High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of $p$ -degrees of connectivity concerning objective functions’ evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for $p$ -disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway

Design and Analysis of Circular Polarized Two-Port MIMO Antennas with Various Antenna Element Orientations

This article presents the circularly polarized antenna operating over 28 GHz mm-wave applications. The suggested antenna has compact size, simple geometry, wideband, high gain, and offers circular polarization. Afterward, two-port MIMO antenna are designed to get Left Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP). Four different cases are adopted to construct two-port MIMO antenna of suggested antenna. In case 1, both of the elements are placed parallel to each other; in the second case, the element is parallel but the radiating patch of second antenna element are rotated by 180°. In the third case, the second antenna element is placed orthogonally to the first antenna element. In the final case, the antenna is parallel but placed in the opposite end of substrate material. The S-parameters, axial ratio bandwidth (ARBW) gain, and radiation efficiency are studied and compared in all these cases. The two MIMO systems of all cases are designed by using Roger RT/Duroid 6002 with thickness of 0.79 mm. The overall size of two-port MIMO antennas is 20.5 mm × 12 mm × 0.79 mm. The MIMO configuration of the suggested CP antenna offers wideband, low mutual coupling, wide ARBW, high gain, and high radiation efficiency. The hardware prototype of all cases is fabricated to verify the predicated results. Moreover, the comparison of suggested two-port MIMO antenna is also performed with already published work, which show the quality of suggested work in terms of various performance parameters over them

Deployment Strategies for Underwater Sensing and Processing Networks

Underwater wireless sensor networks (UWSN) have been evolving for the last decade. UWSNs are used to study and explore underwater life for the benefit of both humans and underwater creatures. Current applications for UWSNs involve navigation, sea current and coral reef monitoring, habitat monitoring of marine life, fish farming, weather forecasting, and oil leakage detection. Such applications require the collection of large amounts of accurate data across various temporal and spatial scales. Given the severely limited bandwidth and the high propagation delay of the underwater acoustic channel, the transmission of these bulky data using acoustic waves is challenging. As a result, the delivery of data requires a significant amount of time and energy. Moreover, underwater sensor nodes use batteries as their main energy source and are connected by underwater modems that are considered to be energy-hungry devices. Because more power is needed to transmit large volumes of data, power consumption is a major performance metric to be considered. One way to mitigate these challenges is to develop underwater systems that allow in-network data processing to be performed underwater rather than offshore. This can be achieved by deploying underwater processing (computerized) nodes that have the ability to sense, relay, process, and analyze the collected data. Computerized nodes are different from the traditional underwater sensor nodes that are limited to sensing, relaying, and transmitting the gathered data. By applying data processing techniques such as aggregation, fusion, and mining to the collected raw data, either the required result (output) is sent or invaluable bits are removed. In either case, only valuable information is transmitted instead of the entire volume of data. As a result, the bandwidth is used efficiently, reductions are made in the end-to-end delay and the power consumption, and the network lifetime is increased. However, underwater processing nodes are more expensive than typical sensor nodes and have more features in terms of allocated power, memory capacity, and higher feature processing unit. In addition, in order to satisfy some network performance objectives and constraints, the processing nodes deployment is challenging as their locations must be carefully chosen. Therefore, we first developed an optimization framework based on Integer Linear Programming (ILP) for the processing node deployment. Then we proposed several efficient placement algorithms for the processing node deployment for the purpose of solving the ILP in polynomial time. We also investigated the effect of the processing node deployment on the surface-level gateway deployment, and we show their tradeoffs. Finally, to evaluate the performance of the processing node deployment, we applied the new technique on a real application and compared it with the existing one

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity

IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA

Design and Implementation of Multilayer GIS Framework in Natural Resources Management: Red Sea Area

This study aims to create an integrated geographical information system (GIS) database of natural resources represented by mining activities in the Red Sea area in Sudan. GIS is a vital tool to help the decision-makers in managing and classifying these resources in terms of quantity and quality within the concept of sustainable development. The paper extracts some models of investment map indicators. In addition to that, it conducts a study and research aimed at developing a mineral resources management and discovering and identifying the new areas of mineral wealth in GIS database. The motivation of this study stems from the fact that countries with these types of wealth can greatly grow their gross domestic product (GDP) through the optimum management and governance. Most of the least development countries (LDC) can focus on the search for alternative natural resources (beside other conventional resources, i.e., agriculture) to support their economy. Red Sea region is one of the great regions full of natural and mineral resources (specifically gold and minerals), beside its strategic location on the way of most of the global trade transaction path. This paper designs and implements GIS multiple database layers. The multiple layers were modelled to overcome the technical difficulties that result from processing of large quantities of tempospatial information. The database was built by using an Oracle database system due to its capability of multilayer design

Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications

Wireless Sensor Networks (WSNs) play an important role in underground mining applications. In particular, they help to collect information using sensors and provide monitoring of complex mine environments to avoid potential risks and manage operations. Despite the importance of WSNs, they face the problem of energy consumption and the difficulty of replacing the batteries of the sensor nodes. The distributed energy-efficient aggregation protocol (DEECP) helps to reduce the power consumption of the WSN. This protocol enables an increase in the lifetime of a WSN. The DEECP algorithm uses the clustering concept and selects cluster heads (CHs) according to the election probability based on the ratio between the residual energy and network average energy of each node. However, this method does not provide an optimum solution because it does not take into account the different sensor energy levels. In addition, the algorithm does not consider the effect of the distance between the base station and sensor node likely be chosen to become a CH. This can significantly affect the performance of the WSN. This paper proposes an optimization threshold for CH selection based on three energy levels of a sensor, namely, low, high, and super as well as the measurement of the distances between base stations and possible nodes to be selected as CHs to optimize the CH selection method. The proposed approach is evaluated and compared with DEECP in terms of dead nodes, alive nodes, and network throughput. The results show that the proposed approach outperforms DEECPs in terms of network stability and lifetime

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis