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Papers by IJICTR Journal
International Journal of Information and Communication Technology Research, 2024
With the start of the Coronavirus epidemic, remote education systems were widely accepted in all ... more With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.
International Journal of Information and Communication Technology Research, 2024
In recent years, the performance of deep neural networks in improving the image retrieval process... more In recent years, the performance of deep neural networks in improving the image retrieval process has been remarkable. Utilizing deep neural networks; however, leads to poor results in retrieving images with missing regions. The operators’ dysfunctions, who consider the relationship between the image pixels, statistically extract incomplete information from an image, which in turn reduces the number of image features and or leads to features' inaccurate identification. An attempt has been made to eliminate the problem of missing image information through image inpainting techniques; therefore, a content-based image retrieval method is proposed for images with missing regions. In this method, through image inpainting the crucial missing information is reconstructed. The image dataset is being queried to find similar samples. For this purpose, a two-stage inpainting framework based on encoder-decoder is used in the image retrieval system. Also, the features of each image are extracted from the integration and concatenating of content and semantic features. Through using handcraft features such as color and texture image content information is extracted from the Resnet-50 deep neural network. Finally, similar images are retrieved based on the minimum Euclidean distance. The performance of the image retrieval model with missing regions is evaluated with the average precision criterion on the Paris 6K datasets. The best retrieval results are 60.11%, 50.14%, and 42.43% for retrieving the top one, five, and ten samples after reconstructing the image with the most missing regions with a destruction frequency of 6 Hz, respectively.
International Journal of Information and Communication Technology Research, 2024
—Web application (app) exploration is a crucial part of various analysis and testing techniques. ... more —Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the current methods are not able to properly explore the state space of web apps. As a result, techniques must be developed to guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement Learning (RL) is a machine learning method in which the best way to do a task is learned through trial and error, with the help of positive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural networks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. However, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration strategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the stateof-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the superiority of Deep RL over traditional RL methods in web app exploration.
International Journal of Information and Communication Technology Research, 2024
As an emerging technology that combines both digital and physical realms, access to information t... more As an emerging technology that combines both digital and physical realms, access to information technology has expanded (IoT) the Internet of Things. The Internet of Things, as it becomes more pervasive, will overshadow human life as much as possible. Some of the major challenges associated with the development of this phenomenon have been the issue of security, which is needed in all its layers and even specifically in individual layers. According to the structure and applications of the Internet of Things, as well as the threats and challenges in cyberspace, we first examine security needs and then, by examining some methods of securing the Internet of Things, we propose a method according to the approaches discussed.
International Journal of Information and Communication Technology Research, 2024
The optimal routing will only be possible if the link weights are calculated correctly. Also, the... more The optimal routing will only be possible if the link weights are calculated correctly. Also, the weight of the links should be different based on the type of destination nodes and the traffic flow of each path in the network and should be updated in real time. In this study, using multi-criteria decision-making techniques, the criteria related to each type of destination node and the importance of that criterion according to the type of the destination node, are considered. This research is applied based on the purpose. Library studies have been used to extract criteria related to each type of the destination node. Also, the field method has been used to calculate the weight of each criterion. The simulation of the proposed method showed that this method has reduced the average weight of links in routing by about half compared to the method that considered only the "distance" criterion.
International Journal of Information and Communication Technology Research, 2024
—In intelligent transport system (ITS) networks, it is very useful to reduce the transmission ene... more —In intelligent transport system (ITS) networks, it is very useful to reduce the transmission energy consumption. Device-to-device (D2D) communication is a key technology that can improve the performance of the ITS networks. In this paper, we investigate a D2D-enabled vehicular network and study a power allocation strategy to maximize the spectral efficiency of the network. Also, for safety-critical vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) applications, it is presented a reliable connection during the V2V and V2I links. The performance analysis of the investigated network shows that the studied method can maximize the total spectral efficiency of the network subject to satisfy some constraints when the channel state information (CSI) is perfectly known or not.
International Journal of Information and Communication Technology Research , 2024
—Hospital information system, as an integrated system consists of complex, diverse, and heterogen... more —Hospital information system, as an integrated system consists of complex, diverse, and heterogeneous information systems, which supports all clinical, administrative, and financial activities of patients; therefore, it faces interoperability problems, which is a critical feature of data sharing and integration. In this regard, a reference architecture will be presented for the integration of different systems in the hospital which comprises system, information and software architecture. This architecture emphasizes interoperability with eight layers of user interface and application, services, data collection and storage, integration, external application systems, communication and information infrastructure, management, and security/privacy, based on service orientation. It will be evaluated by ATAM method, based on ten scenarios. It shows the presented architecture will satisfy all functional and non-functional requirements such as interoperability between different information systems, reduction of information redundancy and development cost and time, scalability and accessibility.
