Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

Development and Verification of Simulation Model Based on Real MANET Experiments for Transport Layer Protocols （UDP and TCP）

There is a lack of appropriate guidelines for realistic user traces, mobility models, routing protocols, considerations of real-life challenges, etc. for general-purpose mobile ad hoc networks (MANET). In this paper, four laptops are used in an open field environment in four scenarios to evaluate the performances of Internet control message protocol (ICMP) based ping and transmission control protocol (TCP) based streaming video applications using optimised link state routing (OLSR) implementation in an IEEE 802.11g wireless network. Corresponding simulations are developed in Network Simulator ns-2 by setting simulation parameters according to the real experiments. Difficulties faced to regenerate real-life scenarios have been discussed and the gaps between reality and simulation are identified. A setup guideline to produce realistic simulation results has been established.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Application of artificial intelligence to improve quality of service in computer networks

Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.     

The reliability of asset management regime of the SROH using air void content of asphalt mixtures

The comparison pairs of cores (each 100 mm apart) from 68 reinstatements sites from various parts of the UK revealed that, the compounding consequences of generic non-homogeneous characteristics of hand laid recipe mixed materials (specified in Specification for the Reinstatement of Openings in Highways (SROH)) and high likeliness of being biased during air void (AV) testing makes the coring method extremely unreliable with very low repeatability and reproducibility. The wide-ranging maximum density reported in every instance in the comparison pair coring experiments meaningfully rationalizes the distorted homogeneity of materials. Although not only maximum density but also bulk density of adjacent cores located only 100 mm apart were found to be varied in the case of every pair in this study. Furthermore, the in situ performance shown by from 50 reinstatements after experiencing 1.5–10 years real-life ageing from various parts of the UK predictably indicates that either the linkage between the reinstatement with non-compliant AV and its impact on footways durability is non-proven or the reported AV content is extremely over estimated. At 95% level of significance, there exists enough evidence to conclude that, due to high uncertainty, very low repeatability and reproducibility and poor reliability with high chances of bias, the assessment of hand laid reinstatement work by AV testing will expose both the contractor and the client to unacceptable risk.     

SCM: Secure and accountable TLS certificate management

In classical public‐key infrastructure (PKI), the certificate authorities (CAs) are fully trusted, and the security of the PKI relies on the trustworthiness of the CAs. However, recent failures and compromises of CAs showed that if a CA is corrupted, fake certificates may be issued, and the security of clients will be at risk. As emerging solutions, blockchain‐ and log‐based PKI proposals potentially solved the shortcomings of the PKI, in particular, eliminating the weakest link security and providing a rapid remedy to CAs' problems. Nevertheless, log‐based PKIs are still exposed to split‐world attacks if the attacker is capable of presenting two distinct signed versions of the log to the targeted victim(s), while the blockchain‐based PKIs have scaling and high‐cost issues to be overcome. To address these problems, this paper presents a secure and accountable transport layer security (TLS) certificate management (SCM), which is a next‐generation PKI framework. It combines the two emerging architectures, introducing novel mechanisms, and makes CAs and log servers accountable to domain owners. In SCM, CA‐signed domain certificates are stored in log servers, while the management of CAs and log servers is handed over to a group of domain owners, which is conducted on the blockchain platform. Different from existing blockchain‐based PKI proposals, SCM decreases the storage cost of blockchain from several hundreds of GB to only hundreds of megabytes. Finally, we analyze the security and performance of SCM and compare SCM with previous blockchain‐ and log‐based PKI schemes.     

Effect of long-term service exposure at elevated temperature on microstructural changes of 5Cr-0.5Mo steels

Effects of long-term service exposure at elevated temperature on microstructural changes have been studied for both virgin and service-exposed process heater tube pipes of 5Cr-0.5Mo steels used in oil refineries. Samples selected for this study had experienced a nominal temperature range of 450 °C to 500 °C for about 20 to 25 years. Two different initial virgin microstructures were taken and designated by steel A and steel B. The virgin microstructure of steel A exhibited fine platelets of fibrous or hairlike M₂C carbides within the ferrite grains and occasionally irregularly shaped M₂₃C₆, both along the grain boundaries and at the grain interiors, and very few spheroidally shaped M₃C, either along the grain boundaries or at the grain interiors. The size, shape, position, distribution, and type of carbides in virgin steel A changed significantly due to 220,000 hours of service exposure in the temperature range of 450 °C to 500 °C. Massive M₂₃C₆ carbides precipitated along the grain boundaries. In addition, regular geometrically shaped M₂₃C₆ carbides, such as hexagonal, square, and triangular type, were observed to form at the grain interiors. The virgin steel B microstructure exhibited predominantly M₂₃C₆ carbides, either along the grain boundaries or at the lath boundaries. Occasionally, fine platelets of M₂C carbides were also observed within the laths. The position, shape, distribution, and type of carbides did not change significantly due to 172,000 hours of service exposure in the temperature range of 450 °C to 500 °C. The average interparticle spacings of the carbides increased from 0.35 to 1.2 μm due to 172,000 hours of exposure.     

Modelling majority and expert influences on opinion formation in online social networks

Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments.     

The Visual Looming Navigation Cue: A Unified Approach

Visual looming is related to an increasing projected size of an object on a viewer's retina as the relative distance between the viewer and the object decreases. It is an indication for threat that may be used along with visual fixation to accomplish navigation tasks. This paper defines visual looming as the time derivative of the relative distance (range) between the observer and the object divided by the relative distance itself. It introduces a unified approach to visual looming by showing how visual looming can be calculated from the relative temporal change in the following attributes of a 2D image sequence: (i) image area, (ii) image brightness, (iii) texture density in the image, and (iv) image blur. It is shown that a closed-form unified expression can be adopted in all these methods. Experimental results illustrate how the measured values of looming are related to the actual values. Finally, looming is used in the sense of a threat of collision, along with visual fixation, to navigate in an unknown environment.