Education is developing very fast with the advancement of technology and the process of the smart era. One can store all educational certificates and credentials in the form of an electronic wallet or a folder. By using this electronic transformation of certificates, users can transfer the certificates from one place to another very easily. The “data island” phenomenon, central data storing, confidentiality, reduced security, and integrity are common problems of electronic data transfer. This study presents a safe sharing of digital documents which uses blockchain technology and an attributed-based cryptosystem to offer a creative solution to the abovementioned issues. The proposed scheme uses Ethereum smart contracts and achieves fine-grain access control by using attribute-based encryption. Finally, we verified our model using the test network and compared the performance with some existing state-of-arts. The results of proposed scheme generated by simulations are more feasible and effective in challenging environments.
{"title":"Secure digital documents sharing using blockchain and attribute-based cryptosystem","authors":"G. Verma, Soumen Kanrar","doi":"10.3233/mgs-221361","DOIUrl":"https://doi.org/10.3233/mgs-221361","url":null,"abstract":"Education is developing very fast with the advancement of technology and the process of the smart era. One can store all educational certificates and credentials in the form of an electronic wallet or a folder. By using this electronic transformation of certificates, users can transfer the certificates from one place to another very easily. The “data island” phenomenon, central data storing, confidentiality, reduced security, and integrity are common problems of electronic data transfer. This study presents a safe sharing of digital documents which uses blockchain technology and an attributed-based cryptosystem to offer a creative solution to the abovementioned issues. The proposed scheme uses Ethereum smart contracts and achieves fine-grain access control by using attribute-based encryption. Finally, we verified our model using the test network and compared the performance with some existing state-of-arts. The results of proposed scheme generated by simulations are more feasible and effective in challenging environments.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87481817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. Chandra Sekhar Kolli, Nihar M. Ranjan, Dharani Kumar Talapula, Vikram S. Gawali, S. Biswas
The tremendous development and rapid evolution in computing advancements has urged a lot of organizations to expand their data as well as computational needs. Such type of services offers security concepts like confidentiality, integrity, and availability. Thus, a highly secured domain is the fundamental need of cloud environments. In addition, security breaches are also growing equally in the cloud because of the sophisticated services of the cloud, which cannot be mitigated efficiently through firewall rules and packet filtering methods. In order to mitigate the malicious attacks and to detect the malicious behavior with high detection accuracy, an effective strategy named Multiverse Fractional Calculus (MFC) based hybrid deep learning approach is proposed. Here, two network classifiers namely Hierarchical Attention Network (HAN) and Random Multimodel Deep Learning (RMDL) are employed to detect the presence of malicious behavior. The network classifier is trained by exploiting proposed MFC, which is an integration of multi-verse optimizer and fractional calculus. The proposed MFC-based hybrid deep learning approach has attained superior results with utmost testing sensitivity, accuracy, and specificity of 0.949, 0.939, and 0.947.
{"title":"Multiverse fractional calculus based hybrid deep learning and fusion approach for detecting malicious behavior in cloud computing environment","authors":"Dr. Chandra Sekhar Kolli, Nihar M. Ranjan, Dharani Kumar Talapula, Vikram S. Gawali, S. Biswas","doi":"10.3233/mgs-220214","DOIUrl":"https://doi.org/10.3233/mgs-220214","url":null,"abstract":"The tremendous development and rapid evolution in computing advancements has urged a lot of organizations to expand their data as well as computational needs. Such type of services offers security concepts like confidentiality, integrity, and availability. Thus, a highly secured domain is the fundamental need of cloud environments. In addition, security breaches are also growing equally in the cloud because of the sophisticated services of the cloud, which cannot be mitigated efficiently through firewall rules and packet filtering methods. In order to mitigate the malicious attacks and to detect the malicious behavior with high detection accuracy, an effective strategy named Multiverse Fractional Calculus (MFC) based hybrid deep learning approach is proposed. Here, two network classifiers namely Hierarchical Attention Network (HAN) and Random Multimodel Deep Learning (RMDL) are employed to detect the presence of malicious behavior. The network classifier is trained by exploiting proposed MFC, which is an integration of multi-verse optimizer and fractional calculus. The proposed MFC-based hybrid deep learning approach has attained superior results with utmost testing sensitivity, accuracy, and specificity of 0.949, 0.939, and 0.947.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74733219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.
