Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

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Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Abstract: Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy

Privacy Preserving Face Recognition in Cloud Robotics : A Comparative Study

Abstract: Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms

MicroRNA-320 suppresses colorectal cancer by targeting SOX4, FOXM1, and FOXQ1

Colorectal cancer (CRC) is the third most common cancer causing high mortality rates world-wide. Delineating the molecular mechanisms leading to CRC development and progression, including the role of microRNAs (miRNAs), are currently being unravelled at a rapid rate. Here, we report frequent downregulation of the microRNA miR-320 family in primary CRC tissues and cell lines. Lentiviral-mediated re-expression of miR-320c (representative member of the miR-320 family) inhibited HCT116 CRC growth and migration in vitro, sensitized CRC cells to 5-Fluorouracil (5-FU), and inhibited tumor formation in SCID mice. Global gene expression analysis in CRC cells over-expressing miR-320c, combined with in silico prediction identified 84 clinically-relevant potential gene targets for miR-320 in CRC. Using a series of biochemical assays and functional validation, SOX4, FOXM1, and FOXQ1 were validated as novel gene targets for the miR-320 family. Inverse correlation between the expression of miR-320 members with SOX4, FOXM1, and FOXQ1 was observed in primary CRC patients' specimens, suggesting that these genes are likely bona fide targets for the miR-320 family. Interestingly, interrogation of the expression levels of this gene panel (SOX4, FOXM1, and FOXQ1) in The Cancer Genome Atlas (TCGA) colorectal cancer data set (319 patients) revealed significantly poor disease-free survival in patients with elevated expression of this gene panel (P-Value: 0.0058). Collectively, our data revealed a novel role for the miR-320/SOX4/FOXM1/FOXQ1 axes in promoting CRC development and progression and suggest targeting those networks as potential therapeutic strategy for CRC

Cognitive IoT-based e-Learning System : enabling context-aware remote schooling during the pandemic

Abstract: (e 2019–2020 coronavirus pandemic had far-reaching consequences beyond the spread of the disease and efforts to cure it. Today, it is obvious that the pandemic devastated key sectors ranging from health to economy, culture, and education. As far as education is concerned, one direct result of the spread of the pandemic was the resort to suspending traditional in-person classroom courses and relying on remote learning and homeschooling instead, by exploiting e-learning technologies, but many challenges are faced by these technologies. Most of these challenges are centered around the efficiency of these delivery methods, interactivity, and knowledge testing. (ese issues raise the need to develop an advanced smart educational system that assists home-schooled students, provides teachers with a range of smart new tools, and enable a dynamic and interactive e-learning experience. Technologies like the Internet of things (IoT) and artificial intelligence (AI), including cognitive models and contextawareness, can be a driving force in the future of e-learning, opening many opportunities to overcome the limitation of the existing remote learning systems and provide an efficient reliable augmented learning experience. Furthermore, virtual reality (VR) and augmented reality (AR), introduced in education as a way for asynchronous learning, can be a second driving force of future synchronous learning. (e teacher and students can see each other in a virtual class even if they are geographically spread in a city, a country, or the globe. (e main goal of this work is to design and provide a model supporting intelligent teaching assisting and engaging e-learning activity. (is paper presents a new model, ViRICTA, an intelligent system, proposing an end-to-end solution with a stack technology integrating the Internet of things and artificial intelligence. (e designed system aims to enable a valuable learning experience, providing an efficient, interactive, and proactive context-aware learning smart services

Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic

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Alts : An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Abstract: According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches

Role of Blockchain Technology in Combating COVID‐19 Crisis

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Analysis of Security Attacks & Taxonomy in Underwater Wireless Sensor Networks

Abstract: Underwater Wireless Sensor Networks (UWSN) have gained more attention from researchers in recent years due to their advancement in marine monitoring, deployment of various applications, and ocean surveillance. The UWSN is an attractive field for both researchers and the industrial side. Due to the harsh underwater environment, own capabilities, open acoustic channel, it's also vulnerable to malicious attacks and threats. Attackers can easily take advantage of these characteristics to steal the data between the source and destination. Many review articles are addressed some of the security attacks and Taxonomy of the Underwater Wireless Sensor Networks. In this study, we have briefly addressed the Taxonomy of the UWSNs from the most recent research articles related to the well-known research databases. This paper also discussed the security threats on each layer of the Underwater Wireless sensor networks. This study will help the researcher’s design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues