Computer Networks and Distributed Systems
Touraj BaniRostam; Hamid BaniRostam; Mir Mohsen Pedram; Amir Masoud Rahamni
Volume 7, Issue 3 , August 2021, , Pages 157-166
Abstract
Abstract—Several studies have been presented to solve challenges of electronic card (e-card) fraud that the two main purposes of these studies are to identify types of e-card fraud and to investigate the methods used in bank fraud detection. To achieve this purpose, one of the most common methods ...
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Abstract—Several studies have been presented to solve challenges of electronic card (e-card) fraud that the two main purposes of these studies are to identify types of e-card fraud and to investigate the methods used in bank fraud detection. To achieve this purpose, one of the most common methods of detecting fraud is to investigate suspicious changes in user behavior. Supervised learning techniques help to find anomalies by analyzing user behavioral history based on past transaction patterns in fraud detection systems. One of challenging issues in detecting fraud is to consider the change of customer behavior and the ability of fraudsters to devise new patterns of fraud, which makes unsupervised learning techniques popular for detecting unknown and new frauds. In this paper, the concepts of fraud, types of banking fraud along with their challenges, different form of fraud and banks' data research tools for early identification have been examined, then a review of the researches on fraud detection will be conducted. This paper aims to introduce fraud detection techniques and methods that have provided appropriate results in the big data environment. Finally, the fraud detection algorithms and proposed methods of related works presented in this paper, will be fully compared on a common dataset in terms of parameters such as speed of fraud detection, accuracy, and cost (hardware and network resources). Ensemble Meta-Learning can be used alone to build a stronger classifier. These techniques have been relatively successful in detecting fraud and reducing costs.
Computer Networks and Distributed Systems
Elham Bozorgzadeh; hamid barati; Ali Barati
Volume 7, Issue 3 , August 2021, , Pages 167-176
Abstract
Vehicular ad hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs) that have inherited some of this type of network's features. Due to road accidents, these networks are a promising technology to increase passengers' comfort and safety and increase road safety and provide traffic information. ...
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Vehicular ad hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs) that have inherited some of this type of network's features. Due to road accidents, these networks are a promising technology to increase passengers' comfort and safety and increase road safety and provide traffic information. In vehicular ad hoc networks, it is challenging to design an efficient routing protocol for data routing in vehicles due to rapid topology changes and frequent disconnections. Applications in these fields require efficient routing protocols. The design of a routing protocol must be done both in terms of useful information dissemination and under the information dissemination environment's actual conditions. In this paper, we overview the existing VANET routing protocols; As there are different routing protocols in VANET, we need to do detailed research on various routing protocols and their strengths/weaknesses. The routing protocols essentially concentrate on delay, packet delivery magnitude relation, information measure utilization, and plenty of alternative factors. However, there are challenges to select a routing protocol to a dynamic topology and special characteristics of VANETs. VANET is extremely advantageous because it helps in up the road safety through reducing the amount of accidents by warning drivers regarding the danger before they really face it and different facilities to comfort drivers.
Software Engineering and Information Systems
Asieh Ghanbarpour; Hassan Naderi; Soheil ZareMotlagh
Volume 6, Issue 3 , August 2020, , Pages 169-186
Abstract
Abstract—Keyword Search is known as a user-friendly alternative for structured languages to retrieve information from graph-structured data. Efficient retrieving of relevant answers to a keyword query and effective ranking of these answers according to their relevance are two main challenges in ...
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Abstract—Keyword Search is known as a user-friendly alternative for structured languages to retrieve information from graph-structured data. Efficient retrieving of relevant answers to a keyword query and effective ranking of these answers according to their relevance are two main challenges in the keyword search over graph-structured data. In this paper, a novel scoring function is proposed, which utilizes both the textual and structural features of answers in order to produce a more accurate order of answers. In addition, a query processing algorithm is developed based on information spreading technique to enumerate answers in approximate order. This algorithm is further improved by allowing a skewed development toward more promising paths and enables a more efficient processing of keyword queries. Performance evaluation through extensive experiments on a standard benchmark of three real-world datasets shows the effectiveness and efficiency of the proposed algorithms.Index Terms—Information retrieval, Database, Keyword search, Relevant answers, Information spreading.
Pattern Analysis and Intelligent Systems
Farhad Soleimanian Gharehchopogh; Berivan Rostampnah
Volume 7, Issue 3 , August 2021, , Pages 177-186
Abstract
Abstract— Clustering is one of the most popular techniques in unsupervised learning in which data is divided into different groups without any prior knowledge, and for this reason, clustering is used in various applications today. One of the most popular algorithms in the field of clustering is ...
