Efficient Discovery Protocol for Ubiquitous Communication in Wireless Environment

Nowadays, according to the advances of the wireless network technologies and also mobile computing devices the concept of ubiquitous computing environments has become a considerable research area. Ubiquitous computing environment means an environment which is saturated by elements or devices with capacities of computing and communication. So, there are a lot of ways to develop and employ applications in such environment as infrastructure, but the most effective and general one is using service advertisements, service discovery and service remote invocation. In an Ad-hoc network which its devices make an ubiquitous computing environment, every device as a server node can announce various applications as services in the environment and at the same time every device is able to listen to the network interface and be aware of surrounding services and invoke the remote services.A mechanism which is needed to recognize surrounding services is called service discovery, this mechanism also clears how to advertise services, and invoke them. Type and method of discovery procedures play the critical role in quality and efficiency of services in ubiquitous environments. Because of these properties (small and mobile) there is a serious limitation for the resources of devices specially power resource. The problem is that the most of service discovery protocols are not effective for wireless Ad- Hoc networks and ubiquities environments, efficiency in case of service quality and power consumption. In this research a new mechanism and algorithm is designed to improve current wireless service discovery protocols. Analysis of the results has shown that the designed mechanism in most of the comparative parameters such as speed of service delivery, power consumption, and coverage of the services will act much better than the current discovery protocols. The proposed solution is compared with (directory based and directory-less based) of discovery protocols in ubiquitous environment in three states: mobile nodes, mobile and static nodes, and static nodes. It can be derived that the proposed model obtains fewer messages around 52% while maintain the same rate of service discovery and false rate of service discovery. The reduction of the number of posts per request coupled with the fact that devices with greater time availability transmit more responses in the proposed model, it can be concluded that energy consumption in devices with more restrictions will be decreased

Descoberta de recursos para sistemas de escala arbitrarias

Doutoramento em InformáticaTecnologias de Computação Distribuída em larga escala tais como Cloud, Grid, Cluster e Supercomputadores HPC estão a evoluir juntamente com a emergência revolucionária de modelos de múltiplos núcleos (por exemplo: GPU, CPUs num único die, Supercomputadores em single die, Supercomputadores em chip, etc) e avanços significativos em redes e soluções de interligação. No futuro, nós de computação com milhares de núcleos podem ser ligados entre si para formar uma única unidade de computação transparente que esconde das aplicações a complexidade e a natureza distribuída desses sistemas com múltiplos núcleos. A fim de beneficiar de forma eficiente de todos os potenciais recursos nesses ambientes de computação em grande escala com múltiplos núcleos ativos, a descoberta de recursos é um elemento crucial para explorar ao máximo as capacidade de todos os recursos heterogéneos distribuídos, através do reconhecimento preciso e localização desses recursos no sistema. A descoberta eficiente e escalável de recursos ´e um desafio para tais sistemas futuros, onde os recursos e as infira-estruturas de computação e comunicação subjacentes são altamente dinâmicas, hierarquizadas e heterogéneas. Nesta tese, investigamos o problema da descoberta de recursos no que diz respeito aos requisitos gerais da escalabilidade arbitrária de ambientes de computação futuros com múltiplos núcleos ativos. A principal contribuição desta tese ´e a proposta de uma entidade de descoberta de recursos adaptativa híbrida (Hybrid Adaptive Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente e altamente escalável, construída sobre uma sobreposição hierárquica virtual baseada na auto-organizaçãoo e auto-adaptação de recursos de processamento no sistema, onde os recursos computacionais são organizados em hierarquias distribuídas de acordo com uma proposta de modelo de descriçãoo de recursos multi-camadas hierárquicas. Operacionalmente, em cada camada, que consiste numa arquitetura ponto-a-ponto de módulos que, interagindo uns com os outros, fornecem uma visão global da disponibilidade de recursos num ambiente distribuído grande, dinâmico e heterogéneo. O modelo de descoberta de recursos proposto fornece a adaptabilidade e flexibilidade para executar consultas complexas através do apoio a um conjunto de características significativas (tais como multi-dimensional, variedade e consulta agregada) apoiadas por uma correspondência exata e parcial, tanto para o conteúdo de objetos estéticos e dinâmicos. Simulações mostram que o HARD pode ser aplicado a escalas arbitrárias de dinamismo, tanto em termos de complexidade como de escala, posicionando esta proposta como uma arquitetura adequada para sistemas futuros de múltiplos núcleos. Também contribuímos com a proposta de um regime de gestão eficiente dos recursos para sistemas futuros que podem utilizar recursos distribuíos de forma eficiente e de uma forma totalmente descentralizada. Além disso, aproveitando componentes de descoberta (RR-RPs) permite que a nossa plataforma de gestão de recursos encontre e aloque dinamicamente recursos disponíeis que garantam os parâmetros de QoS pedidos.Large scale distributed computing technologies such as Cloud, Grid, Cluster and HPC supercomputers are progressing along with the revolutionary emergence of many-core designs (e.g. GPU, CPUs on single die, supercomputers on chip, etc.) and significant advances in networking and interconnect solutions. In future, computing nodes with thousands of cores may be connected together to form a single transparent computing unit which hides from applications the complexity and distributed nature of these many core systems. In order to efficiently benefit from all the potential resources in such large scale many-core-enabled computing environments, resource discovery is the vital building block to maximally exploit the capabilities of all distributed heterogeneous resources through precisely recognizing and locating those resources in the system. The efficient and scalable resource discovery is challenging for such future systems where the resources and the underlying computation and communication infrastructures are highly-dynamic, highly-hierarchical and highly-heterogeneous. In this thesis, we investigate the problem of resource discovery with respect to the general requirements of arbitrary scale future many-core-enabled computing environments. The main contribution of this thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and flexibility to perform complex querying by supporting a set of significant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The simulation shows that HARD can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a proper architecture for future many-core systems. We also contributed to propose a novel resource management scheme for future systems which efficiently can utilize distributed resources in a fully decentralized fashion. Moreover, leveraging discovery components (RR-RPs) enables our resource management platform to dynamically find and allocate available resources that guarantee the QoS parameters on demand

Enhancing road safety through accurate detection of hazardous driving behaviors with graph convolutional recurrent networks

Car accidents remain a significant public safety issue worldwide, with the majority of them attributed to driver errors stemming from inadequate driving knowledge, non-compliance with regulations, and poor driving habits. To increase road safety, several studies proposed Driving Behavior Detection (DBD) systems that can differentiate between safe and unsafe driving behavior. Many of these papers used the sensor information retrieved from the CAN (Controller Area Network) bus to construct their models. According to the existing literature, using public sensors reduces the detection model's accuracy while adding vendor-specific sensors into the data increases the accuracy. However, the earlier techniques' utility is limited by the use of non-public sensors. As a result, this paper presents a reliable DBD system based on Graph Convolutional Long Short-Term Memory networks in order to improve the detection model's precision and practical usability for public sensors. Additionally, non-public sensors were utilized to assess the model's effectiveness. The proposed model achieved an accuracy of 97.5% for public sensors and an average accuracy of 98.1% for non-public sensors, which shows that the proposed model can produce consistent and accurate results for both scenarios. The proposed DBD system deployed on Raspberry Pi at the network edge to analyze the driver's driving behavior locally. Drivers can access daily driving condition reports, sensor data, and prediction results from the DBD system through the monitoring dashboard. A voice warning from the dashboard also warns drivers of hazardous driving conditions.</p

A survey on botnets, issues, threats, methods, detection and prevention

Botnets have become increasingly common and progressively dangerous to both business and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the population has been performing corporate activities from their homes. This leads to speculation that most computer users and employees working remotely do not have proper defences against botnets, resulting in botnet infection propagating to other devices connected to the target network. Consequently, not only did botnet infection occur within the target user’s machine but also neighbouring devices. The focus of this paper is to review and investigate current state of the art and research works for both methods of infection, such as how a botnet could penetrate a system or network directly or indirectly, and standard detection strategies that had been used in the past. Furthermore, we investigate the capabilities of Artificial Intelligence (AI) to create innovative approaches for botnet detection to enable making predictions as to whether there are botnets present within a network. The paper also discusses methods that threat-actors may be used to infect target devices with botnet code. Machine learning algorithms are examined to determine how they may be used to assist AI-based detection and what advantages and disadvantages they would have to compare the most suitable algorithm businesses could use. Finally, current botnet prevention and countermeasures are discussed to determine how botnets can be prevented from corporate and domestic networks and ensure that future attacks can be prevented