{"title":"Enhanced tolerance-based intuitionistic fuzzy rough set theory feature selection and ResNet-18 feature extraction model for arrhythmia classification","authors":"M. Rajeshwari, K. Kavitha","doi":"10.3233/mgs-220317","DOIUrl":"https://doi.org/10.3233/mgs-220317","url":null,"abstract":"Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85268896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present the application of multi-objective optimisation analytic methodologies to goal models in this research, with the intention of providing various benefits beyond the initial modelling act. Optimisation analysis can be used by modellers to evaluate goal satisfaction, evaluate high-level design alternatives, aid analysts in deciding on high-level requirements and system design, verify the sanity of a model, and improve communication and learning. Goal model analysis may be done in a variety of methods, depending on the nature of the model and the study’s goal. In our work, we use the Goal-Oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN), a new International Telecommunication Union (ITU) recommendation that offers the first standard goal-oriented language. Existing optimisation methods are geared towards maximising objective functions. On the other hand, real-world problems necessitate simultaneous optimisation of both maximising and minimising objective functions. This work explores a GRL model analysis that may accommodate the conflicting goals of various inter-dependent actors in a goal model using the Analytic Hierarchy Process (AHP). By evaluating the qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., objectives and tasks) that make up the model, we construct a multi-objective optimisation method for analysis using the GRL model. The proposed hybrid technique evaluates the contribution of alternatives to the accomplishment of top softgoals. It is then integrated with the top softgoals’ normalised relative priority values. The integration result may be utilised to assess multiple alternatives based on the requirements problem. Although the URN standard does not mandate a specific propagation algorithm, it does outline certain criteria for developing evaluation mechanisms. Case studies were used to assess the viability of the suggested approach in a simulated environment using JAVA Eclipse and IBM Cplex. The findings revealed that the proposed method can be used to analyse goals in goal models with opposing multi-objective functions.
{"title":"Goal-oriented requirement language model analysis using analytic hierarchy process","authors":"Sreenithya Sumesh, A. Krishna, R.Z. ITU-T","doi":"10.3233/mgs-220242","DOIUrl":"https://doi.org/10.3233/mgs-220242","url":null,"abstract":"We present the application of multi-objective optimisation analytic methodologies to goal models in this research, with the intention of providing various benefits beyond the initial modelling act. Optimisation analysis can be used by modellers to evaluate goal satisfaction, evaluate high-level design alternatives, aid analysts in deciding on high-level requirements and system design, verify the sanity of a model, and improve communication and learning. Goal model analysis may be done in a variety of methods, depending on the nature of the model and the study’s goal. In our work, we use the Goal-Oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN), a new International Telecommunication Union (ITU) recommendation that offers the first standard goal-oriented language. Existing optimisation methods are geared towards maximising objective functions. On the other hand, real-world problems necessitate simultaneous optimisation of both maximising and minimising objective functions. This work explores a GRL model analysis that may accommodate the conflicting goals of various inter-dependent actors in a goal model using the Analytic Hierarchy Process (AHP). By evaluating the qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., objectives and tasks) that make up the model, we construct a multi-objective optimisation method for analysis using the GRL model. The proposed hybrid technique evaluates the contribution of alternatives to the accomplishment of top softgoals. It is then integrated with the top softgoals’ normalised relative priority values. The integration result may be utilised to assess multiple alternatives based on the requirements problem. Although the URN standard does not mandate a specific propagation algorithm, it does outline certain criteria for developing evaluation mechanisms. Case studies were used to assess the viability of the suggested approach in a simulated environment using JAVA Eclipse and IBM Cplex. The findings revealed that the proposed method can be used to analyse goals in goal models with opposing multi-objective functions.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78017869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patients with chronic liver diseases typically experience lipid profile problems, and mortality from cirrhosis complicated by portal vein thrombosis (PVT) is very significant. A lipoprotein (Lp) is a bio-chemical assemblage with the main job of moving fat molecules in water that are hydrophobic. Lipoproteins are present in all eubacterial walls. Lipoproteins are of tremendous interest in the study of spirochaetes’ pathogenic mechanisms. Since spirochaete lipobox sequences are more malleable than other bacteria, it’s proven difficult to apply current prediction methods to new sequence data. The major goal is to present a Lipoprotein detection model in which correlation features, enhanced log energy entropy, raw features, and semantic similarity features are extracted. These extracted characteristics are put through a hybrid model that combines a Gated Recurrent Unit (GRU) and a Long Short-Term Memory (LSTM). Then, the outputs of GRU and LSTM are averaged to obtain the output. Here, GRU weights are optimized via the Selfish combined Henry Gas Solubility Optimization with cubic map initialization (SHGSO) model.
