International Journal Information and Communication Technology Research
Volume:12 Issue: 1, Winter 2020

Reliable detection of life under rubble and collapsed buildings after disasters like earthquake or air raid is the most important issue in life-detection process. In this paper, the performance of microwave life-detection system (MLDS) based on a continuous wave (CW) radar is analyzed from different aspects such as penetration depth, sensitivity, and total harmonic distortion (THD) of the output signal. A novel quadrature receiver as an appropriate architecture for the MLDS, and harmonic radar system as an alternative structure are proposed in order to resolve the well-known null point issue and improve the sensitivity of the system. Results show that by using these structures in the MLDS, the null points can be completely removed and hence the chance of detecting a trapped victim under the rubble can be improved considerably. Moreover, by using the harmonic structure, the received power in some distances away from the MLDS can be improved by 3 dB compared to that of the conventional systems. By examining different frequencies, 1.15 GHz (L-band) is found to be the most appropriate carrier frequency because of deeper penetration of about 5 meters in the rubble and 7 percent improved output signal THD compared to the previously designed X-band radars for the MLDS.

Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Smart home is considered one of the rather prominent applications of the Internet of Things (IoT), integrating high-levels of efficiency, home security, energy & cost saving to everyone’s life. In spite of all the benefits this technology provides, privacy and security are highly concerning issues that require more considerations. IoT-A reference architecture was established with the purpose of evaluating current sources and protocols, ensuring the compliance of things and protocols, and providing a comprehensive solution for different applications of IoT. This study was performed with the purpose of providing a general framework for improving security at all levels of design, implementation, and application of equipment and protocols using the IoT-A reference architecture by addressing the challenge of security in the Internet of Things and smart homes. This paper employs the term Security Framework to refer to a method for applying all technologies, procedures, software, and other components to provide security in smart homes. This research seeks to outline all the reference architecture's vulnerabilities and threats, following which an improved model for the reference architecture is proposed to meet all security requirements. Considering the theoretical evaluations performed in this study, the proposed framework, which was created by adding two components of threat and vulnerability management and field management while making some alterations to the licensing component, satisfies to an acceptable level the security requirements of the smart home and enhances the privacy of the IoT-based smart home.

Identifying the roots of a worm and reconstructing its spread path are among essential concerns in digital forensics. This knowledge assist the prosecutor in understanding how the attack happened in the network and how security protections were breached. Evaluating methods proposed for this purpose is problematic due to the lack of suitable datasets containing both worm traffic and normal traffic. In this paper, we investigate various approaches of generating such datasets and propose a technique to generate suitable datasets for these evaluations.  ReaSE is a tool for creating realistic simulation environments, which considers three aspects, i.e., topology generation, normal traffic generation, and attack traffic generation. We modify ReaSE to make it suitable for generating these datasets. We also generate various datasets for Code Red I, Code Red II, SQL Slammer and modified version of them in different scenarios and make them accessible to the public.

Disseminating information through the World Wide Web as the most popular medium has resulted in creating a huge number of web pages and so growing the dimension of the web. In this era of big data, an efficient website ranking to satisfy the web user requirements in different areas such as marketing and E-commerce is a major challenge in the current Internet. In this context, the role of ranking algorithms as a tool to provide services such as measuring the website visibility and comparing the website position to the competitors is crucial. In this paper, we propose an architecture for web domain ranking which includes processing capability required for handling Big Data available on the web. The proposed architecture presents a new method for web domain ranking that is independent of the link structure of the web graph. The proposed method provides web domain ranking based on the number of unique visitors, the number of user sessions, and session duration.

The blogosphere is an effective communication platform where users publish and exchange their opinions. By analyzing user behavior, current and future trends of a community can be discovered. The proposed model for processing the social data of users first extracts related sentiments of weblog comments. An improved PSO algorithm is then employed to detect the trend of users in the TRDT (TRend DeTection) phase. By the discovery of trends at a reasonable time and appropriate precision, this model predicts future trends of the blogosphere using the Q-learning algorithm in the TRPT (TRend PredicTion) phase. Given the ever-increasing processing requirements and a huge volume of data, our approach provides a distributed processing/storage platform for TRDT and TRPT phases. The precision and performance of the proposed model in the TRDT phase are measured by the Chi-squared standard test. Moreover, the evaluation of the TRPT phase shows the comparable precision of the proposed approach with real-world scenarios such as the Netflix predictive system.

In the e-learning environment, there are various learners with varying learning characteristics, including prior knowledge, experience, motivation, and learning objective, and each learner is responsible for their own learning. In such environments, there would not be an effective and efficient learning, unless adaptive approaches are considered. Thus, the ultimate goal of adaptive learning is delivering courses, programs, and educational resources tailored to the learning characteristics of individual learner. The most important step in adaptive learning is to identify and select appropriate indicator based on which adapt learning would be performed. Researchers have selected a variety of indicators in their studies, and due to the fact that learning style model is one of the most significant indicators in recognizing individual differences in the learning process in order to adapt to the e-learning environment, in this study, "Kolb’s learning style model" was considered as the selected indicator. However, given the fact that there is uncertainty in determining this indicator, it is very complex, thus it cannot accurately described and defined. In this research, fuzzy sets theory was used to model the uncertainty and inherent ambiguity in the learning style model by creating a set of rules which was able to increase the precision of identifying dimensions of the learning style. To achieve this, a fuzzy system utilizing learners’ network behaviors in the environment to identifying and modeling their learning style was designed. In this system, the precision of the measurement in identifying individuals’ learning style compared to the results of the questionnaire that was previously completed by learners is 89.07%, showing that this method has increased the precision compared to other methods.