International Journal of Information and Communication Technology Research , 2024
—Sentiment analysis of online doctor reviews helps patients to better evaluate and select the rel... more —Sentiment analysis of online doctor reviews helps patients to better evaluate and select the related doctors based on the previous patients' satisfaction. Although some studies are addressing this problem in the English language, only one preliminary study has been reported for the Persian language. In this study, we propose a new evolutionary deep model for sentiment analysis of Persian online doctor reviews. The proposed method utilizes both Persian reviews and their English translations as inputs of two separate deep models. Then, the outputs of the two models are combined in a single vector which is used for deciding the sentiment polarity of the review in the last layer of the proposed deep model. To improve the performance of the system, we propose an evolutionary approach to optimize the hyperparameters of the proposed deep model. We also compared three evolutionary algorithms, namely, Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Gray Wolf Optimization (GWO) algorithm, for this purpose. We evaluated the proposed model in two phases; In the first phase, we compared four deep models, namely, long shortterm memory (LSTM), convolutional neural network (CNN), a hybrid of LSTM and CNN, and a bidirectional LSTM (BiLSTM) model with four traditional machine learning models including Naïve Bayes (NB), decision tree (DT), support vector machines (SVM), and random forest (RF). The results showed that the BiLSTM and CNN models outperform other methods, significantly. In the second phase, we compared the optimized version of two proposed bi-lingual models in which either two BiLSTM or two CNN models were used in parallel for processing Persian and English reviews. The results indicated that the optimization of the CNN using ACO and the optimization of BiLSTM using a genetic algorithm can achieve the best performance among other combinations of two deep models and three optimization algorithms. In the current study, we proposed two deep models for bi-lingual sentiment analysis of online Persian doctor reviews. Moreover, we optimized the proposed models using ACO, genetic algorithm, and gray wolf optimization methods. The results indicated that the proposed bi-lingual model outperforms a similar model using only Persian or English reviews. Also, optimizing the parameter of proposed deep models using ACO or genetic algorithms improved the performance of the models.
International Journal of Information and Communication Technology Research , 2024
Software evolution and continuous changes make maintenance difficult, reducing the quality of sof... more Software evolution and continuous changes make maintenance difficult, reducing the quality of software structure and architecture. To cope with this challenge, re-modularization is used to promote the modular structure of software system by the re-grouping of software elements. In this paper, the proposed method recognizes various dependencies in terms of an objective function unlike what has been stated in some other methods. In this method, a search-based many-objective fitness function is proposed to formulate re-modularization as an optimization problem. The results of the proposed method have been compared to the effects of four other methods based on MQ and NED. The results show the proposed method improved re-modularization remarkably compared to others in terms of both MQ and NED criteria especially for smaller software. Therefore, the proposed method can be effective in redefining real-world applications.
International Journal of Information and Communication Technology Research , 2024
—In order to provide access control on encrypted data, Attribute-based encryption (ABE) defines e... more —In order to provide access control on encrypted data, Attribute-based encryption (ABE) defines each user using a set of attributes. Fuzzy identity-based encryption (FIBE) is a variant of ABE that allows for a threshold access structure for users. To address the potential threat posed by future quantum computers, this paper presents a postquantum fuzzy IBE scheme based on lattices. However, current lattice-based ABE schemes face challenges related to computational complexity and the length of ciphertext and keys. This paper aims to improve the performance of an existing fuzzy IBE scheme by reducing key length and computational complexity during the encryption phase. While negative attributes are not utilized in our scheme, we prove its security under the learning with error (LWE) hard problem assumption in the selective security model. These improvements have significant implications for the field of ABE.
International Journal of Information and Communication Technology Research , 2024
Utilizing IoT technologies for monitoring large-scale smart facilities such as power, water and g... more Utilizing IoT technologies for monitoring large-scale smart facilities such as power, water and gas distribution networks has been the subject of many studies recently. The aim is to detect anomalous events in the network due to elements’ failure, bad designs, attacks or abuses of the network and alert the network operators in a timely manner. As the centralized cloud-based approaches are impractical in time-critical and real-time anomaly detection applications due to 1) high sensor-to-cloud transmission latency 2) high communication cost and 3) high energy consumption at the sensor nodes, the distributed anomaly detection methods based on Deep Neural Networks (DNN) have been applied in past studies vastly. In these methods, in order to detect anomalies in real-time, copies of the anomaly detection model are placed at the sensor nodes (rather than placing one at the cloud node) reducing the sensor-to-cloud transmissions significantly. Nevertheless, new normal samples collected at the sensor nodes still need to be transmitted to the cloud node at predefined intervals to re-train the distributed anomaly detection DNNs. In order to minimize these sensor-to-cloud transmissions during the retraining process, in this paper, two well-known lossless coding algorithms: Huffman Coding and Arithmetic Coding were studied and it was observed that the Huffman and Arithmetic Coding were able to reduce the transmission traffic up to 50% and 75% respectively using two IoT benchmark datasets of pipeline measurements. Besides, the Huffman Coding shown to be computationally feasible on resource limited sensors and resulted in up to 10% saving in energy consumption on each sensor resulting in longer network longevity. Moreover, the experimental results showed that the auto-encoder DNN could outperform the one-class SVM in the iterative distributed anomaly detection method.
