Privacy protection and energy optimization for 5G-aided industrial internet of things

The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

MULDASA:Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialect

Impact of Leadership Support on KMS-based Knowledge Seeking Behavior: Lessons Learned

Abstract: KM is a strategy that improves organizational performance and survival in a business environment. It is a challenging scenario for the organizations to manage their knowledge properly; in practice, a small amount of missing knowledge sometimes causes a great delay in the project. KMS are becoming an increasingly popular tool of KM in software development organizations. Tempting employees to seek knowledge from KMS remain an important concern for researchers and practitioners. Leadership support has been widely recognized in many studies as an important driving factor of knowledge seeking and contributions; however, the role of leadership support in promoting knowledge seeking behavior using KMS has not been adequately addressed. The purpose of this paper is to examine the leadership support and its relationship with the success of KMS as perceived by employees in China's software development organizations who are using KMS for knowledge seeking. The data which were collected from a survey of 73 employees working in different software development organizations of china were used to test the proposed research model. The results of the survey revealed that leadership support is positively related with employee's continuous intention of seeking knowledge. Further, Leadership support helps in promoting the use of KMS for knowledge seeking within organizations. This analysis is vital for senior management and leaders of the organizations who want to make their KMS successful, and to establish a knowledge sharing capability of their employees. In doing so, leaders should focus their efforts towards promoting KMS-based knowledge seeking behavior in their organizations

Improving the Safety and Security of Software Systems by Mediating SAP Verification

Security and performance (SAP) are two critical NFRs that affect the successful completion of software projects. Organizations need to follow the practices that are vital to SAP verification. These practices must be incorporated into the software development process to identify SAP-related defects and avoid failures after deployment. This can only be achieved if organizations are fully aware of SAP verification activities and appropriately include them in the software development process. However, there is a lack of awareness of the factors that influence SAP verification, which makes it difficult for businesses to improve their verification efforts and ensure that the released software meets these requirements. To fill this gap, this research study aimed to identify the mediating factors (MFs) influencing SAP verification and the actions to promote them. Ten MFs and their corresponding actions were identified after thoroughly reviewing the existing literature. The mapping of MFs and their corresponding actions were initially evaluated with the help of a pilot study. Mathematical modeling was utilized to model these MFs and examine each MF’s unique effect on software SAP verification. In addition, two case studies with a small- and a medium-sized organization were used to better understand the function these MFs play in the process of SAP verification. The research findings suggested that MFs assist software development organizations in their efforts to integrate SAP verification procedures into their standard software systems. Further investigation is required to support the understanding of these MFs when building modern software systems

Energy Optimization for Smart Cities Using IoT

When it comes to smart cities, one of the biggest issues is energy optimization. This is because these cities employ a large number of interconnected devices to autonomously manage city operations, which consumes a lot of energy. This difficulty has been addressed in this paper by using the advantages of contemporary cutting-edge technologies such as the Internet of Things (IoT), 5 G, and cloud computing for energy efficiency in smart cities. With the use of these cutting-edge technologies, we have proposed a model that can be used to optimize energy consumption in smart homes and smart cities alike. Street lighting, building and street billboards, smart homes, and smart parking are among the four essential features of smart cities that would benefit from the proposed model’s energy savings. All smart city electric appliances will be equipped with IoT sensors that will detect movements and react to commands. In order to transport data swiftly between communication channels and the cloud, 5 G technology will be deployed, and the cloud technology will be used to store and retrieve data effectively. The suggested model was evaluated using mathematical modeling, and the findings indicate that the proposed model may assist in improving energy usage in smart cities

Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies

Kingdom of Saudi Arabia (KSA) highways hold the record for having the straightest, longest highways in the world. Since the country’s major population centers are dispersed across the country and due to the country’s geography, which includes valleys, deserts, and mountains, among other landscapes, these highways connect the many cities of the kingdom and neighboring nations. However, it is still challenging to provide emergency assistance in a timely way in the case of accidents, such as first aid, medical aid, police protection, etc. The transport ministry is actively working on improvements and safety features for the drivers. This research proposes a CET (cutting-edge technologies)-based model named the real-time security, health, and privacy monitoring model for passenger safety (RTSHPMP) for securing the traveler’s safety and privacy besides medical and legal help. The vehicle will be equipped with IoT-based front-back cameras to collect real-time data and share it with the cloud using 5G network. The local and national trusted authorities (TAs) will monitor the collected cloud data and inform the government machinery (police, first aid, fire brigade, hospitals) in the case of an accident. In addition, the data collected through other vehicles on the road at the time of the incident will help supply evidence linked to the accident. The RTSHPMP was evaluated with the help of a case study, and the results show that it provides an efficient and secure mechanism for traveler safety on Saudi highways at the time of need

Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study

Health insurance has become a crucial component of people’s lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP’s applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting

Enhancing diabetic retinopathy classification using deep learning

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the “Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection” dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL

Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies

Kingdom of Saudi Arabia (KSA) highways hold the record for having the straightest, longest highways in the world. Since the country’s major population centers are dispersed across the country and due to the country’s geography, which includes valleys, deserts, and mountains, among other landscapes, these highways connect the many cities of the kingdom and neighboring nations. However, it is still challenging to provide emergency assistance in a timely way in the case of accidents, such as first aid, medical aid, police protection, etc. The transport ministry is actively working on improvements and safety features for the drivers. This research proposes a CET (cutting-edge technologies)-based model named the real-time security, health, and privacy monitoring model for passenger safety (RTSHPMP) for securing the traveler’s safety and privacy besides medical and legal help. The vehicle will be equipped with IoT-based front-back cameras to collect real-time data and share it with the cloud using 5G network. The local and national trusted authorities (TAs) will monitor the collected cloud data and inform the government machinery (police, first aid, fire brigade, hospitals) in the case of an accident. In addition, the data collected through other vehicles on the road at the time of the incident will help supply evidence linked to the accident. The RTSHPMP was evaluated with the help of a case study, and the results show that it provides an efficient and secure mechanism for traveler safety on Saudi highways at the time of need