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Abstract— Clustering is one of the most popular techniques in unsupervised learning in which data is divided into different groups without any prior knowledge, and for this reason, clustering is used in various applications today. One of the most popular algorithms in the field of clustering is the k-means clustering algorithm. The most critical weakness of k-means clustering is that it is sensitive to initial values for parameterization and may stop at local minima. Despite its many advantages, such as high speed and ease of implementation due to its dependence on the initial parameters, this algorithm is in the optimal local configuration and does not always produce the optimal answer for clustering. Therefore, this paper proposes a new model using the Bald Eagle Search (BES) Algorithm with the Sine Cosine Algorithm (SCA) for clustering. The evaluation of the proposed model is based on the number of iterations, convergence, number of generations, and execution time on 8 UCI datasets. The proposed model is compared with Flower Pollination Algorithm (FPA), Crow Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Sine-Cosine Algorithm (SCA). The results show that the proposed model has a better fit compared to other algorithms. According to the analysis, it can be claimed that the proposed model is about 10.26% superior to other algorithms and also has an extraordinary advantage over k-means.
Software Engineering and Information Systems
shahrzad Oveisi; Mohammad Nadjafi; Mohammad Ali Farsi; Ali moeini; Mahmood Shabankhah
Volume 6, Issue 3 , August 2020, , Pages 187-200
Abstract
One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software ...
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One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software risks based on the FMEA indices with respect to reduce the risk level by means of experts’ opinions. To this end, TOPSIS as one of the most applicable methods of prioritizing and ordering the significance of events has been used. Since uncertainty in the data is inevitable, the entropy principle has been applied with the help of fuzzy theory to overcome this problem to weigh the specified indices.The applicability and effectiveness of the proposed approach is validated through a real case study risk analysis of an Air/Space software system. The results show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision making of our software system that is in the early stages of software life cycle. After obtaining the events and assessing their risk using the existing method, finally, suggestions are given to reduce the risk of the event with a higher risk rating.
Computer Networks and Distributed Systems
Elham shamsinejad; Mir Mohsen Pedram; Amir Masoud Rahamni; Touraj BaniRostam
Volume 7, Issue 3 , August 2021, , Pages 187-196
Abstract
By increasing access to high amounts of data through internet-based technologies such as social networks and mobile phones and electronic devices, many companies have considered the issues of accessing large, random and fast data along with maintaining data confidentiality. Therefore, confidentiality ...
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By increasing access to high amounts of data through internet-based technologies such as social networks and mobile phones and electronic devices, many companies have considered the issues of accessing large, random and fast data along with maintaining data confidentiality. Therefore, confidentiality concerns and protection of specific data disclosure are one of the most challenging topics. In this paper, a variety of data anonymity methods, anonymity operators, the attacks that can endanger data anonymity and lead to the disclosure of sensitive data in the big data have been investigated. Also, different aspects of big data such as data sources, content format, data preparation, data processing and common data repositories will be discussed. Privacy attacks and contrastive techniques like k anonymity, neighborhood t and L diversity have been investigated and two main challenges to use k anonymity on big data will be identified, as well. Two main challenges to use k anonymity on big data will be identified. The first challenge of confidential attributes can also be as pseudo-identifier attributes, which increases the number of pseudo-identifier elements, and it may lead to the loss of great information to achieve k anonymity. The second challenge in big data is the unlimited number of data controllers are likely to lead to the disclosure of sensitive data through the independent publication of k anonymity. Then different anonymity algorithms will be presented and finally, the different parameters of time order and the consumable space of big data anonymity algorithms will be compared.
mohammad masdari; sasan Gharehpasha; ahmad jafarian
Volume 6, Issue 4 , November 2020, , Pages 201-212
Abstract
Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically ...
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Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Energy efficiency in data centers has become a hot topic in recent years as more and larger data centers have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in server consolidation. In the past few years, many approaches to virtual machine placement have been proposed, but existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines. In this paper, we proposed a new approach for placement based on Discrete Chaotic whale optimization Algorithm. First goal of our presented algorithm is reducing the energy consumption in datacenters by decreasing the number of active physical machines. Second goal is decreasing waste of resources and management of them using optimal placement of virtual machines on physical machines in cloud data centers. By using the method, the increase in migration of virtual machines to physical machines is prevented. Finally, our proposed algorithm is compared to some algorithms in this area like FF, ACO, MGGA, GSA, and FCFS.
