutrients on insulin resistance and mitochondrial biogenesis/function in skeletal muscle of type 2 diabetic Goto-Kakizaki rats. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated that defect of glucose and lipid metabolism is associated with low mitochondrial content and reduced mitochondrial enzyme activity in skeletal muscle of the diabetic Goto-Kakizaki rats. The treatment of combination of R-alpha-lipoic acid, acetyl-L-carnitine, nicotinamide, and biotin effectively improved glucose tolerance, decreased the basal insulin secretion and the level of circulating free fatty acid (FFA), and prevented the reduction of mitochondrial biogenesis in skeletal muscle. The nutrients treatment also significantly increased mRNA levels of genes involved in lipid metabolism, including peroxisome proliferator-activated receptor-alpha (Ppar alpha), peroxisome proliferator-activated receptor-delta (Ppar delta), and carnitine palmitoyl transferase-1 (Mcpt-1) and activity of mitochondrial complex I and II in skeletal muscle. All of these effects of mitochondrial nutrients are comparable to that of the antidiabetic drug, pioglitazone. In addition, the treatment with nutrients, unlike pioglitazone, did not cause body weight gain. CONCLUSIONS/SIGNIFICANCE: These data suggest that a combination of mitochondrial targeting nutrients may improve skeletal mitochondrial dysfunction and exert hypoglycemic effects, without causing weight gain

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics. To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ). Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE 0.94) compared to FFANN (NRMSE 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation

Intrusion Detection into Cloud-Fog-Based IoT Networks Using Game Theory

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network