International Journal of Information and Communication Technology Research , 2024
Due to the high-power consumption and complexity of fully digital baseband precoding, its impleme... more Due to the high-power consumption and complexity of fully digital baseband precoding, its implementation in massive millimeter-wave multiple-input multiple-output (MIMO) systems is not cost-efficient and practical; for this reason, hybrid precoding has attracted a lot of attention in recent years. Most hybrid precoding techniques concentrate on the fully-connected structure, although they require lots of phase shifters, which is high energy-consuming. On the contrary, the partially-connected structure has low power consumption, nevertheless, suffers from a severe decrease in spectral efficiency (SE). To enhance SE, this paper proposed a dynamic hybrid precoding structure where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF) chains. To determine the digital precoder and the states of switch, a novel alternating minimization algorithm is proposed, which leverages closed-form solutions at each iteration to efficiently converge to an optimal solution. Furthermore, the phase shifter matrix is optimized through an iterative solution. The simulation results show that in terms of SE, the proposed algorithm with a dynamic structure achieves higher performance than the partial structure. Also, since the proposed structure reduces the number of phase shifters, it can guarantee better energy efficiency (EE) than the fully connected structure
International Journal of Information and Communication Technology Research , 2023
As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog comp... more As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. Software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.
International Journal of Information and Communication Technology Research , 2023
— Reading traditional meters is always time-consuming and expensive. Using smart meters solves mo... more — Reading traditional meters is always time-consuming and expensive. Using smart meters solves most of the problems existing in the traditional meter network. Smart meters are an advanced form of traditional electro-mechanical devices that can measure energy consumption in real-time and communicate through one or more wired or wireless networks. These devices can communicate from long distances and get changed, making them an easy target for attacks. This paper studies the security mechanisms in smart meters networks and suggests some security solutions in such networks. We have developed software for managing the information of smart meters and controlling them remotely. In this paper, we present the implemented security mechanisms in the developed smart meter management software. The proposed solutions for enhancing the security of this software include implementing the authentication system, enabling user management, and defining different access levels to prevent users from connecting without proper authentication and access control in the developed software. Moreover, hashing the password with a random salt technique is implemented for securing the database. Furthermore, we have secured the software platform to prevent web attacks such as Clickjacking and CSRF attacks.
International Journal of Information and Communication Technology Research , 2023
Facial expression recognition using deep learning methods has been one of the active research fie... more Facial expression recognition using deep learning methods has been one of the active research fields in the last decade. However, most of the previous works have focused on the implementation of the model in the laboratory environment, and few researchers have addressed the real-world challenges of facial expression recognition systems. One of the challenges of implementing the face recognition system in the real environment (e.g. webcam or robot) is to create a balance between accuracy and speed of model recognition. Because, increasing the complexity of the neural network model leads to an increase in the accuracy of the model, but due to the increase in the size of the model, the recognition speed of the model decreases. Therefore, in this paper, we propose a model to recognize the seven main emotions (Happiness, sadness, anger, surprise, fear, disgust and natural), which can create a balance between accuracy and recognition speed. Specifically, the proposed model has three main components. First, in the feature extraction component, the features of the input images are extracted using a combination of normal and separable convolutional networks. Second, in the feature integration component, the extracted features are integrated using the attention mechanism. Finally, the merged features are used as the input of the multi-layer perceptron neural network to recognize the input facial expression. Our proposed approach has been evaluated using three public datasets and images received via webcam
International Journal of Information and Communication Technology Research , 2023
New emerging industries, such as vertical markets, need diverse networking requirements that the ... more New emerging industries, such as vertical markets, need diverse networking requirements that the next generation mobile networks have to support effectively. Network slicing is the basic solution to meet the diverse requirements of various services over a common network infrastructure. Different network slicing architectures have been proposed; however, to the best of our knowledge, there is no unified architecture to cover all aspects of technology. In this paper, we propose a complete network slicing architecture based on 3GPP 5G system that addresses a unified end-to-end approach. We show how this architecture can create and operate various slices in the core and radio access sections using SDN controllers, virtualization, NFV MANO, and 3GPP management functions. We do compare our proposed one with some famous network-slicing architectures. The Comparison Results show that our proposed architecture is complete and covers all the aspects of network slicing. It uses NFV management and orchestration capabilities and SDN controllers while being compatible with 3GPP Service-Based architecture. It also provides life cycle management of network slices in both creation and operation phases in both core and radio access domains of the 5G network. In addition, we have included a functional RAN layer split to lay the corresponding layers in centralized or distributed units according to the requirements of each eMBB, mMTC, or URLLC slice.
