A wireless sensor network system for border security and crossing detection

The protection of long stretches of countries’ borders has posed a number of challenges. Effective and continuous monitoring of a border requires the implementation of multi-surveillance technologies, such as Wireless Sensor Networks (WSN), that work as an integrated unit to meet the desired goals. The research presented in this thesis investigates the application of topologically Linear WSN (LWSNs) to international border monitoring and surveillance. The main research questions studied here are: What is the best form of node deployment and hierarchy? What is the minimum number of sensor nodes to achieve k− barrier coverage in a given belt region? iven an appropriate network density, how do we determine if a region is indeed k−barrier covered? What are the factors that affect barrier coverage? How to organise nodes into logical segments to perform in-network processing of data? How to transfer information from the networks to the end users while maintaining critical QoS measures such as timeliness and accuracy. To address these questions, we propose an architecture that specifies a mechanism to assign nodes to various network levels depending on their location. These levels are used by a cross-layer communication protocol to achieve data delivery at the lowest possible cost and minimal delivery delay. Building on this levelled architecture, we study the formation of weak and strong barriers and how they determine border crossing detection probability. We propose new method to calculate the required node density to provide higher intruder detection rate. Then, we study the effect of people movement models on the border crossing detection probability. At the data link layer, new energy balancing along with shifted MAC protocol are introduced to further increase the network lifetime and delivery speed. In addition, at network layer, a routing protocol called Level Division raph (LD ) is developed. LD utilises a complex link cost measurement to insure best QoS data delivery to the sink node at the lowest possible cost. The proposed system has the ability to work independently or cooperatively with other monitoring technologies, such as drowns and mobile monitoring stations. The performance of the proposed work is extensively evaluated analytically and in simulation using real-life conditions and parameters. The simulation results show significant performance gains when comparing LD to its best rivals in the literature Dynamic Source Routing. Compared to DSR, LD achieves higher performance in terms of average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining similar performance in terms of normalised routing load and energy consumption

Challenges Hindering a Supportive Culture of Dialogue in Saudi Arabia

This study identified challenges hindering high school administration to establish a supportive culture of dialogue within the school. A questionnaire was subsequently designed, and its validity and reliability were verified. The questionnaire was then applied to a randomly selected sample of high school principals (N=39) and teachers (N=115) in Riyadh, Saudi Arabia. The researchers used various statistical methods appropriate for analyzing the collected data. Among the key findings of the study include: (1) administration of Riyadh high schools support building a supportive organizational culture of dialogue; and (2) the main challenges for them in establishing a supportive culture of dialogue were related to Organizational, Physical and Human difficulties. The study, therefore, recommended providing guidelines and assistance for creating a supportive culture of dialogue, including the provision of necessary training for the administration and teachers to enhance their abilities to build an appropriate culture of dialogue. Keywords: Culture of dialogue, dialogic pedagogy, high schools, organizational culture, Saudi Arabia

IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate

User-centric secured smart virtual assistants framework for disables

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes security and privacy considerations paramount in virtual assistant platforms. The gaps and weaknesses in existing systems can be identified by researching the context of current practice concerning their features, usability, limits in security procedures, and privacy restrictions. Therefore, we present a framework that combines blockchain-based security with federated learning (FL) to address the current shortcomings of virtual assistant technology. The examination focuses on two primary facets of cutting-edge virtual assistants. Firstly, it evaluates existing IoT-based virtual personal assistant systems designed for persons with disabilities, examining their features, usability, and limitations. The aim is to identify the specific needs and requirements of individuals with disabilities, considering their unique challenges and preferences in utilizing virtual assistant technologies. Second, considering the sensitivity of the information sent between users and virtual assistants, it explores the issues of security and privacy that arise while using such systems. The investigation covers authentication, data encryption, access control, and data privacy rules to provide a snapshot of the prevailing state protecting virtual assistants. Besides this, the framework strengthens the privacy and security of virtual assistants using blockchain technology. Through several empirical trials, it is found that the framework maintains better performance and usability, along with the provision of robust security mechanisms to safeguard user data and guarantee privacy

IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate

Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on

Architecture and enhanced-algorithms to manage servers-processes into network: a management system

This work investigates minimizing the makespan of multiple servers in the case of identical parallel processors. In the case of executing multiple tasks through several servers and each server has a fixed number of processors. The processors are generally composed of two processors (core duo) or four processors (quad). The meaningful format of the number of processors is 2k, and k ≥ 0. The problem is to find a schedule that minimizes the makespan on 2k processors. This problem is identified as NP-hard one. A new network architecture is proposed based on the addition of server management. In addition, two novel algorithms are proposed to solve the addressed scheduling problems. The proposed algorithms are based on the decomposition of the main problem in several sub-problems that are applied to develop new heuristics. In each level of the generated tree, some results are saved and used to decompose the set of processes into subsets for the next level. The proposed methods are experimentally examined showing that the running time of the proposed heuristics is remarkably better than its best rival from the literature. The application of this method is devoted to the network case when there are several servers to be exploited. The experimental results show that in 87.9% of total instances, the most loaded and least loaded subset-sum heuristic (MLS) reaches the best solution. The best-proposed heuristic reaches in 87.4% of cases the optimal solution in an average time of 0.002 s compared with the best of the literature which reaches a solution in an average time of 1.307 s

User-centric secured smart virtual assistants framework for disables

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes security and privacy considerations paramount in virtual assistant platforms. The gaps and weaknesses in existing systems can be identified by researching the context of current practice concerning their features, usability, limits in security procedures, and privacy restrictions. Therefore, we present a framework that combines blockchain-based security with federated learning (FL) to address the current shortcomings of virtual assistant technology. The examination focuses on two primary facets of cutting-edge virtual assistants. Firstly, it evaluates existing IoT-based virtual personal assistant systems designed for persons with disabilities, examining their features, usability, and limitations. The aim is to identify the specific needs and requirements of individuals with disabilities, considering their unique challenges and preferences in utilizing virtual assistant technologies. Second, considering the sensitivity of the information sent between users and virtual assistants, it explores the issues of security and privacy that arise while using such systems. The investigation covers authentication, data encryption, access control, and data privacy rules to provide a snapshot of the prevailing state protecting virtual assistants. Besides this, the framework strengthens the privacy and security of virtual assistants using blockchain technology. Through several empirical trials, it is found that the framework maintains better performance and usability, along with the provision of robust security mechanisms to safeguard user data and guarantee privacy