Computer Architecture and Digital Systems
Jibril Bala; Olayemi Olaniyi; Taliha Folorunso; Tayo Arulogun
Volume 6, Issue 4 , November 2020, , Pages 213-226
Abstract
Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID ...
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Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID controllers and IMCs tuned with Genetic Algorithm (GA) and Fuzzy Logic (FL), on a poultry feeding system. The use of GA and FL for tuning of the PID and IMC parameters was done to enhance the adaptability and optimality of the controller. A comparative analysis was made to analyse closed loop performance and ascertain the most effective controller. The results showed that the GA-PID and FL-PID gave a better performance in the aspect of rise time, settling time and Integrated Absolute Error (IAE). On the other hand, the GA-IMC and FL-IMC gave better performances in the aspect of the performance overshoot. Therefore, for processes in which a faster response and lower IAE are desired, the GA-PID and FL-PID are more effective while for processes in which the major objective is to minimise the overshoot, the GA-IMC and FL-IMC are more suitable.
Pattern Analysis and Intelligent Systems
Neda Damya; Farhad Soleimanian Gharehchopogh
Volume 6, Issue 4 , November 2020, , Pages 227-238
Abstract
Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been ...
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Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.
Pattern Analysis and Intelligent Systems
Aroj Subedi; Pradip Ganesh; Sandip Mishra
Volume 6, Issue 4 , November 2020, , Pages 239-250
Abstract
Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while ...
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Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM leading to a faulty model. In this project, a simple yet efficient mechanism is used to match and reconnect the endpoints of the broken segments accurately and efficiently. The matching of the endpoints is done using the concept of minimum Euclidean distance and gradient direction while the Cubic Hermite spline interpolation technique is used to reconnect the endpoints by estimating the values using a mathematical function that minimizes overall surface curvature resulting in a smooth curve. The purpose of this work is to reconnect the broken contour lines generated during the digitization of the contour map, to help build the most appropriate digital elevation model for the corresponding contour map.
Pattern Analysis and Intelligent Systems
Mehrnaz Mirhasani; Reza Ravanmehr
Volume 6, Issue 4 , November 2020, , Pages 251-264
Abstract
The movie recommendation systems are always faced with the new movie cold start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie cold start problem. In ...
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The movie recommendation systems are always faced with the new movie cold start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie cold start problem. In this study, we propose a hybrid movie recommendation method utilizing microblogs, movie features, and sentiment lexicon to reduce the effect of data sparsity. For this purpose, first, the movie features are extracted from the Internet Movie Database (IMDB), and the average IMDB score is calculated during the 7-days opening of the movie. Second, the related tweets of the movie and the cast are retrieved by the Twitter API. Third, the polarity of tweets and the public’s feeling towards the target movie is extracted using sentiment lexicon analysis. Finally, the results of the three previous steps are integrated, and the prediction is obtained. Our results are compared with the sales volume of the target movie in 7-days opening, which is available in the Mojo Box office. In addition to the real-world benchmarking, we performed extensive experiments to demonstrate the accuracy and effectiveness of our proposed approach in comparison with the other state-of-the-art methods.
Computer Networks and Distributed Systems
behnam Kiani Kalejahi; Jala Guluzade; shabnam maharramli
Volume 6, Issue 4 , November 2020, , Pages 265-272
Abstract
AbstractBlockchain technology is the first successful Bitcoin Network. It enables the ledger to become more decentralized and secure. Since it is not limited to bitcoin and controlled by third parties by government, corporations, or banks, the technology is capturing several industries, including cryptocurrency, ...
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AbstractBlockchain technology is the first successful Bitcoin Network. It enables the ledger to become more decentralized and secure. Since it is not limited to bitcoin and controlled by third parties by government, corporations, or banks, the technology is capturing several industries, including cryptocurrency, infrastructure& hardware, financial technology, Internet & mobile and so on. Blockchain is used as a public ledger to verify all peer-to-peer system transactions and maintain traded bitcoin spending from central authorities while bitcoin has distributed transactions. Achieving high Blockchain-based performance and privacy & security are global issues that are desire to be overcome as claims show they are still significant challenges in many Blockchain applications. This paper presents an introduction to Blockchain and the process of this technology in the way of outlining Blockchain types. Also, recent advances, challenges, real economy integration, and current situations of this technology have been listed. Key Words: Blockchain, transaction, nodes, privacy, scalability, consensus, future directions
Pattern Analysis and Intelligent Systems
shahrzad Oveisi
Articles in Press, Accepted Manuscript, Available Online from 02 June 2022
Abstract
Nowadays, we are witnessing the growth of financial theft cases and thereby a high degree of financial losses concomitant with the growth of IoT technology, as well as development and utilization of this technology in banking and financial fields along with the increase in the volume of transactions. ...
