Efficient parallel processing of all-pairs shortest paths on multicore and GPU systems

Finding the shortest path between any two nodes in a graph, known as the All-Pairs Shortest Paths (APSP), is a fundamental problem in many data analysis problems, such as supply chains in logistics, routing protocols in IoT networks that involve consumer electronics as well as data analysis for social networking apps and Google Maps apps used by the general public on their smartphones. In this work, we present a novel approach to solve the APSP problem on multicore and GPU systems. In our approach, a graph is first pre-processed by partitioning the graph into sub-graphs. Then, each sub-graph is processed in parallel using any existing shortest path algorithm such as the Floyd-Warshall algorithm or Dijkstra’s algorithm. Finally, the distance results in individual sub-graphs are aggregated to obtain the distances of APSP for the entire graph. OpenMP and CUDA are used to implement the parallelization on multicore CPUs and GPUs, respectively. We conduct the extensive experiments with both synthetic and real-world graphs on the JADE (Joint Academic Data Science Endeavour) cluster at the University of Oxford, which is part of the Tier-2 high performance computing facilities in the UK. In the experiments, we compared our methods with three existing APSP algorithms in the literature, including n-Dijkstra, ParAPSP and SuperFW. The results show that our methods outperform the existing algorithms, achieving the speedup of up to 8.3x over Dijkstra

Mobile Base Program for Drug-Drug Interactions (MBPDDIs)

A mobile application program was used in performing a drug-drug interactions (DDIs) for drugs used in Iraq, depending on the drug-drug interactions chart (DDIsC) originated by Ninava Drug Industry (NDI). Two programs were used for designing this work; the first is Microsoft office access which is used to design the form which included the list of drugs and ten registers under it contain different drugs names (Access of drug was available through browsing therapeutic groups or searching for a brand name). If the drug in combo list interacts with more than ten drugs, the combo contains the same name of drug but with number 2, 3 and so on.The design which includes in mobile contains android system.In the second design of the drugs interaction a visual basic program is used. Two lists of drugs were used in this program. When a drug from the first list is selected with another drug from the second list, the symbol offer at text box

Optimal deep learning driven intrusion detection in SDN-Enabled IoT environment

In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT environment in this scenario. The current study devises a Harmony Search algorithm-based Feature Selection with Optimal Convolutional Autoencoder (HSAFS-OCAE) for intrusion detection in the SDN-enabled IoT environment. The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS (HSAFS) technique is exploited at first for feature selection. Next, the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment. Finally, the Artificial Fish Swarm Algorithm (AFSA) is used to fine-tune the hyperparameters. This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty. The proposed HSAFS-OCAE technique was experimentally validated under different aspects, and the comparative analysis results established the supremacy of the proposed model

Efficient computational offloading of dependent tasks in mobile edge networks

Mobile Edge Network (MEN) is emerging as a novel computing paradigm that puts high storage and computational power within easy reach of mobile users for a range of applications such as big data applications and location-based services. The MENs consist of a number of small base stations, which we call Cloudlets, that provide the required services to end-users. Ecosystems are resource-constrained, making execution of resource-hungry applications challenging. Computation offload between ecosystems and cloudlets plays a key role in this vision and ensures that the integration between ecosystem and cloudlet is seamless with better quality of service such as lower latency. Analysis of the available literature relating to currently proposed offloading techniques focuses on centralised approaches with a small number of mostly static user devices hosting in-dependent tasks. In this thesis, we address three major offloading problems: (i) that algorithms consider distributed environment with multi offloading systems, (ii) users with ecosystems are mobile and (iii) tasks are dependent as (DAGs). We develop the offloading algorithms for mobile user devices with hosting of dependent tasks, where a dependent task cannot start until its immediate predecessor tasks have completed, with the aim of reducing completion latency. We start by formalizing the dependent task offloading problem as a constraint satisfaction problem with all proposed algorithms. While the first objective aims at minimising completion latency with central server in edge, the second objective aims at minimising completion latency with multi systems in distributed environment. We construct optimisation models for both objectives and develop two offloading algorithms to approximate the optimal solution. According to the results with ns-3 simulation, Optimisation CPLEX, and real deployment, our offloading algorithms are able to efficiently produce allocation schemes that are close to optimal during the offloading

