A high-performance and scalable multi-core aware software solution for network monitoring

In recent years, the need for high-performance network monitoring tools, which can cope with rapidly increasing network bandwidth, has become vital. A possible solution is to utilize the processing power of multi-core processors that nowadays are available as commercial-off-the-shelf (COTS) hardware. In this paper, we introduce a software solution for wire-speed packet capturing and transmission for TCP/IP networks under Linux operating system, called DashCap. The results of our experimental evaluations show that the proposed solution causes more than two times performance boost for packet capturing in comparison to the existing software solutions under Linux. We have proposed a scalable software architecture for network monitoring tools called DashNMon, which is based on DashCap. Multi-core awareness is a distinguished property of this architecture. Comparing to the existing cluster-based solutions, DashNMon can be used with COTS multi-core processors. In order to evaluate the proposed solutions, we have developed several prototype tools. The results of the experiments carried out using these tools show the scalability and high performance of the network monitoring tools that are based on the proposed architecture. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded network intrusion detection systems (NIDSs) or application-layer firewalls, completely in the user space and with better utilization of the computational resources of multi-processor/multi-core systems.     

Dynamics of a rumor‐spreading model with diversity of configurations in scale‐free networks

In this paper, we propose a new rumor‐spreading model, which is a variant of the susceptible–exposed–infectious–removed epidemic model. We consider the influences of the diversity of configurations as a defense strategy to diminish the damage brought by rumor diffusion. The diversity of configuration can be found in different instant messengers or social networks. Moreover, we apply two kinds of the rumor immunization strategies such as random immunization and targeted immunization and show that targeted immunization is better than random immunization for rumor‐spreading control in scale‐free networks. We carry out the systematic analysis of the model and study the local and global stability of rumor‐free equilibrium. In the proposed model, we introduce two new states, namely, hibernator and stifler2, to study dynamical behaviors of the model. The proposed model combines both targeted immunization and diversity of configuration to prevent rumor spreading. By numerical simulations, we indicate that our model is appreciably better than other models in terms of declining the rumor diffusion speed. Furthermore, we investigate many related dynamic properties to make the result more complete. Simulation results show the effect of different parameters on the rumor diffusion in scale‐free networks; meanwhile, numerical simulations confirm the theoretical solution. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel trust evolution algorithm based on a quantum-like model of computational trust

Cognition, Technology & Work - Trust models play an important role in decision support systems and computational environments in general. The common goal of the existing trust models is to...     

Influence of Air Cooling and Aging on Microstructure and Wear Properties of 3D-Printed and Conventionally Produced Medical-Grade Ti6Al4V ELI Alloy

The emergence of additive manufacturing (AM) in recent years has become one of the most demanded manufacturing technologies in the biomedical and aerospace industries due to its ease of fabrication of components with complex geometry. Ti6Al4V parts manufactured by AM process however require a post-processing to optimize their mechanical properties for engineering applications. The cooling rates after the heat treatment play a significant role in tailoring the final microstructure and properties of medical-grade Ti6Al4V ELI alloys. This study therefore aims to investigate the changes in microstructure and consequently mechanical and wear properties of both AM-fabricated (3D-printed) and conventionally produced Ti6Al4V ELI alloy by the effect of post-heat treatment, air-cooling and aging (200 °C, 500 °C and 800 °C) processes. Typically, the formation of lath colonies and precipitated needle-like lath structures after solutionization (@ 1080 °C) and air cooling above the β-transus yielded a retained cubic β-phase regardless of the manufacturing process. Different spatial distributions of the alpha (α) lath and basket-weave structures as well as coarsened AlTi₃ intermetallic (V-shaped) structures evolve, which later reduced in area fraction in 3D printing. Also, increasing the aging temperatures after slow (air) cooling gradually enhances α-β-phase transformation rates regardless of the manufacturing process because of diffusional redistribution of alloying elements. In addition, the evolution of V-shaped structures improves the hardness (up to 29 pct) and wear performance of 3D-printed materials (up to 126 pct) relative to the conventionally produced materials, regardless of the β content.

     

A formulation of computational trust based on quantum decision theory

In this paper, we propose a new formulation of computational trust based on quantum decision theory (QDT). By using this new formulation, we can divide the assigned trustworthiness values to objective and subjective parts. First, we create a mapping between the QDT definitions and the trustworthiness constructions. Then, we demonstrate that it is possible for the quantum interference terms to appear in the trust decision making process. By using the interference terms, we can quantify the emotions and subjective preferences of the trustor in various contexts with different amounts of uncertainty and risk. The non-commutative nature of quantum probabilities is a valuable mathematical tool to model the relative nature of trust. In relative trust models, the evaluation of a trustee candidate is not only dependent on the trustee itself, but on the other existing competitors. In other words, the first evaluation is performed in an isolated context whereas the rest of the evaluations are performed in a comparative one. It is shown that a QDT-based model of trust can account for these order effects in the trust decision making process. Finally, based on the principles of risk and uncertainty aversion, interference alternation theorem and interference quarter law, quantitative values are assigned to interference terms. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by a quantum model of trust rather than the commonly used classical models.     

Effect of Heat Treatment on the Microstructural Evolution and Properties of 3D-Printed and Conventionally Produced Medical-Grade Ti6Al4V ELI Alloy

Medical-grade Ti6Al4V extra-low interstitial (ELI) alloy has widespread applications in the biomedical industry due to its excellent corrosion and wear resistance. Even though 3D printing offers geometry flexibility and rapid means of fabricating customized parts, 3D-printed parts are often plagued with defects including porosity, high residual stresses, and non-equilibrium structures. Thus, post-processing heat treatments may be required to optimize its properties for engineering applications. In this study, the effect of post-processing heat treatment on the microstructure, hardness, and wear properties of 3D-printed and conventionally produced medical-grade Ti6Al4V ELI alloy samples was investigated. In general, distinct α (alpha) lath and basket-weave lath structures with a high degree of orientation were observed within the microstructure of the as-printed samples. Heat treatment led to the growth of distinct continuous and discontinuous α-lath structures along prior β (beta) grain boundaries as well as basket-weave lath and the coalescence of V-shaped structures within the prior β-grains. The hardness of both the 3D-printed and conventionally produced samples increased after heat treatment (≥ 400 HV), regardless of the cooling rate and aging temperature. After being water quenched/aged, the 3D-printed samples at 500 °C had the highest hardness values owing to the presence of coarse V-shaped structures. Furthermore, the V-shaped structures were always harder than all other structures regardless of the heat treatment and manufacturing process used, indicating that these structures dictate the overall mechanical integrity of the material. X-ray diffraction and electron probe microanalysis indicated that the V-shaped structures are rich in aluminum and titanium content, which can form hcp α′ (AlTi₃) intermetallic phases. The 3D-printed samples had higher wear resistance overall than the conventionally produced samples regardless of the heat treatment used. Aging at 500 °C led to a higher coefficient of friction after 3D printing owing to an increase in α-phases. Therefore, during heat treatment, the microstructure and properties of medical-grade Ti6Al4V ELI alloy are significantly affected by the starting microstructure, the rate of cooling below the β-transus, and aging temperature and time, regardless of the manufacturing process used.

     

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.     

Task offloading in an optimized power-performance manner

The cloud computing systems, such as the Internet of Things (IoT), are usually introduced with a three-layer architecture (IoT-Fog-Cloud) for the task offloading that is a solution to compensate for resource constraints in these systems. Offloading at the right location is the most significant challenge in this field. It is more appropriate to offload tasks to fog than to cloud based on power and performance metrics, but its resources are more limited than the resources of the cloud. This paper tries to optimize these factors in the fog by specifying the number of usable servers in the fog. For this purpose, we model a fog computing system using the queueing theory. Furthermore, binary search and reinforcement learning algorithms are proposed to determine the minimum number of servers with the lowest power consumption. We evaluate the cost of the fog in different scenarios. By solving the model, we find that the proposed dispatching policy is very flexible and outperformed the known policies by up to 31% and in no case is it worse than either of them, and the overall offloading cost increases when fog rejects tasks with a high probability. Our offloading method is more effective than running all fog servers simultaneously, based on simulation results. It is evident from the similarities between the simulation results and those derived from the analytical method that the model and results are valid.