Forest Terrain Identification using Semantic Segmentation on UAV Images

Beavers' habitat is known to alter the terrain, providing biodiversity in the area, and recently their lifestyle is linked to climatic changes by reducing greenhouse gases levels in the region. To analyse the impact of beavers’ habitat on the region, it is, therefore, necessary to estimate the terrain alterations caused by beaver actions. Furthermore, such terrain analysis can also play an important role in domains like wildlife ecology, deforestation, land-cover estimations, and geological mapping. Deep learning models are known to provide better estimates on automatic feature identification and classification of a terrain. However, such models require significant training data. Pre-existing terrain datasets (both real and synthetic) like CityScapes, PASCAL, UAVID, etc, are mostly concentrated on urban areas and include roads, pathways, buildings, etc. Such datasets, therefore, are unsuitable for forest terrain analysis. This paper contributes, by providing a finely labelled novel dataset of forest imagery around beavers’ habitat, captured from a high-resolution camera on an aerial drone. The dataset consists of 100 such images labelled and classified based on 9 different classes. Furthermore, a baseline is established on this dataset using state-of-the-art semantic segmentation models based on performance metrics including Intersection Over Union (IoU), Overall Accuracy (OA), and F1 score

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them

Applying the DeLone and McLean Model for Assessment of Golestan Academic System: A Case Study

Objective: In this paper, we have tried to evaluate the quality of the “Golestan” system by relying on the conceptual model of Delon and Maclean (2003). Methods: To do this, we have used the Model of Pavkovic, Gasper, and Jadrick (2021), which includes the developed form of the Delon and Maclean model along with the standard formative questionnaire, consists of 5 dimensions and 40 subscales, and organized in the continuum range of 1 to 10. The present study was the educational support staff and students of Tehran public universities who were sampled by multi-stage cluster sampling method and Shahid Beheshti, Shahed, and Allameh Tabataba'i universities were selected as samples and sample sizes of 151 and 302 were determined by Cochran formula for educational support staff and students of the mentioned universities, which finally 128 and 264 employees and students responded to the questionnaire, respectively. Results: Some important findings were obtained from this study. First, the “system performance quality” in all subscales was lower than average, and “information quality” was higher than average. Second, the components of “proportionality to the need”, “the time of registration of inputs”, “alternative access possibility”, “the confidentiality of access”, and “the accuracy of the information” were identified as most important. Third, the evaluation of students about “information quality”, “quality of system performance”, and “service quality” of the Golestan system was more desirable and positive than the staff's point of view. Fourth, only the dimension of “system performance quality” was effective on “system use”, and the rest of the dimensions namely “quality of information” and "quality of information" had no significant effect on the use of the system. Finally, the dimension of “system performance quality” had the most significant indirect effect on user satisfaction. Conclusions: The quality of system performance and its ingredients such as being adjusted to needs, reliable accessibility, optimum response time, optimum input time, error-free performance, and multiple options for getting access are the most important factors of quality for the Golestan system

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

An integrated controller for car-accessories and home/ office multimedia devices using mobile phone with security features.

Today people have to use many remote controllers (or smart cards) for different applications, such as, home/ office devices, door-access, laptop/ PC data protection and cars accessories. Besides, there is a lack of a system to integrate and smartly manage all these accessories, especially remotely by using personal mobile/ hank phone. In this invention we had followed the idea that considers a hand phone as a gateway and communication point for access and control remotely all personal devices, car accessories. This product provides an integrated user interface based on Bluetooth enabled mobile phones and PDAs to efficiently manage and control personal computers, laptops, access doors, home or office devices such as air condition; and car accessories remotely with some new features. The final achievement of this invention includes the following subjects: 1. A smart remote control and authorization mechanism for computers and laptops, accessories functions by using Bluetooth and GSM technology and mobile phone interface characterized in that:(a) A new intelligent centralized system includes the integration of all different car accessories in one system; (b) An enhanced and developed smart car technologies which means full control of the car accessories from a personal and uniform access point which is most conventional such as Mobile phones 2. A build in smart air-absorber/ air-con which uses automatically functions and also has ability to be controlled via Mobile phones or Internet connection