Dependent task offloading with deadline-aware scheduling in mobile edge networks

In the field of the Internet of Things (IoT), Edge computing has emerged as a revolutionary paradigm that offers unprecedented benefits by serving the IoT at the network edge. One of the primary advantages of edge computing is that it reduces the job completion time by offloading tasks at the edge server from the IoT. Typically, a job is made up of dependent tasks in which the output of one task is required as the input to the other. This work proposes a directed cyclic graph model that represents the dependencies among these tasks focusing on jointly optimizing task dependencies with deadline constraints for tasks that are delay-sensitive. Thus, dependent tasks are scheduled while considering their deadlines using priority-aware scheduling. For tasks with no deadlines, the processing is done with First-Come-First-Serve (FCFS) scheduling. The tasks with a priority are offloaded to the suitable edge server for processing by using a priority queue to enhance the task satisfaction rate under deadline constraints. To model the suitable edge server decision, we use the Markov decision process (MDP) that minimizes the total completion time. Additionally, we model the mobility of users while offloading tasks to the edge servers. The throughput results demonstrate that the proposed strategy outperforms random offloading, the highest data rate offloading (HDR), the highest computing device (HCD), and delay-dependent priority-aware offloading (DPTO), by 66.67%, 43.75%, 27.78%, and 4.55%, respectively. Furthermore, the proposed strategy surpasses random, HDR, and HCD offloading in terms of task satisfaction rate by 20.48%, 16.28%, and 12.36%, respectively

Modified Equilibrium Optimization Algorithm With Deep Learning-Based DDoS Attack Classification in 5G Networks

5G networks offer high-speed, low-latency communication for various applications. As 5G networks introduce new capabilities and support a wide range of services, they also become more vulnerable to different kinds of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Effective DDoS attack classification in 5G networks is a critical aspect of ensuring the security, availability, and performance of these advanced communication infrastructures. In recent days, machine learning (ML) and deep learning (DL) models can be employed for an accurate DDoS attack detection process. In this aspect, this study designs a Modified Equilibrium Optimization Algorithm with Deep Learning based DDoS Attack Classification (MEOADL-ADC) method in 5G networks. The goal of the MEOADL-ADC technique is the automated classification of DDoS attacks in the 5G network. The MEOADL-ADC technique follows a three-stage process such as feature selection, classification, and hyperparameter tuning. Primarily, the MEOADL-ADC technique employs MEOA based feature selection approach. Next, the MEOADL-ADC technique utilizes the long short-term memory (LSTM) model for the classification of DDoS attacks. Finally, the tunicate swarm algorithm (TSA) is exploited to adjust the hyperparameter of the LSTM model. The design of MEOA-based feature selection and TSA-based hyperparameter tuning shows the novelty of the work. The experimental outcome of the MEOADL-ADC method is tested on a benchmark dataset, and the outcomes indicate the betterment of the MEOADL-ADC algorithm over the current methods with maximum accuracy of 97.60%

Modified Bald Eagle Search Algorithm With Deep Learning-Driven Sleep Quality Prediction for Healthcare Monitoring Systems

Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenient, nonintrusive, and cost-effective method. The applications of deep learning (DL) in sleep quality prediction represent a groundbreaking technique for addressing sleep-related disorders. In this aspect, the article offers the design of a Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality Prediction (MBES-DLSQP) for Healthcare Monitoring Systems. The MBES-DLSQP technique combines the strengths of a DL model with a hyperparameter tuning strategy to provide precise sleep quality predictions. At the primary stage, the MBES-DLSQP technique undergoes data pre-processing. Besides, the MBES-DLSQP technique uses a stacked sparse autoencoder (SSAE)-based prediction model, which can extract and encode high-dimensional sleep data. The MBES-DLSQP incorporates MBESA-based hyperparameter tuning which assures its optimal configurations to further boost the efficiency of the SSAE model. The experimental outcome of the MBES-DLSQP algorithm is tested on the sleep dataset from the Kaggle repository. The experimental value infers that the MBES-DLSQP technique shows promising performance in sleep quality prediction with a maximum accuracy of 98.33%

SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks

In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model

Quantum Water Strider Algorithm with Hybrid-Deep-Learning-Based Activity Recognition for Human–Computer Interaction

Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches

Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network

The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches