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High-speed networks are utilized to satisfy user demands by using cloud and edge services. Due to the heterogeneous nature of these networks, security, trust, and privacy are the significant challenges for these networks. These networks are vulnerable to various security threats, lack of trustworthy devices, and user privacy concerns due to architectural complexities, diversity of protocols, and dynamic network topologies. This Systematic Literature Review (SLR) explores these networks in terms of technologies, edge and cloud services, security, trust and privacy concerns. Torress-Carrion methodology is used as a guideline to conduct this review to find the recent security, trust and privacy concerns. The attacks related to each area and their solutions are discussed to evaluate their strengths and weaknesses. The various existing technologies explore like Blockchain, encryption, hashing, and fuzzy logic. In addition, the trust evaluation and user privacy provision techniques are also discuss. This SLR also explores the methods designed for different communication layers against the various multilingual passive/active attacks like Sybil, Denial of Service (DoS), Distributed Denial of Service (DDoS), and Man in the Middle (MITM). This SLR will provide a direction to new researchers in the field of security, trust, and privacy to design more efficient and effective solutions to deal with new security challenges.

With tremendous advancements in smart phone industry and IoT devices, edge computing has emerged to provide computational services at edge of the network. As a result, applications can be executed in a distributive manner to meet the latency requirements of applications. Despite these advancements, these devices are still considered to have limited energy, memory and computational resources compared to stationary standalone devices. Mobile edge computing extends mobile cloud computing close to end-users, decreasing latency and bandwidth utilisation by offloading tasks to edge servers. Fog computing places nodes between cloud and edge servers. A challenging approach in these paradigms is establishing offloading decision to edge/fog server, and distributed application execution at run time for heterogeneous devices. This paper reviews execution of resource-intensive tasks on mobile devices in distributed frameworks. The objective is to identify research gaps and highlight new research directions that can be pursued in future. Application execution involves application partitioning, task allocation, task execution and task resumption. In this paper, we propose a separate taxonomy for each step in application execution, reviews state-of-the-art techniques, and analyses critical aspects of application execution in a distributed manner. It further investigates similarities and differences among these techniques based on each step of application execution.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Objective: To evaluate the in vitro antifungal activity of Potash Alum (Phitkary) solution on Candida Albicans growth on heat cured acrylic resin. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Oral Pathology, Peshawar Dental College, Peshawar, from Nov 2019 to Jan 2020. Methodology: Acrylic resin discs (n=120) of standard size were prepared following the manufacturer’s instructions. The acrylic discs were contaminated with a suspension of 106cfus/ml of C. albicans in Sabouraud dextrose broth for 24 hours. After contamination, 30 acrylic discs were randomly selected, washed, sonicated, and plated for colony counts. The remaining 90 acrylic discs were randomly divided into 3 groups, 30 acrylic discs in each group. Group A discs were kept in 5.25% Sodium Hypochlrite solution, group B discs were kept in sterile Phosphate Buffered Saline and group C discs were kept in Potash-alum solution (10mg/ml). After 2 hours, all discs were washed, sonicated, and cultured on Sabouraud dextrose agar in serial dilutions for colony counts. Data was analysed using SPSS version-19. Results: Both Potash Alum (10 mg/ml) and Sodium Hypochlorite were effective in completely removing the attached Candida cells from the acrylic resin. In contrast, the negative control was only able to reduce viable counts by 23.6%. Sodium Hypochlorite and Potash Alum both showed statistically significant activity (p

Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high-quality features and very few samples of data. Feature selection is performed on such datasets to identify the optimal feature subset. The major goal of feature selection is to improve the accuracy by identifying a minimal feature subset. For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. The performance of SVM is analyzed using accuracy, sensitivity, specificity, and f-measure. The accuracy of 94.45% and 91% is achieved on each dataset, respectively.

Due to the availability of fingerprint verification technology on mobile devices, fingerprint based human identification has become the most widely used biometric technology in everyday life. The elderly find digital payment applications difficult to operate and unsecure. This work has exploited fingerprint verification availability on mobile devices to provide a user friendly and secure digital wallet payment facility to elderlies who are unable to avail this facility due to the complex infrastructure of traditional authentication mechanisms. A novel digital payment mechanism is presented which uses Bluetooth technology of mobile devices for billing at the point of sale and fingerprint verification for user authentication. A proof of the concept study is presented to validate the proposed model using state of the art usability questionnaires and attributes with the target group. The result indicates that the proposed methodology provides satisfaction and ease of use to the user.

Cloud computing is rapidly taking over the information technology industry because it makes computing a lot easier without worries of buying the physical hardware needed for computations, rather, these services are hosted by companies with provide the cloud services. These companies contain a lot of computers and servers whose main source of power is electricity, hence, design and maintenance of these companies is dependent on the availability of steady and cheap electrical power supply. Cloud centers are energy-hungry. With recent spikes in electricity prices, one of the main challenges in designing and maintenance of such centers is to minimize electricity consumption of data centers and save energy. Efficient data placement and node scheduling to offload or move storage are some of the main approaches to solve these problems. In this article, we propose an Extreme Gradient Boosting (XGBoost) model to offload or move storage, predict electricity price, and as a result reduce energy consumption costs in data centers. The performance of this method is evaluated on a real-world dataset provided by the Independent Electricity System Operator (IESO) in Ontario, Canada, to offload data storage in data centers and efficiently decrease energy consumption. The data is split into 70% training and 30% testing. We have trained our proposed model on the data and validate our model on the testing data. The results indicate that our model can predict electricity prices with a mean squared error (MSE) of 15.66 and mean absolute error (MAE) of 3.74% respectively, which can result in 25.32% cut in electricity costs. The accuracy of our proposed technique is 91% while the accuracy of benchmark algorithms RF and SVR is 89% and 88%, respectively.

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Cloud Computing involves utilization of centralized computing resources and services, including remote servers, storage, programs, and usages which minimize the power utilization of the client assets. Therefore, it is extremely important to accomplish energy efficiency of cloud computing. Virtualization is used to set up a foundation for the execution part as the heart of energy effective cloud. Virtualization incorporates certain advancements, such as consolidation and resource utilization. A number of techniques, such as DVFS virtualization as well as teleportation can be used by empowering the tasks of multiple virtual types of equipment to a single server to increase the vitality proficiency of datacenters. The objective of this review is to analyze contemporary for energy as well as performance management, vitality for effective data centers and resource distributions. Our review will address the latest issues researchers have addressed in energy as well as management of performance in recent years. We will take a closer look at these existing techniques based on tools, OS, virtualization, and datacenter stages taxonomy. Finally, a performance comparison of existing techniques is presented that can assist in identifying gaps for future research in this area.

Proposed is the energy efficient management system to develop a robust micro grid with lesser loss of probability and efficient with energy. We have presented techniques from the related work and find the optimized technique as well. Our research mainly depend upon the Particle Swarm Optimization in which we divide the grids into local and global solutionsto find the optimized solution for the power supply and to overcome the nonlinear loads. We then compared the PSO with LEACH (Low energy adaptive clustering hierarchy) and found that PSO is the best solution for energy efficient management system.

Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.

In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. To the best of our knowledge, no research to date has been conducted to assist network forensics investigators and cloud service providers in finding an optimal method for investigation of network vulnerabilities found in cloud networks. To this end and in this paper, the state-of-the-art C-NFMs are classified and analyzed based on the cloud network perspective using SWOT analysis. It implies that C-NFMs have a suitable impact on cloud network, which further requires for reformation to ensure its applicability in cloud networks.

The latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC.

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer’s reviews. Online consumer’s reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers’ reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers’ reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers’ feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.