{"title":"Lipoprotein detection: Hybrid deep classification model with improved feature set","authors":"P. N. Kathavate, J. Amudhavel","doi":"10.3233/mgs-220329","DOIUrl":"https://doi.org/10.3233/mgs-220329","url":null,"abstract":"Patients with chronic liver diseases typically experience lipid profile problems, and mortality from cirrhosis complicated by portal vein thrombosis (PVT) is very significant. A lipoprotein (Lp) is a bio-chemical assemblage with the main job of moving fat molecules in water that are hydrophobic. Lipoproteins are present in all eubacterial walls. Lipoproteins are of tremendous interest in the study of spirochaetes’ pathogenic mechanisms. Since spirochaete lipobox sequences are more malleable than other bacteria, it’s proven difficult to apply current prediction methods to new sequence data. The major goal is to present a Lipoprotein detection model in which correlation features, enhanced log energy entropy, raw features, and semantic similarity features are extracted. These extracted characteristics are put through a hybrid model that combines a Gated Recurrent Unit (GRU) and a Long Short-Term Memory (LSTM). Then, the outputs of GRU and LSTM are averaged to obtain the output. Here, GRU weights are optimized via the Selfish combined Henry Gas Solubility Optimization with cubic map initialization (SHGSO) model.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83794346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The building extraction technology in remote sensing imagery has been a research hotspot. Building extraction in remote sensing imagery plays an important role in land planning, disaster assessment, digital city construction, etc. Although many scholars have explored many methods, it is difficult to realize high-precision automatic extraction due to the problems in high-resolution remote sensing images, such as the same object with different spectrum, the same spectrum with different object, noise shadow and ground object occlusion. Therefore, this paper proposes an urban building extraction based on information fusion-oriented deep encoder-decoder network. First, the deep encoder-decoder network is adopted to extract the shallow semantic features of building objects. Second, a polynomial kernel is used to describe the middle feature map of deep network to improve the identification ability for fuzzy features. Third, the shallow features and high-order features are fused and sent to the end of the encoder-decoder network to obtain the building segmentation results. Finally, we conduct abundant experiments on public data sets, the recall rate, accuracy rate, and F1-Score are greatly improved. The overall F1-score increases by about 4%. Compared with other state-of-the-art building extraction network structures, the proposed network is better to segment the building target from the background.
{"title":"Urban building extraction based on information fusion-oriented deep encoder-decoder network in remote sensing imagery","authors":"Cheng Zhang, Mingzhou Ma, Dan He","doi":"10.3233/mgs-220339","DOIUrl":"https://doi.org/10.3233/mgs-220339","url":null,"abstract":"The building extraction technology in remote sensing imagery has been a research hotspot. Building extraction in remote sensing imagery plays an important role in land planning, disaster assessment, digital city construction, etc. Although many scholars have explored many methods, it is difficult to realize high-precision automatic extraction due to the problems in high-resolution remote sensing images, such as the same object with different spectrum, the same spectrum with different object, noise shadow and ground object occlusion. Therefore, this paper proposes an urban building extraction based on information fusion-oriented deep encoder-decoder network. First, the deep encoder-decoder network is adopted to extract the shallow semantic features of building objects. Second, a polynomial kernel is used to describe the middle feature map of deep network to improve the identification ability for fuzzy features. Third, the shallow features and high-order features are fused and sent to the end of the encoder-decoder network to obtain the building segmentation results. Finally, we conduct abundant experiments on public data sets, the recall rate, accuracy rate, and F1-Score are greatly improved. The overall F1-score increases by about 4%. Compared with other state-of-the-art building extraction network structures, the proposed network is better to segment the building target from the background.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87979781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.
{"title":"Multi objective task scheduling based on hybrid metaheuristic algorithm for cloud environment","authors":"P. Neelakantan, N. Yadav","doi":"10.3233/mgs-220218","DOIUrl":"https://doi.org/10.3233/mgs-220218","url":null,"abstract":"Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90453813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.
{"title":"A survey on cloud computing scheduling algorithms","authors":"M. Malekimajd, Ali Safarpoor-Dehkordi","doi":"10.3233/mgs-220217","DOIUrl":"https://doi.org/10.3233/mgs-220217","url":null,"abstract":"Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73083754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modelling and analysis in software system development can be especially challenging in early requirements engineering (RE), where high-level system non-functional requirements are discovered. In the early stage, hard to measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is key to system success. Goal-oriented requirements engineering (GORE) has been successful in dealing with the issues that may arise during the analysis of requirements. While assisting in the analysis of requirements, i* goal model is the only framework available among the many GORE models, emphasising socio-technical domains such as stakeholders/actors/players, goals/objectives, dependencies and design options/alternatives. Most current approaches to goal-model analysis use quantitative methods or formal information that is hard to gather in early RE, or produce analysis results automatically over models. In real-time competitive applications, the goals of various stakeholders are conflicting in complex systems. Also, each of the system goals have various alternative design options for the systems and optimal selection of goal-oriented requirements faces several challenges in requirements-based engineering. Hence, effective decision-making frameworks are necessary to capture the real issues to achieve multi-objective optimisation of interdependent actors. To obtain an optimum strategy for interdependent actors in the i* goal model must balance the opposing goals reciprocally. To achieve this, the model needs to go beyond the analytical decision-making tools such as sensitivity analysis tasks, cost-effective analysis process, game-theoretic concepts and analytical hierarchical process. To address these requirements, this paper discusses the design of novel frameworks for an agent-based goal model analysis in requirements engineering. The objective of this paper is to provide a brief and comprehensive review of the major efforts undertaken along this line of research. In this paper we have prepared literature review of the concepts, terminology, significance and techniques of Goal oriented requirements engineering in the context of non-functional requirements analysis.
{"title":"Challenges and review of goal-oriented requirements engineering based competitive non-functional requirements analysis","authors":"Sreenithya Sumesh, A. Krishna","doi":"10.3233/mgs-220231","DOIUrl":"https://doi.org/10.3233/mgs-220231","url":null,"abstract":"Modelling and analysis in software system development can be especially challenging in early requirements engineering (RE), where high-level system non-functional requirements are discovered. In the early stage, hard to measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is key to system success. Goal-oriented requirements engineering (GORE) has been successful in dealing with the issues that may arise during the analysis of requirements. While assisting in the analysis of requirements, i* goal model is the only framework available among the many GORE models, emphasising socio-technical domains such as stakeholders/actors/players, goals/objectives, dependencies and design options/alternatives. Most current approaches to goal-model analysis use quantitative methods or formal information that is hard to gather in early RE, or produce analysis results automatically over models. In real-time competitive applications, the goals of various stakeholders are conflicting in complex systems. Also, each of the system goals have various alternative design options for the systems and optimal selection of goal-oriented requirements faces several challenges in requirements-based engineering. Hence, effective decision-making frameworks are necessary to capture the real issues to achieve multi-objective optimisation of interdependent actors. To obtain an optimum strategy for interdependent actors in the i* goal model must balance the opposing goals reciprocally. To achieve this, the model needs to go beyond the analytical decision-making tools such as sensitivity analysis tasks, cost-effective analysis process, game-theoretic concepts and analytical hierarchical process. To address these requirements, this paper discusses the design of novel frameworks for an agent-based goal model analysis in requirements engineering. The objective of this paper is to provide a brief and comprehensive review of the major efforts undertaken along this line of research. In this paper we have prepared literature review of the concepts, terminology, significance and techniques of Goal oriented requirements engineering in the context of non-functional requirements analysis.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76433090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.
{"title":"An approach for data integrity authentication and protection in fog computing","authors":"M.N. Babitha, M. Siddappa","doi":"10.3233/mgs-220210","DOIUrl":"https://doi.org/10.3233/mgs-220210","url":null,"abstract":"The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79115111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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