Analytical Modelling and Performability Evaluation of Multi-Channel WLANs with Global Failures

Wireless local area networks (WLANs) which are based on IEEE 802.11 standard are used widely in existing local area network configurations. IEEE 802.11 offers multiple non-overlapping channels to increase the capacity of the network. There are strong evidences that WLANs are prone to impairments. In order to improve the quality of service (QoS) and to evaluate the performance of WLANs realistically, the availability of the systems should be considered. This paper studies performability evaluation of a multi-channel WLAN using analytical modelling approach. Unlike the existing studies, the failures of the overall system, where a critical function unit fails making all the channels unavailable are considered. A new term is introduced as global failures. It is possible to solve the models considered using matrix geometric method where system parameters and minimal non negative solution R is computed by an iterative method. However spectral expansion method is a well-known alternative where the iterative calculations for solving R is avoided using eigenvalues and eigenvectors. The exact spectral expansion method is employed to obtain performability measures such as mean queue length and blocking probability. Iterative refinements are employed in solution of simultaneous equations

Seamless key agreement framework for mobile-sink in IoT based cloud-centric secured public safety sensor networks

Recently, the Internet of Things (IoT) has emerged as a significant advancement for Internet and mobile networks with various public safety network applications. An important use of IoT-based solutions is its application in post-disaster management, where the traditional telecommunication systems may be either completely or partially damaged. Since enabling technologies have restricted authentication privileges for mobile users, in this paper, a strategy of mobile-sink is introduced for the extension of user authentication over cloud-based environments. A seamless secure authentication and key agreement (S-SAKA) approach using bilinear pairing and elliptic-curve cryptosystems is presented. It is shown that the proposed S-SAKA approach satisfies the security properties, and as well as being resilient to nodecapture attacks, it also resists significant numbers of other well-known potential attacks related with data confidentiality, mutual authentication, session-key agreement, user anonymity, password guessing, and key impersonation. Moreover, the proposed approach can provide a seamless connectivity through authentication over wireless sensor networks to alleviate the computation and communication cost constraints in the system. In addition, using Burrows–Abadi–Needham logic, it is demonstrated that the proposed S-SAKA framework offers proper mutual authentication and session key agreement between the mobile-sink and the base statio

Approaches to Modelling and Analysis of Performability Evaluation in Wireless Environments

Most wireless communication and mobile computing systems are expected to be operational 24 hours a day, 7 days a week. However, wireless communication systems encounter failures. Performability modelling and evaluation of wireless and mobile systems have been of interest for recent research work. Especially, because of the complexity of the next generation wireless and mobile systems, modelling and performance evaluation is essential to improve the architecture according to the quality of service (QoS) requirements and performance characteristics. This paper presents a performability evaluation framework for wireless cellular networks in wireless environments using analytical modelling approaches. The availability and mobility issues are also considered in the proposed models. Well-known approximate Markov reward model solution and the exact Spectral expansion solution approaches are considered. Performability measures of proposed models, such as blocking probability and mean queue length are presented

Planning pesticides usage for herbal and animal pests based on intelligent classification system with image processing and neural networks

Pests are divided into two as herbal and animal pests in agriculture, and detection and use of minimum pesticides are quite challenging task. Last three decades, researchers have been improving their studies on these manners. Therefore, effective, efficient, and as well as intelligent systems are designed and modelled. In this paper, an intelligent classification system is designed for detecting pests as herbal or animal to use of proper pesticides accordingly. The designed system suggests two main stages. Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. Autonomy and time efficiency within the pesticide usage are also discussed

Analytical Modelling and Performability Analysis for Cloud Computing Using Queuing System.

In recent years, cloud computing becomes a new computing model emerged from the rapid development of the internet. Users can reach their resources with high flexibility using the cloud computing systems all over the world. However, such systems are prone to failures. In order to obtain realistic quality of service (QoS) measurements, failure and recovery behaviours of the system should be considered. System's failures and repairs are associated with availability context in QoS measurements. In this paper, performance issues are considered with the availability of the system. Markov Reward Model (MRM) method is used to get QoS measurements. The mean queue length (MQL) results are calculated using the MRM. The results explicitly show that failures and repairs affect the system performance significantly

Lung Lesion Segmentation Using Gaussian Filter and Discrete Wavelet Transform

Lung cancer is the growth of a tumour, referred to as a nodule that arises from cells covering the airways of the respiratory arrangement. Effective detection of lung cancer at premature stages enables any cure options, and reduce risk of insidious surgery and increased continued existence rate. Recently, image processing techniques are extensively used in different medical areas for lung tumour image improvement in early detection and cure stages. This is due to the importance of the time factor of discovering the abnormality issues in target images. The developed system is mainly an algorithm combining different image processing techniques such as filtering, erosion, discrete wavelets transform, and thresholding. However, the main aim of this work is to investigate the effectiveness of different filters along with different types of discrete wavelets toward an accurate segmentation of a lung tumour in a CT image. The experimental results of the developed system show that the use of Gaussian filter with the Haar wavelets is the best for such segmentation task