Experimental Analysis and Optimization Approach of Self-Clocked Rate Adaptation for Multimedia Congestion Control Algorithm in Emulated 5G Environment.

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases. Several interesting algorithms are proposed to focus on this dilemma, such as the Self-Clocked Rate Adaptation for Multimedia (SCReAM) designed for interactive real-time video streaming applications. One of the main issues of SCReAM is the high design complexity due to the large size of its documentation and coding. Furthermore, there is a considerable number of parameters that can be adjusted to accomplish the desired performance. This study proposes a guided parameters' tuning approach to assess and optimize the SCReAM algorithm in an emulated 5G environment through a detailed exploration of its parameters. The proposed approach consists of three phases, namely, the initialization phase, the standalone experimentation phase, and the hybrid experimentation phase. In the first phase, we illustrate the method of initializing and implementing the environment, followed by specifying the investigated parameters' settings, testing, and validation. The second phase aims to investigate SCReAM parameters in isolation to identify the effect on the performance in relation to network queue delay, smoothed Round Trip Time (sRTT), and throughput. The final phase discusses the possibility of achieving the optimum performance by combining various sets to provide researchers with clear and explicit guidelines to establish an adequate SCReAM behavior for the desired application. To the best of our knowledge, this is the first study that proposes a preliminary and comprehensive analysis of the SCReAM algorithm. Based on the proposed approach, when L4S/ECN is disabled, we reduced the network queue delay by 63.36% and increased the network throughput by 48.6% as compared to the results generated by the original design. In L4S/ECN-enabled mode, the network queue delay is reduced by 16.17% while the network throughput increased by 93%.

Leveraging Blockchain Technology for Ensuring Security and Privacy Aspects in Internet of Things: A Systematic Literature Review.

As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT's basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.

Role of deep learning in early detection of COVID-19: Scoping review.

BACKGROUND: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. OBJECTIVE: Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. METHODS: This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. RESULTS: We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. CONCLUSION: The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.