International Journal of Information and Communication Technology Research , 2023
In this paper, a probe for measuring radio-frequency electric fields in the environment is design... more In this paper, a probe for measuring radio-frequency electric fields in the environment is designed and presented. These electric fields consist of multi cellular technology (2G, 3G and 4G), including four bands: GSM900, GSM1800, 3G2100 and LTE2600. This device, called the MCT electric probe, is realized by three orthogonal antennas, in connection to frequency multiplexer circuits and detectors. The proposed antenna is a 3-D multi-branch monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotopically. The proposed multiplexer can separate the received signals into four narrowband and has the ability to remove out-of-band signals. The detector is able to convert the fields received from the antenna and multiplexer sections to suitable DC voltages for amplifying and digital processing. Finally, the designed MCT electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields.
International Journal of Information and Communication Technology Research , 2023
An 8 port narrowband Radial Power Combiner (RPC) with excellent combining efficiency is presented... more An 8 port narrowband Radial Power Combiner (RPC) with excellent combining efficiency is presented. A novel useful criteria for optimal design of the RPC is developed and the design is studied based on it. Also to provide a reliable, tolerant and cost effective mechanical design, the mechanical structure of the combiner is simplified for fabrication and assembly. The designed RPC has standard waveguide ports at inputs, so it can be used in standard high power applications without any adaptors. The physical structure simplicity guarantees the product reliability for industrial high power applications. The optimized RPC design is fabricated and the measurement results are presented. The back-to-back measurement setup using customized through lines and two identical combiners have helped to take into account other efficiency degrading phenomena like port junctions’ discontinuity. It is shown that simulated power combining efficiency of 99% and measured power combining efficiency of 97.7 % is achieved.
International Journal of Information and Communication Technology Research , 2023
A novel pre-training method is proposed to improve deep-neural-networks (DNN) and long-short-term... more A novel pre-training method is proposed to improve deep-neural-networks (DNN) and long-short-term-memory (LSTM) performance, and reduce the local minimum problem for speech enhancement. We propose initializing the last layer weights of DNN and LSTM by Non-Negative-Matrix-Factorization (NMF) basis transposed values instead of random weights. Due to its ability to extract speech features even in presence of non-stationary noises, NMF is faster and more successful than previous pre-training methods for network convergence. Using NMF basis matrix in the first layer along with another pre-training method is also proposed. To achieve better results, we further propose training individual models for each noise type based on a noise classification strategy. The evaluation of the proposed method on TIMIT data shows that it outperforms the baselines significantly in terms of perceptual-evaluation-of-speech-quality (PESQ) and other objective measures. Our method outperforms the baselines in terms of PESQ up to 0.17, with an improvement percentage of 3.4%.
International Journal of Information and Communication Technology Research , 2023
The Internet of Things (IoT) is one of the new technologies that has received significant attenti... more The Internet of Things (IoT) is one of the new technologies that has received significant attention in the last decade and has been used in all aspects of life (including agriculture, medicine, business, industry, education, etc.).
One of the most important applications of the Internet of Things is in the process of student education, which has been discussed a lot in recent years and has led to a significant progress in the education industry, however, there are still many challenges in this field, which includes managing classrooms, conference halls, teaching in offices, public schools, and e-learning websites. Therefore, it is necessary to create a new framework to improve teaching methods. On the other hand, providing educational materials for students according to their level of understanding and learning goals can have a significant effect on improving the quality of teaching and learning.
In this research, a framework for improving the quality of teaching to students in the context of the Internet of Things has been presented. In this framework, the mental and psychological condition and stress conditions of the students are investigated and provided to the teacher in real-time, so that they can make the right decision with sufficient information based on the conditions of each student in the classroom and use the information to adapt their teaching methods and tests. This framework, with the help of the Internet of Things, provides information about each student and mental and psychological elements (such as heart rate, body temperature, etc.) as well as factors affecting the classroom environment (including the amount of noise pollution, ambient temperature, light, etc.), collects and uses fuzzy logic to place students in different categories with and without stress conditions. Kuja simulator and MATLAB software are used for simulation. The results of the simulation show that this framework can detect the students' stress and by adapting the test and teaching conditions to the mental and psychological state of the students, it can indirectly improve the educational quality for students.
International Journal of Information and Communication Technology Research, 2024
With the start of the Coronavirus epidemic, remote education systems were widely accepted in all ... more With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.
International Journal of Information and Communication Technology Research, 2024
In recent years, the performance of deep neural networks in improving the image retrieval process... more In recent years, the performance of deep neural networks in improving the image retrieval process has been remarkable. Utilizing deep neural networks; however, leads to poor results in retrieving images with missing regions. The operators’ dysfunctions, who consider the relationship between the image pixels, statistically extract incomplete information from an image, which in turn reduces the number of image features and or leads to features' inaccurate identification. An attempt has been made to eliminate the problem of missing image information through image inpainting techniques; therefore, a content-based image retrieval method is proposed for images with missing regions. In this method, through image inpainting the crucial missing information is reconstructed. The image dataset is being queried to find similar samples. For this purpose, a two-stage inpainting framework based on encoder-decoder is used in the image retrieval system. Also, the features of each image are extracted from the integration and concatenating of content and semantic features. Through using handcraft features such as color and texture image content information is extracted from the Resnet-50 deep neural network. Finally, similar images are retrieved based on the minimum Euclidean distance. The performance of the image retrieval model with missing regions is evaluated with the average precision criterion on the Paris 6K datasets. The best retrieval results are 60.11%, 50.14%, and 42.43% for retrieving the top one, five, and ten samples after reconstructing the image with the most missing regions with a destruction frequency of 6 Hz, respectively.
International Journal of Information and Communication Technology Research, 2024
—Web application (app) exploration is a crucial part of various analysis and testing techniques. ... more —Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the current methods are not able to properly explore the state space of web apps. As a result, techniques must be developed to guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement Learning (RL) is a machine learning method in which the best way to do a task is learned through trial and error, with the help of positive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural networks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. However, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration strategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the stateof-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the superiority of Deep RL over traditional RL methods in web app exploration.
International Journal of Information and Communication Technology Research, 2024
As an emerging technology that combines both digital and physical realms, access to information t... more As an emerging technology that combines both digital and physical realms, access to information technology has expanded (IoT) the Internet of Things. The Internet of Things, as it becomes more pervasive, will overshadow human life as much as possible. Some of the major challenges associated with the development of this phenomenon have been the issue of security, which is needed in all its layers and even specifically in individual layers. According to the structure and applications of the Internet of Things, as well as the threats and challenges in cyberspace, we first examine security needs and then, by examining some methods of securing the Internet of Things, we propose a method according to the approaches discussed.
International Journal of Information and Communication Technology Research, 2024
The optimal routing will only be possible if the link weights are calculated correctly. Also, the... more The optimal routing will only be possible if the link weights are calculated correctly. Also, the weight of the links should be different based on the type of destination nodes and the traffic flow of each path in the network and should be updated in real time. In this study, using multi-criteria decision-making techniques, the criteria related to each type of destination node and the importance of that criterion according to the type of the destination node, are considered. This research is applied based on the purpose. Library studies have been used to extract criteria related to each type of the destination node. Also, the field method has been used to calculate the weight of each criterion. The simulation of the proposed method showed that this method has reduced the average weight of links in routing by about half compared to the method that considered only the "distance" criterion.
International Journal of Information and Communication Technology Research, 2024
—In intelligent transport system (ITS) networks, it is very useful to reduce the transmission ene... more —In intelligent transport system (ITS) networks, it is very useful to reduce the transmission energy consumption. Device-to-device (D2D) communication is a key technology that can improve the performance of the ITS networks. In this paper, we investigate a D2D-enabled vehicular network and study a power allocation strategy to maximize the spectral efficiency of the network. Also, for safety-critical vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) applications, it is presented a reliable connection during the V2V and V2I links. The performance analysis of the investigated network shows that the studied method can maximize the total spectral efficiency of the network subject to satisfy some constraints when the channel state information (CSI) is perfectly known or not.
International Journal of Information and Communication Technology Research , 2024
—Hospital information system, as an integrated system consists of complex, diverse, and heterogen... more —Hospital information system, as an integrated system consists of complex, diverse, and heterogeneous information systems, which supports all clinical, administrative, and financial activities of patients; therefore, it faces interoperability problems, which is a critical feature of data sharing and integration. In this regard, a reference architecture will be presented for the integration of different systems in the hospital which comprises system, information and software architecture. This architecture emphasizes interoperability with eight layers of user interface and application, services, data collection and storage, integration, external application systems, communication and information infrastructure, management, and security/privacy, based on service orientation. It will be evaluated by ATAM method, based on ten scenarios. It shows the presented architecture will satisfy all functional and non-functional requirements such as interoperability between different information systems, reduction of information redundancy and development cost and time, scalability and accessibility.
International Journal of Information and Communication Technology Research , 2024
—Sentiment analysis of online doctor reviews helps patients to better evaluate and select the rel... more —Sentiment analysis of online doctor reviews helps patients to better evaluate and select the related doctors based on the previous patients' satisfaction. Although some studies are addressing this problem in the English language, only one preliminary study has been reported for the Persian language. In this study, we propose a new evolutionary deep model for sentiment analysis of Persian online doctor reviews. The proposed method utilizes both Persian reviews and their English translations as inputs of two separate deep models. Then, the outputs of the two models are combined in a single vector which is used for deciding the sentiment polarity of the review in the last layer of the proposed deep model. To improve the performance of the system, we propose an evolutionary approach to optimize the hyperparameters of the proposed deep model. We also compared three evolutionary algorithms, namely, Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Gray Wolf Optimization (GWO) algorithm, for this purpose. We evaluated the proposed model in two phases; In the first phase, we compared four deep models, namely, long shortterm memory (LSTM), convolutional neural network (CNN), a hybrid of LSTM and CNN, and a bidirectional LSTM (BiLSTM) model with four traditional machine learning models including Naïve Bayes (NB), decision tree (DT), support vector machines (SVM), and random forest (RF). The results showed that the BiLSTM and CNN models outperform other methods, significantly. In the second phase, we compared the optimized version of two proposed bi-lingual models in which either two BiLSTM or two CNN models were used in parallel for processing Persian and English reviews. The results indicated that the optimization of the CNN using ACO and the optimization of BiLSTM using a genetic algorithm can achieve the best performance among other combinations of two deep models and three optimization algorithms. In the current study, we proposed two deep models for bi-lingual sentiment analysis of online Persian doctor reviews. Moreover, we optimized the proposed models using ACO, genetic algorithm, and gray wolf optimization methods. The results indicated that the proposed bi-lingual model outperforms a similar model using only Persian or English reviews. Also, optimizing the parameter of proposed deep models using ACO or genetic algorithms improved the performance of the models.
International Journal of Information and Communication Technology Research , 2024
Software evolution and continuous changes make maintenance difficult, reducing the quality of sof... more Software evolution and continuous changes make maintenance difficult, reducing the quality of software structure and architecture. To cope with this challenge, re-modularization is used to promote the modular structure of software system by the re-grouping of software elements. In this paper, the proposed method recognizes various dependencies in terms of an objective function unlike what has been stated in some other methods. In this method, a search-based many-objective fitness function is proposed to formulate re-modularization as an optimization problem. The results of the proposed method have been compared to the effects of four other methods based on MQ and NED. The results show the proposed method improved re-modularization remarkably compared to others in terms of both MQ and NED criteria especially for smaller software. Therefore, the proposed method can be effective in redefining real-world applications.
International Journal of Information and Communication Technology Research , 2024
—In order to provide access control on encrypted data, Attribute-based encryption (ABE) defines e... more —In order to provide access control on encrypted data, Attribute-based encryption (ABE) defines each user using a set of attributes. Fuzzy identity-based encryption (FIBE) is a variant of ABE that allows for a threshold access structure for users. To address the potential threat posed by future quantum computers, this paper presents a postquantum fuzzy IBE scheme based on lattices. However, current lattice-based ABE schemes face challenges related to computational complexity and the length of ciphertext and keys. This paper aims to improve the performance of an existing fuzzy IBE scheme by reducing key length and computational complexity during the encryption phase. While negative attributes are not utilized in our scheme, we prove its security under the learning with error (LWE) hard problem assumption in the selective security model. These improvements have significant implications for the field of ABE.
International Journal of Information and Communication Technology Research , 2024
Utilizing IoT technologies for monitoring large-scale smart facilities such as power, water and g... more Utilizing IoT technologies for monitoring large-scale smart facilities such as power, water and gas distribution networks has been the subject of many studies recently. The aim is to detect anomalous events in the network due to elements’ failure, bad designs, attacks or abuses of the network and alert the network operators in a timely manner. As the centralized cloud-based approaches are impractical in time-critical and real-time anomaly detection applications due to 1) high sensor-to-cloud transmission latency 2) high communication cost and 3) high energy consumption at the sensor nodes, the distributed anomaly detection methods based on Deep Neural Networks (DNN) have been applied in past studies vastly. In these methods, in order to detect anomalies in real-time, copies of the anomaly detection model are placed at the sensor nodes (rather than placing one at the cloud node) reducing the sensor-to-cloud transmissions significantly. Nevertheless, new normal samples collected at the sensor nodes still need to be transmitted to the cloud node at predefined intervals to re-train the distributed anomaly detection DNNs. In order to minimize these sensor-to-cloud transmissions during the retraining process, in this paper, two well-known lossless coding algorithms: Huffman Coding and Arithmetic Coding were studied and it was observed that the Huffman and Arithmetic Coding were able to reduce the transmission traffic up to 50% and 75% respectively using two IoT benchmark datasets of pipeline measurements. Besides, the Huffman Coding shown to be computationally feasible on resource limited sensors and resulted in up to 10% saving in energy consumption on each sensor resulting in longer network longevity. Moreover, the experimental results showed that the auto-encoder DNN could outperform the one-class SVM in the iterative distributed anomaly detection method.
International Journal of Information and Communication Technology Research , 2024
Due to the high-power consumption and complexity of fully digital baseband precoding, its impleme... more Due to the high-power consumption and complexity of fully digital baseband precoding, its implementation in massive millimeter-wave multiple-input multiple-output (MIMO) systems is not cost-efficient and practical; for this reason, hybrid precoding has attracted a lot of attention in recent years. Most hybrid precoding techniques concentrate on the fully-connected structure, although they require lots of phase shifters, which is high energy-consuming. On the contrary, the partially-connected structure has low power consumption, nevertheless, suffers from a severe decrease in spectral efficiency (SE). To enhance SE, this paper proposed a dynamic hybrid precoding structure where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF) chains. To determine the digital precoder and the states of switch, a novel alternating minimization algorithm is proposed, which leverages closed-form solutions at each iteration to efficiently converge to an optimal solution. Furthermore, the phase shifter matrix is optimized through an iterative solution. The simulation results show that in terms of SE, the proposed algorithm with a dynamic structure achieves higher performance than the partial structure. Also, since the proposed structure reduces the number of phase shifters, it can guarantee better energy efficiency (EE) than the fully connected structure
International Journal of Information and Communication Technology Research , 2023
As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog comp... more As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. Software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.
International Journal of Information and Communication Technology Research , 2023
— Reading traditional meters is always time-consuming and expensive. Using smart meters solves mo... more — Reading traditional meters is always time-consuming and expensive. Using smart meters solves most of the problems existing in the traditional meter network. Smart meters are an advanced form of traditional electro-mechanical devices that can measure energy consumption in real-time and communicate through one or more wired or wireless networks. These devices can communicate from long distances and get changed, making them an easy target for attacks. This paper studies the security mechanisms in smart meters networks and suggests some security solutions in such networks. We have developed software for managing the information of smart meters and controlling them remotely. In this paper, we present the implemented security mechanisms in the developed smart meter management software. The proposed solutions for enhancing the security of this software include implementing the authentication system, enabling user management, and defining different access levels to prevent users from connecting without proper authentication and access control in the developed software. Moreover, hashing the password with a random salt technique is implemented for securing the database. Furthermore, we have secured the software platform to prevent web attacks such as Clickjacking and CSRF attacks.
International Journal of Information and Communication Technology Research , 2023
Facial expression recognition using deep learning methods has been one of the active research fie... more Facial expression recognition using deep learning methods has been one of the active research fields in the last decade. However, most of the previous works have focused on the implementation of the model in the laboratory environment, and few researchers have addressed the real-world challenges of facial expression recognition systems. One of the challenges of implementing the face recognition system in the real environment (e.g. webcam or robot) is to create a balance between accuracy and speed of model recognition. Because, increasing the complexity of the neural network model leads to an increase in the accuracy of the model, but due to the increase in the size of the model, the recognition speed of the model decreases. Therefore, in this paper, we propose a model to recognize the seven main emotions (Happiness, sadness, anger, surprise, fear, disgust and natural), which can create a balance between accuracy and recognition speed. Specifically, the proposed model has three main components. First, in the feature extraction component, the features of the input images are extracted using a combination of normal and separable convolutional networks. Second, in the feature integration component, the extracted features are integrated using the attention mechanism. Finally, the merged features are used as the input of the multi-layer perceptron neural network to recognize the input facial expression. Our proposed approach has been evaluated using three public datasets and images received via webcam
International Journal of Information and Communication Technology Research , 2023
New emerging industries, such as vertical markets, need diverse networking requirements that the ... more New emerging industries, such as vertical markets, need diverse networking requirements that the next generation mobile networks have to support effectively. Network slicing is the basic solution to meet the diverse requirements of various services over a common network infrastructure. Different network slicing architectures have been proposed; however, to the best of our knowledge, there is no unified architecture to cover all aspects of technology. In this paper, we propose a complete network slicing architecture based on 3GPP 5G system that addresses a unified end-to-end approach. We show how this architecture can create and operate various slices in the core and radio access sections using SDN controllers, virtualization, NFV MANO, and 3GPP management functions. We do compare our proposed one with some famous network-slicing architectures. The Comparison Results show that our proposed architecture is complete and covers all the aspects of network slicing. It uses NFV management and orchestration capabilities and SDN controllers while being compatible with 3GPP Service-Based architecture. It also provides life cycle management of network slices in both creation and operation phases in both core and radio access domains of the 5G network. In addition, we have included a functional RAN layer split to lay the corresponding layers in centralized or distributed units according to the requirements of each eMBB, mMTC, or URLLC slice.
International Journal of Information and Communication Technology Research , 2023
In this paper, a probe for measuring radio-frequency electric fields in the environment is design... more In this paper, a probe for measuring radio-frequency electric fields in the environment is designed and presented. These electric fields consist of multi cellular technology (2G, 3G and 4G), including four bands: GSM900, GSM1800, 3G2100 and LTE2600. This device, called the MCT electric probe, is realized by three orthogonal antennas, in connection to frequency multiplexer circuits and detectors. The proposed antenna is a 3-D multi-branch monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotopically. The proposed multiplexer can separate the received signals into four narrowband and has the ability to remove out-of-band signals. The detector is able to convert the fields received from the antenna and multiplexer sections to suitable DC voltages for amplifying and digital processing. Finally, the designed MCT electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields.
International Journal of Information and Communication Technology Research , 2023
An 8 port narrowband Radial Power Combiner (RPC) with excellent combining efficiency is presented... more An 8 port narrowband Radial Power Combiner (RPC) with excellent combining efficiency is presented. A novel useful criteria for optimal design of the RPC is developed and the design is studied based on it. Also to provide a reliable, tolerant and cost effective mechanical design, the mechanical structure of the combiner is simplified for fabrication and assembly. The designed RPC has standard waveguide ports at inputs, so it can be used in standard high power applications without any adaptors. The physical structure simplicity guarantees the product reliability for industrial high power applications. The optimized RPC design is fabricated and the measurement results are presented. The back-to-back measurement setup using customized through lines and two identical combiners have helped to take into account other efficiency degrading phenomena like port junctions’ discontinuity. It is shown that simulated power combining efficiency of 99% and measured power combining efficiency of 97.7 % is achieved.
International Journal of Information and Communication Technology Research , 2023
A novel pre-training method is proposed to improve deep-neural-networks (DNN) and long-short-term... more A novel pre-training method is proposed to improve deep-neural-networks (DNN) and long-short-term-memory (LSTM) performance, and reduce the local minimum problem for speech enhancement. We propose initializing the last layer weights of DNN and LSTM by Non-Negative-Matrix-Factorization (NMF) basis transposed values instead of random weights. Due to its ability to extract speech features even in presence of non-stationary noises, NMF is faster and more successful than previous pre-training methods for network convergence. Using NMF basis matrix in the first layer along with another pre-training method is also proposed. To achieve better results, we further propose training individual models for each noise type based on a noise classification strategy. The evaluation of the proposed method on TIMIT data shows that it outperforms the baselines significantly in terms of perceptual-evaluation-of-speech-quality (PESQ) and other objective measures. Our method outperforms the baselines in terms of PESQ up to 0.17, with an improvement percentage of 3.4%.
International Journal of Information and Communication Technology Research , 2023
The Internet of Things (IoT) is one of the new technologies that has received significant attenti... more The Internet of Things (IoT) is one of the new technologies that has received significant attention in the last decade and has been used in all aspects of life (including agriculture, medicine, business, industry, education, etc.).
One of the most important applications of the Internet of Things is in the process of student education, which has been discussed a lot in recent years and has led to a significant progress in the education industry, however, there are still many challenges in this field, which includes managing classrooms, conference halls, teaching in offices, public schools, and e-learning websites. Therefore, it is necessary to create a new framework to improve teaching methods. On the other hand, providing educational materials for students according to their level of understanding and learning goals can have a significant effect on improving the quality of teaching and learning.
In this research, a framework for improving the quality of teaching to students in the context of the Internet of Things has been presented. In this framework, the mental and psychological condition and stress conditions of the students are investigated and provided to the teacher in real-time, so that they can make the right decision with sufficient information based on the conditions of each student in the classroom and use the information to adapt their teaching methods and tests. This framework, with the help of the Internet of Things, provides information about each student and mental and psychological elements (such as heart rate, body temperature, etc.) as well as factors affecting the classroom environment (including the amount of noise pollution, ambient temperature, light, etc.), collects and uses fuzzy logic to place students in different categories with and without stress conditions. Kuja simulator and MATLAB software are used for simulation. The results of the simulation show that this framework can detect the students' stress and by adapting the test and teaching conditions to the mental and psychological state of the students, it can indirectly improve the educational quality for students.