New quality of service routing algorithms based on local state information. The development and performance evaluation of new bandwidth-constrained and delay-constrained quality of service routing algorithms based on localized routing strategies.

The exponential growth of Internet applications has created new challenges for the control and administration of large-scale networks, which consist of heterogeneous elements under dynamically changing traffic conditions. These emerging applications need guaranteed service levels, beyond those supported by best-effort networks, to deliver the intended services to the end user. Several models have been proposed for a Quality of Service (QoS) framework that can provide the means to transport these services. It is desirable to find efficient routing strategies that can meet the strict routing requirements of these applications. QoS routing is considered as one of the major components of the QoS framework in communication networks. In QoS routing, paths are selected based upon the knowledge of resource availability at network nodes and the QoS requirements of traffic. Several QoS routing schemes have been proposed that differ in the way they gather information about the network state and the way they select paths based on this information. The biggest downside of current QoS routing schemes is the frequent maintenance and distribution of global state information across the network, which imposes huge communication and processing overheads. Consequently, scalability is a major issue in designing efficient QoS routing algorithms, due to the high costs of the associated overheads. Moreover, inaccuracy and staleness of global state information is another problem that is caused by relatively long update intervals, which can significantly deteriorate routing performance. Localized QoS routing, where source nodes take routing decisions based solely on statistics collected locally, was proposed relatively recently as a viable alternative to global QoS routing. It has shown promising results in achieving good routing performance, while at the same time eliminating many scalability related problems. In localized QoS routing each source¿destination pair needs to determine a set of candidate paths from which a path will be selected to route incoming flows. The goal of this thesis is to enhance the scalability of QoS routing by investigating and developing new models and algorithms based on the localized QoS routing approach. For this thesis, we have extensively studied the localized QoS routing approach and demonstrated that it can achieve a higher routing performance with lower overheads than global QoS routing schemes. Existing localized routing algorithms, Proportional Sticky Routing (PSR) and Credit-Based Routing (CBR), use the blocking probability of candidate paths as the criterion for selecting routing paths based on either flow proportions or a crediting mechanism, respectively. Routing based on the blocking probability of candidate paths may not always reflect the most accurate state of the network. This has motivated the search for alternative localized routing algorithms and to this end we have made the following contributions. First, three localized bandwidth-constrained QoS routing algorithms have been proposed, two are based on a source routing strategy and the third is based on a distributed routing strategy. All algorithms utilize the quality of links rather than the quality of paths in order to make routing decisions. Second, a dynamic precautionary mechanism was used with the proposed algorithms to prevent candidate paths from reaching critical quality levels. Third, a localized delay-constrained QoS routing algorithm was proposed to provide routing with an end-to-end delay guarantee. We compared the performance of the proposed localized QoS routing algorithms with other localized and global QoS routing algorithms under different network topologies and different traffic conditions. Simulation results show that the proposed algorithms outperform the other algorithms in terms of routing performance, resource balancing and have superior computational complexity and scalability features.Umm AlQura University, Saudi Arabi

A Secure Cloud Computing Model based on Data Classification

AbstractIn cloud computing systems, the data is stored on remote servers accessed through the internet. The increasing volume of personal and vital data, brings up more focus on storing the data securely. Data can include financial transactions, important documents, and multimedia contents. Implementing cloud computing services may reduce local storage reliance in addition to reducing operational and maintenance costs. However, users still have major security and privacy concerns about their outsourced data because of possible unauthorized access within the service providers. The existing solutions encrypt all data using the same key size without taking into consideration the confidentiality level of data which in turn will increase the cost and processing time. In this research, we propose a secure cloud computing model based on data classification. The proposed cloud model minimizes the overhead and processing time needed to secure data through using different security mechanisms with variable key sizes to provide the appropriate confidentiality level required for the data. The proposed model was tested with different encryption algorithms, and the simulation results showed the reliability and efficiency of the proposed framework

A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance

A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance

Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques

Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy

MIMO Radio Frequency Identification: A Brief Survey

In this paper, we briefly look at the latest state-ot-the-art in the domain of multi-input multi-output (MIMO) radio frequency identification (RFID) systems while detailing the work done in the domain of anti-collision, range enhancements, bit error rate (BER) improvements and security. Various passive ultra-high frequency (UHF) RFID implementations are considered that employ multiple antennas at the reader and single or multiple antennas at each tag. We look at several recent works those explored MIMO for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the BER as well as range extension. With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems under-perform in such settings causing low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision schemes. We provide a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved

Anti-Collision Algorithm for Identification in Precision Agriculture Applications

Precision Agriculture (PA) techniques employing Internet of Things (IoT) can significantly improve crop yields and enhance productivity. Radio frequency identification (RFID) based IoT systems can enhance precision livestock farming by enabling livestock traceability and identification in large farms, but congestion is a major challenge. In such conditions, livestock estimation using RFID is a complex task because of high chances of tag collision due to significant increase in interference at the gates. Several anti-collision algorithms have been explored in recent past to address this issue with the aid of a single antenna. However, the throughput gains from a single antenna are limited particularly for dense scenarios like gates at a small ruminant farm. Therefore, we propose a multi-antenna RFID reader system with a dynamic frame slotted ALOHA (DFSA) anti-collision algorithm to address the tag collision problem by exploiting the spatial diversity of the multiple inputs and multiple outputs (MIMO) backscattering channel. The proposed technique is simulated in MATLAB for several tag population figures both with and without interference actions of various intensities. Results have shown that significant throughput gains can be achieved, in comparison with gains attained by anti-collision algorithms based on a single antenna. The simulation testbed enables us to evaluate the proposed technique for multiple receive antenna configurations