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Nowadays, we are witnessing the growth of financial theft cases and thereby a high degree of financial losses concomitant with the growth of IoT technology, as well as development and utilization of this technology in banking and financial fields along with the increase in the volume of transactions. Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples and well-defined patterns or signatures with those that do not conform to the expected profiles. Various methods have been introduced to identify, detect and prevent such thefts. This paper presents a LSTM based approach for detecting fraudulent transactions. I express the fraud detection problem as a sequence classification task and employ LSTM based method to incorporate transaction sequences. Then, the results are assessed using two classifier methods, namely random forest and decision tree and Mean Square Error. In the second case, because the data are imbalanced, undersampling and oversampling techniques have been used, after which I employ LSTM neural network and results assessed with MSE. Finally, the results of the two groups were compared with confusion matrix. According to the evaluations, my proposed method with Random forest classifier gives the best results.
Pattern Analysis and Intelligent Systems
Zahra narimani; Farhad Soleimanian Gharehchopogh
Articles in Press, Accepted Manuscript, Available Online from 07 July 2022
Abstract
One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes ...
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One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes represent objects, and edges represent connections between objects. Communities are groups of nodes that have many internal connections and few intergroup connections. Although in social network research, the detection of communities has attracted much attention, most of them face functional weaknesses because the structure of the community is not clear or the characteristics of nodes in a community are not the same. Besides, many existing algorithms have complex and costly calculations. In this paper, a model based on Harris Hawk Optimization (HHO) algorithm and Opposition-based Learning (OBL) is proposed for Community Detection (CD). The proposed model uses an OBL to balance exploration and exploitation. The balance between exploration and exploitation is effective in achieving optimal solutions. The evaluation of the proposed model is performed on four different datasets based on modularity criteria and NMI (Normalized Mutual Information). The results show that the proposed model has higher modularity, NMI, and generalizability compared to other methods.
Pattern Analysis and Intelligent Systems
Ehsan Soleimani; Kamal Mirzaie
Articles in Press, Accepted Manuscript, Available Online from 30 July 2022
Abstract
With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of ...
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With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of CD is to discover subcommunities within the original networks. Identifying these groups in social networks has many applications in discovering new groups, and extracting common points between users. The main challenge in CD is to find the relationships between the nodes and form clusters in them. In particular, the use of CD techniques makes it possible to identify similar nodes that have common features. Because user data does not belong to a specific category and there is a lot of data in the community and a similar relationship should be established between them based on the type of data. In this paper, a new method for CD using an Adaptive Genetic Algorithm (AGA) is proposed. The evaluation of the proposed model was performed on the Enron Email suite using Normalized Mutual Information (NMI), Variation Information (VI) and Cross Common Fraction (CCF) criteria. The results show that with increasing the number of generations, the accuracy of the proposed model increases and also the rate of crossover and mutation are very effective in the accuracy of detection. The accuracy of NMI, VI and CCF criteria with 800 iterations is 82.17, 72.95 and 75.16, respectively.
Computer Networks and Distributed Systems
Saeed Banaeian Far; Maryam Rajabzadeh Asaar; Afrooz Haghbin
Articles in Press, Accepted Manuscript, Available Online from 19 August 2022
Abstract
Privacy is a matter of considerable public concern in internet-based communications. In other words, it is sensed more with the growth of internet-based communications and the increasing number of connected users. This paper focuses on the blockchain-based retail markets. It presents a framework that ...
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Privacy is a matter of considerable public concern in internet-based communications. In other words, it is sensed more with the growth of internet-based communications and the increasing number of connected users. This paper focuses on the blockchain-based retail markets. It presents a framework that allows retail market owners to change their retail markets' pseudonyms to a new ones and transfer their accumulated reputations to their new pseudonyms in an untraceable way so that no one can find a link between the transferred reputation and the previous retail market. The main idea is to assign the retail market reputation to a Non-Fungible Token (NFT) and anonymously transfer the NFT ownership to another owner. The presented method is named blockchain-based reputation transfer (BB-RT) and is designed using the two concepts of NFT and Signature of Knowledge (SoK) for providing an unknown and unlinkable transfer. The BB-RT framework provides security for retail markets, including unlinkable reputation transfer for removing retail markets' adversaries. Each retail market owner can prove its retail market's previous reputation in its new retail market. It is believed that the BB-RT framework can be applied in Distributed Autonomous Organization (DAO)-based markets (businesses) since markets' reputations are transferred without the help of a central manager. Finally, the mentioned BB-RT framework properties are analyzed.
Software Engineering and Information Systems
Narges Akhound; Sahar Adabi; Ali Rezaee; Amir masoud Rahmani
Articles in Press, Accepted Manuscript, Available Online from 27 September 2022
Abstract
The advent of the Internet of Things (IoT) technology has made it possible for different devices to be widely connected to the Internet and interact. It has led to the production of large amounts of heterogeneous data. On the other hand, cloud computing is a convenient and efficient processing model ...
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The advent of the Internet of Things (IoT) technology has made it possible for different devices to be widely connected to the Internet and interact. It has led to the production of large amounts of heterogeneous data. On the other hand, cloud computing is a convenient and efficient processing model for storing and processing data. Still, the increasing demand for real-time and delay-sensitive applications is increasing day by day. Due to network bandwidth limitations, these problems cannot be solved using cloud computing alone. A fog layer located between the IoT devices and the cloud computing layer has been proposed to overcome the problem of resource constraints in mobile devices. delay-sensitive applications run that require more volume and power resources. In this paper, end-to-end architecture for integrating IoT, fog, and cloud layers into a large-scale dispatched application is proposed to support high availability to make efficient use of fog-cloud resources and achieve the appropriate quality of service (QoS) in terms of delay and failure probability criteria. The mentioned architecture consists of three hierarchal layers: IoT devices, fog nodes, and cloud data centers. Depending on the processing power of each layer's resources, user requests may be executed on the same layer or sent to a higher layer. Then, quality characteristics such as availability, performance, and interoperability for the proposed architecture are evaluated by the ATAM scenario-based method. The basis of architectural evaluation and analysis in this method is the study of the requirements and the quality characteristics of the system architecture.
Computer Networks and Distributed Systems
Mohsen Raji; Saeed Keshavarzi; Mehran Farvardin
Articles in Press, Accepted Manuscript, Available Online from 07 November 2022
Abstract
A Wireless Sensor Network (WSN) is consisted of thousands of sensor nodes to monitor environmental conditions. Energy plays a major role in these networks because nodes are worked with batteries and are sometimes placed in an inconsistent environment where their battery cannot be charged or replaced. ...
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A Wireless Sensor Network (WSN) is consisted of thousands of sensor nodes to monitor environmental conditions. Energy plays a major role in these networks because nodes are worked with batteries and are sometimes placed in an inconsistent environment where their battery cannot be charged or replaced. In this paper, a new dynamic clustering method is presented for WSNs with moving targets. In this method, target tracking is carried out using the speed and direction of target movement as well as selection of the optimal cluster-head (CH) using the artificial bee colony (ABC) meta-heuristic algorithm with fuzzy TOPSIS. In the proposed CH selection method, three criteria are considered; i.e. the remaining energy of the node, the cost of selecting each node as the CH, and the risk of hardware failure of each node. Since each of these criteria should be assigned with a suitable weight, ABC optimization algorithm is used to find the best weights for ranking decisions. After specifying the objective function in the ABC algorithm and weighting the criteria, fuzzy TOPSIS algorithm is used and each of the nodes is fuzzified in each of the criteria that are normalized and weighted. The results show that in addition to maintaining the accuracy of tracking the moving target, the proposed method achieves a 5.4% improvement in network energy consumption in comparison with a state-of-the-art method called EEAC.
Computer Networks and Distributed Systems
Sara Mohammadi; Parvaneh Asghari; Amir Masoud Rahmani
Articles in Press, Accepted Manuscript, Available Online from 02 December 2022
Abstract
As a new technology, cloud computing is a key part of making systems more efficient and better and improving the Internet of Things. One of the significant challenges in fog computing is trust management, taking into account the processing, storage, and network constraints of fog devices. This study ...
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As a new technology, cloud computing is a key part of making systems more efficient and better and improving the Internet of Things. One of the significant challenges in fog computing is trust management, taking into account the processing, storage, and network constraints of fog devices. This study suggests that a multi-objective imperialist competitive optimization algorithm be used to increase trust and decrease response time in fog environments. After formulating trust, delay, and accuracy, the multi-objective imperialist competitive optimization algorithm is developed and evaluated for fog server selection. Evaluations show that the proposed method is more efficient and works well in terms of accuracy, delay, and trust than other algorithms.