Pub Date : 2026-01-01DOI: 10.1016/j.ijin.2025.11.006
Hanwen Wang , Sheng Lu , Yanxin Jiang , Ying Shi , Chaoran Song , Guoqiang Liu
Distribution networks constitute the terminal stage of the electric power system, delivering electricity from transmission grids to end users. Owing to their wide geographic dispersion and the outdoor installation of most equipment, these networks are highly exposed to extreme weather events that may damage facilities and interrupt power supply services. Ensuring timely fault repair and service restoration is therefore essential for maintaining system reliability. However, large numbers of faults often occur simultaneously and may further deteriorate if not repaired promptly, reducing the overall efficiency of restoration activities. Moreover, effective recovery requires the coordinated use of multiple resources, such as repair crews, vehicles, and spare parts, whose limited availability, time constraints, and operational dependencies create additional challenges. To overcome these issues, a collaborative framework for rapid fault repair and service restoration in distribution networks, called Coradin, is proposed. It is composed of three layers, including the data integration layer responsible for data collection, the scheduling optimization layer responsible for strategy formulation and optimization, and the repair execution layer responsible for repair and restoration. Extensive experiments on the IEEE 33-bus and 123-bus systems verify the framework’s effectiveness in improving restoration efficiency and service reliability.
{"title":"A collaborative framework for rapid fault repair and service restoration in distribution networks","authors":"Hanwen Wang , Sheng Lu , Yanxin Jiang , Ying Shi , Chaoran Song , Guoqiang Liu","doi":"10.1016/j.ijin.2025.11.006","DOIUrl":"10.1016/j.ijin.2025.11.006","url":null,"abstract":"<div><div>Distribution networks constitute the terminal stage of the electric power system, delivering electricity from transmission grids to end users. Owing to their wide geographic dispersion and the outdoor installation of most equipment, these networks are highly exposed to extreme weather events that may damage facilities and interrupt power supply services. Ensuring timely fault repair and service restoration is therefore essential for maintaining system reliability. However, large numbers of faults often occur simultaneously and may further deteriorate if not repaired promptly, reducing the overall efficiency of restoration activities. Moreover, effective recovery requires the coordinated use of multiple resources, such as repair crews, vehicles, and spare parts, whose limited availability, time constraints, and operational dependencies create additional challenges. To overcome these issues, a <u>co</u>llaborative framework for <u>ra</u>pid fault repair and service restoration in <u>di</u>stribution <u>n</u>etworks, called Coradin, is proposed. It is composed of three layers, including the data integration layer responsible for data collection, the scheduling optimization layer responsible for strategy formulation and optimization, and the repair execution layer responsible for repair and restoration. Extensive experiments on the IEEE 33-bus and 123-bus systems verify the framework’s effectiveness in improving restoration efficiency and service reliability.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"7 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993612","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}
Pub Date : 2026-01-01DOI: 10.1016/j.ijin.2025.11.005
Li Yu , Jiao Jian , Jin-Ping Shi , Hong-Sheng Cao
Smart contracts have been widely used on blockchain to automate financial and business transactions. While they are susceptible to attacks because of their involvement in operations like asset transfers. Researchers have suggested various methods to address vulnerabilities in these contracts and improve their reliability. Nevertheless, when the contract code is excessively lengthy, redundant contextual information may adversely affect repair performance. To address this problem, we propose a localizable machine learning model to repair long contracts. Firstly, we extract a data collection that includes code structure information from the abstract syntax tree of the smart contract source code. Then we leverage a pre-trained BERT model to generate code embeddings and use BiLSTM for training the vulnerability localization model. Finally, the vulnerable code lines in the contract are localized based on statistical thresholding of the model’s output values. On this basis, we construct the corresponding code extraction algorithms to generate code repair fragments and use machine learning techniques to repair contracts. We compare our method with existing approaches using public datasets. The results demonstrate a significant improvement in performance over direct repair methods, effectively addressing the challenges associated with the repair of long-code smart contracts.
{"title":"Enhancing blockchain network security: A targetable machine learning model for effective vulnerability repair","authors":"Li Yu , Jiao Jian , Jin-Ping Shi , Hong-Sheng Cao","doi":"10.1016/j.ijin.2025.11.005","DOIUrl":"10.1016/j.ijin.2025.11.005","url":null,"abstract":"<div><div>Smart contracts have been widely used on blockchain to automate financial and business transactions. While they are susceptible to attacks because of their involvement in operations like asset transfers. Researchers have suggested various methods to address vulnerabilities in these contracts and improve their reliability. Nevertheless, when the contract code is excessively lengthy, redundant contextual information may adversely affect repair performance. To address this problem, we propose a localizable machine learning model to repair long contracts. Firstly, we extract a data collection that includes code structure information from the abstract syntax tree of the smart contract source code. Then we leverage a pre-trained BERT model to generate code embeddings and use BiLSTM for training the vulnerability localization model. Finally, the vulnerable code lines in the contract are localized based on statistical thresholding of the model’s output values. On this basis, we construct the corresponding code extraction algorithms to generate code repair fragments and use machine learning techniques to repair contracts. We compare our method with existing approaches using public datasets. The results demonstrate a significant improvement in performance over direct repair methods, effectively addressing the challenges associated with the repair of long-code smart contracts.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"7 ","pages":"Pages 10-17"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026163","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.10.002
Chuanfeng Lin, Bo Hong, Yuwei Xie
Traditional intrusion detection systems (IDS) face challenges in complex and heterogeneous network environments, particularly in terms of accuracy, adaptability, and performance. This study presents an advanced IDS integrating the TensorFlow framework with heuristic optimization algorithms, including genetic and crow search algorithms, to address these challenges. The proposed system employs deep learning for feature extraction and classification while optimizing detection paths to enhance performance. Experimental results demonstrate that the system achieves a detection accuracy of 96.7 %, representing a 15 % improvement over traditional methods, with a corresponding 20 % increase in processing speed. The false positive and negative rates are measured at 2.3 % and 1.4 %, respectively. Quantitative analysis shows this work provides measurable improvements in intrusion detection for heterogeneous networks.
{"title":"Design and research of multi-heterogeneous network security intrusion detection system based on TensorFlow framework","authors":"Chuanfeng Lin, Bo Hong, Yuwei Xie","doi":"10.1016/j.ijin.2025.10.002","DOIUrl":"10.1016/j.ijin.2025.10.002","url":null,"abstract":"<div><div>Traditional intrusion detection systems (IDS) face challenges in complex and heterogeneous network environments, particularly in terms of accuracy, adaptability, and performance. This study presents an advanced IDS integrating the TensorFlow framework with heuristic optimization algorithms, including genetic and crow search algorithms, to address these challenges. The proposed system employs deep learning for feature extraction and classification while optimizing detection paths to enhance performance. Experimental results demonstrate that the system achieves a detection accuracy of 96.7 %, representing a 15 % improvement over traditional methods, with a corresponding 20 % increase in processing speed. The false positive and negative rates are measured at 2.3 % and 1.4 %, respectively. Quantitative analysis shows this work provides measurable improvements in intrusion detection for heterogeneous networks.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 305-313"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839252","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}
Indoor usage of smartphones and electronic devices can be a source of information to detect the indoor location, which is known as localization that can be applied in large human health services and workplaces for example. Though the Global Positioning System (GPS) provides many effective location services using satellite signals, indoor localization is not included. Therefore, several technologies have been used for indoor localization, including Wireless Fidelity (Wi-Fi), Bluetooth Low Energy (BLE), and Received Signal Strength Indicator (RSSI), it has resulted in the proposal of Machine Learning (ML)-based indoor localization methodologies. Unlike the RSSI that indicates how well your device can hear a signal in a Wi-Fi network, this paper proposes indoor localization prediction using ML techniques based on Wi-Fi RSSI fingerprinting methodologies, encompassing data preprocessing, such as Data Cleansing (DC), Future Tuning (FT), and Feature Selection (FS). The proposed ML prediction models for indoor localization classifiers investigation in this paper are Support Vector Machine (SVM), K Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Linear Regression (LR). Moreover, a comprehensive performance comparison for the proposed prediction models is performed in this paper using nine datasets with different areas in a total of 31,470 records. The results show that KNN achieved the best performance for all parameters, making it the most recommended classifier for RSSI fingerprinting schemes.
{"title":"Machine learning in indoor localization prediction using Received Signal Strength Indicator and Wi-Fi network","authors":"Hani Attar , Walaa Saber Ismail , Mohamed Hafez , Shaimaa Bahaa , Mohanad A. Deif , M. Khosravi , Howida Youssry","doi":"10.1016/j.ijin.2025.10.001","DOIUrl":"10.1016/j.ijin.2025.10.001","url":null,"abstract":"<div><div>Indoor usage of smartphones and electronic devices can be a source of information to detect the indoor location, which is known as localization that can be applied in large human health services and workplaces for example. Though the Global Positioning System (GPS) provides many effective location services using satellite signals, indoor localization is not included. Therefore, several technologies have been used for indoor localization, including Wireless Fidelity (Wi-Fi), Bluetooth Low Energy (BLE), and Received Signal Strength Indicator (RSSI), it has resulted in the proposal of Machine Learning (ML)-based indoor localization methodologies. Unlike the RSSI that indicates how well your device can hear a signal in a Wi-Fi network, this paper proposes indoor localization prediction using ML techniques based on Wi-Fi RSSI fingerprinting methodologies, encompassing data preprocessing, such as Data Cleansing (DC), Future Tuning (FT), and Feature Selection (FS). The proposed ML prediction models for indoor localization classifiers investigation in this paper are Support Vector Machine (SVM), K Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Linear Regression (LR). Moreover, a comprehensive performance comparison for the proposed prediction models is performed in this paper using nine datasets with different areas in a total of 31,470 records. The results show that KNN achieved the best performance for all parameters, making it the most recommended classifier for RSSI fingerprinting schemes.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 233-243"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465328","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.07.003
Jinxiang Sha , Jintao Wu , Mingliang Wang , Yonglin Pu , Sheng Lu , Muhammad Bilal
Mobile Edge Computing (MEC) enhances service quality by decentralizing computational resources to network edges, thereby reducing latency and improving Quality of Service (QoS). However, the spatial distribution of edge servers critically impacts network transmission efficiency, while heterogeneous user perceptions of QoS metrics frequently lead to suboptimal Quality of Experience (QoE). Current research on Edge Server Placement (ESP) predominantly focuses on localized optimization of QoS metrics, yet fails to adequately incorporate systematic QoE modeling and coordinated optimization frameworks, leading to significant discrepancies between actual user experience and satisfaction with resource allocation. To address this gap, this study establishes a formalized QoE-aware Edge Server Placement (EESP) framework by rigorously characterizing the interdependence between QoE and QoS. We first prove the NP-completeness of the EESP problem through computational complexity analysis. Subsequently, we develop an Integer Linear Programming-based exact solver (EESP-O) for small-scale scenarios and propose an Enhanced Genetic Algorithm (EESP-EGA) for large-scale deployments. The EESP-EGA integrates adaptive crossover probability mechanisms and elite retention strategies to achieve near-optimal solutions for complex real-world configurations. Experimental evaluations conducted on a broad range of real-world datasets demonstrate that the proposed method outperforms several existing representative approaches in terms of QoE.
{"title":"QoE-aware edge server placement in mobile edge computing using an enhanced genetic algorithm","authors":"Jinxiang Sha , Jintao Wu , Mingliang Wang , Yonglin Pu , Sheng Lu , Muhammad Bilal","doi":"10.1016/j.ijin.2025.07.003","DOIUrl":"10.1016/j.ijin.2025.07.003","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) enhances service quality by decentralizing computational resources to network edges, thereby reducing latency and improving Quality of Service (QoS). However, the spatial distribution of edge servers critically impacts network transmission efficiency, while heterogeneous user perceptions of QoS metrics frequently lead to suboptimal Quality of Experience (QoE). Current research on Edge Server Placement (ESP) predominantly focuses on localized optimization of QoS metrics, yet fails to adequately incorporate systematic QoE modeling and coordinated optimization frameworks, leading to significant discrepancies between actual user experience and satisfaction with resource allocation. To address this gap, this study establishes a formalized QoE-aware Edge Server Placement (EESP) framework by rigorously characterizing the interdependence between QoE and QoS. We first prove the NP-completeness of the EESP problem through computational complexity analysis. Subsequently, we develop an Integer Linear Programming-based exact solver (EESP-O) for small-scale scenarios and propose an Enhanced Genetic Algorithm (EESP-EGA) for large-scale deployments. The EESP-EGA integrates adaptive crossover probability mechanisms and elite retention strategies to achieve near-optimal solutions for complex real-world configurations. Experimental evaluations conducted on a broad range of real-world datasets demonstrate that the proposed method outperforms several existing representative approaches in terms of QoE.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 65-78"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694327","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.02.002
Longxin Lin , Huaibin Qin , Quan Qi , Rui Gu , Pengxiang Zuo , Yongqiang Cheng
The aim of Knowledge Graph Embedding (KGE) is to acquire low-dimensional representations of entities and relationships for the purpose of predicting new valid triples, thereby enhancing the functionality of intelligent networks that rely on accurate data representation. In recommendation systems, for example, the model can enhance personalized suggestions by better understanding user-item relationships, especially when the relationships are hierarchical, such as in the case of user preferences across different product categories. Existing KGE models mostly learn embeddings in Euclidean space, which perform well in high-dimensional settings. However, in low-dimensional scenarios, these models struggle to accurately capture the hierarchical information of relationships in knowledge graphs (KG), a limitation that can adversely affect the performance of intelligent network systems where structured knowledge is critical for decision making and operational efficiency. Recently, the MuRP model was proposed, introducing the use of hyperbolic space for KG embedding. Using the properties of hyperbolic space, where the space near the center is small and the space away from the center is large, the MuRP model achieves effective KG embedding even in low-dimensional training conditions, making it particularly suitable for dynamic environments typical of intelligent networks. Therefore, this paper proposes a method that utilizes the characteristics of hyperbolic geometry to create an embedding model in hyperbolic space, combining translation and multi-dimensional rotation geometric transformations. This model accurately represents various relationship patterns in knowledge graphs, including symmetry, asymmetry, inversion, composition, hierarchy, and multiplicity, which are essential for enabling robust interactions in intelligent network frameworks. Experimental results demonstrate that the proposed model generally outperforms Euclidean space embedding models under low-dimensional training conditions and performs comparably to other hyperbolic KGE models. In experiments using the WN18RR dataset, the Hits@10 metric improved by 0.3% compared to the baseline model, and in experiments using the FB15k-237 dataset, the Hits@3 metric improved by 0.1% compared to the baseline model, validating the reliability of the proposed model and its potential contribution to advancing intelligent network applications.
{"title":"Multi-relation-pattern knowledge graph embeddings for link prediction in hyperbolic space","authors":"Longxin Lin , Huaibin Qin , Quan Qi , Rui Gu , Pengxiang Zuo , Yongqiang Cheng","doi":"10.1016/j.ijin.2025.02.002","DOIUrl":"10.1016/j.ijin.2025.02.002","url":null,"abstract":"<div><div>The aim of Knowledge Graph Embedding (KGE) is to acquire low-dimensional representations of entities and relationships for the purpose of predicting new valid triples, thereby enhancing the functionality of intelligent networks that rely on accurate data representation. In recommendation systems, for example, the model can enhance personalized suggestions by better understanding user-item relationships, especially when the relationships are hierarchical, such as in the case of user preferences across different product categories. Existing KGE models mostly learn embeddings in Euclidean space, which perform well in high-dimensional settings. However, in low-dimensional scenarios, these models struggle to accurately capture the hierarchical information of relationships in knowledge graphs (KG), a limitation that can adversely affect the performance of intelligent network systems where structured knowledge is critical for decision making and operational efficiency. Recently, the MuRP model was proposed, introducing the use of hyperbolic space for KG embedding. Using the properties of hyperbolic space, where the space near the center is small and the space away from the center is large, the MuRP model achieves effective KG embedding even in low-dimensional training conditions, making it particularly suitable for dynamic environments typical of intelligent networks. Therefore, this paper proposes a method that utilizes the characteristics of hyperbolic geometry to create an embedding model in hyperbolic space, combining translation and multi-dimensional rotation geometric transformations. This model accurately represents various relationship patterns in knowledge graphs, including symmetry, asymmetry, inversion, composition, hierarchy, and multiplicity, which are essential for enabling robust interactions in intelligent network frameworks. Experimental results demonstrate that the proposed model generally outperforms Euclidean space embedding models under low-dimensional training conditions and performs comparably to other hyperbolic KGE models. In experiments using the WN18RR dataset, the Hits@10 metric improved by 0.3% compared to the baseline model, and in experiments using the FB15k-237 dataset, the Hits@3 metric improved by 0.1% compared to the baseline model, validating the reliability of the proposed model and its potential contribution to advancing intelligent network applications.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 57-64"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071842","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.09.002
Hongzi Li , Guifen Zhang , Qinchun Su , Lina Ge
Federated self-supervised representation learning combines federated learning with self-supervised mechanisms to learn general representations from distributed unlabeled data, effectively reducing reliance on labeled data. However, under data heterogeneity, existing methods primarily focus on aligning local and global models in the parameter space, often overlooking the issue of knowledge forgetting in the global model caused by incompatibility in local representation spaces. This limits the quality of representations and overall model performance. To address this challenge, we propose FedGRF, a global representation fine-tuning method for federated self-supervised representation learning. FedGRF maintains a generator on the server to produce pseudo-data, which is then used to drive global representation fine-tuning and mitigate the forgetting of local representation knowledge by the global model. By mining hard samples arising from the fusion of local representations and employing a controllable fine-tuning mechanism, FedGRF effectively promotes the transfer of local representation knowledge to the global model. Extensive experimental results demonstrate that FedGRF achieves competitive performance improvements over existing methods.
{"title":"Global representation fine-tuning for federated self-supervised representation learning","authors":"Hongzi Li , Guifen Zhang , Qinchun Su , Lina Ge","doi":"10.1016/j.ijin.2025.09.002","DOIUrl":"10.1016/j.ijin.2025.09.002","url":null,"abstract":"<div><div>Federated self-supervised representation learning combines federated learning with self-supervised mechanisms to learn general representations from distributed unlabeled data, effectively reducing reliance on labeled data. However, under data heterogeneity, existing methods primarily focus on aligning local and global models in the parameter space, often overlooking the issue of knowledge forgetting in the global model caused by incompatibility in local representation spaces. This limits the quality of representations and overall model performance. To address this challenge, we propose FedGRF, a global representation fine-tuning method for federated self-supervised representation learning. FedGRF maintains a generator on the server to produce pseudo-data, which is then used to drive global representation fine-tuning and mitigate the forgetting of local representation knowledge by the global model. By mining hard samples arising from the fusion of local representations and employing a controllable fine-tuning mechanism, FedGRF effectively promotes the transfer of local representation knowledge to the global model. Extensive experimental results demonstrate that FedGRF achieves competitive performance improvements over existing methods.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 224-232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361915","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}
With the rapid expansion of digital asset trading, the contradiction between data sharing and privacy protection has increasingly become a significant challenge in the Internet environment. To address this issue, this paper proposes a secure multi-party computation scheme based on blockchain technology. Firstly, in response to the risk of data leakage in distributed storage scenarios, a threshold-based encryption algorithm is designed, utilizing a distributed key protection mechanism to effectively prevent single-point failures and data breaches. Secondly, a smart contract system is developed: the ERC721 contract is used to confirm the ownership of data assets, the ERC20 contract facilitates the transfer of usage rights, and the threshold decryption contract ensures secure multi-party computation and compliant incentive distribution. The collaboration of these three types of contracts enables comprehensive on-chain management of data assets, covering the entire process from ownership confirmation and circulation to compliant usage. In addition, this paper integrates non-interactive zero-knowledge proofs into the multi-party interaction process, allowing public verification of data consistency and computational validity on the blockchain. Finally, experiments are conducted to evaluate the impact of computation latency, communication overhead, and encryption parameters on system performance. The proposed scheme demonstrates significant performance improvements over mainstream SMPC protocols, with a 95.4 % reduction in key generation time and a 19.5 % reduction in ciphertext decryption time. Meanwhile, the scheme effectively resists various semi-malicious attacks, ensuring data security and privacy.
{"title":"Secure digital asset trading technology based on MPC and blockchain","authors":"Hongguo Zhang, Yuxin Sun, Kaiqi Zhang, Zhibo Guan, Chao Ma, Hai Huang","doi":"10.1016/j.ijin.2025.11.004","DOIUrl":"10.1016/j.ijin.2025.11.004","url":null,"abstract":"<div><div>With the rapid expansion of digital asset trading, the contradiction between data sharing and privacy protection has increasingly become a significant challenge in the Internet environment. To address this issue, this paper proposes a secure multi-party computation scheme based on blockchain technology. Firstly, in response to the risk of data leakage in distributed storage scenarios, a threshold-based encryption algorithm is designed, utilizing a distributed key protection mechanism to effectively prevent single-point failures and data breaches. Secondly, a smart contract system is developed: the ERC721 contract is used to confirm the ownership of data assets, the ERC20 contract facilitates the transfer of usage rights, and the threshold decryption contract ensures secure multi-party computation and compliant incentive distribution. The collaboration of these three types of contracts enables comprehensive on-chain management of data assets, covering the entire process from ownership confirmation and circulation to compliant usage. In addition, this paper integrates non-interactive zero-knowledge proofs into the multi-party interaction process, allowing public verification of data consistency and computational validity on the blockchain. Finally, experiments are conducted to evaluate the impact of computation latency, communication overhead, and encryption parameters on system performance. The proposed scheme demonstrates significant performance improvements over mainstream SMPC protocols, with a 95.4 % reduction in key generation time and a 19.5 % reduction in ciphertext decryption time. Meanwhile, the scheme effectively resists various semi-malicious attacks, ensuring data security and privacy.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 253-264"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579023","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.11.010
Yousef O. Sharrab , Maymona A. Alshabatat , Hani H. Attar , Basma M. Irtahi , Mohammad Ali H. Eljinini , M. Khosravi , Mohamed A. Hafez
This work discusses and emphasizes improvements in emergency medical services (EMS) by using intelligent transportation systems (ITS), geographic information systems (GIS), network analysis approaches, interviews, questionnaires, and field studies. The aim is to study ambulance operation mechanisms, analyze the issues they face, and determine the efficiency and effectiveness of ambulance station locations in the civil defense centers in Amman city, which is regarded as a key point in the field of e-health, which improves the human health services by euthanizing the health system access. This study also seeks to find solutions to recent issues that ambulances face, such as late response time. The official local target is 9 min to calculate, while a 7-min threshold was used in GIS analysis to assess optimal coverage. The results show that GIS has a high impact on planning, location analysis, and network processes. Moreover, the results demonstrate that GIS can be effectively used to build a spatial allocation model for determining optimal ambulance service locations and thus help select the most suitable sites for service provision. However, the results show that the actual response time exceeded the 9-min target established by the Civil Defense Department, which is attributed to several factors such as road congestion, traffic light delays, bad weather conditions, and other reasons. This study suggests several recommendations to overcome ambulance traffic challenges in Amman city by implementing intelligent transportation systems in the management of EMS.
{"title":"Enhanced intelligent transportation network systems for optimized emergency medical services management","authors":"Yousef O. Sharrab , Maymona A. Alshabatat , Hani H. Attar , Basma M. Irtahi , Mohammad Ali H. Eljinini , M. Khosravi , Mohamed A. Hafez","doi":"10.1016/j.ijin.2025.11.010","DOIUrl":"10.1016/j.ijin.2025.11.010","url":null,"abstract":"<div><div>This work discusses and emphasizes improvements in emergency medical services (EMS) by using intelligent transportation systems (ITS), geographic information systems (GIS), network analysis approaches, interviews, questionnaires, and field studies. The aim is to study ambulance operation mechanisms, analyze the issues they face, and determine the efficiency and effectiveness of ambulance station locations in the civil defense centers in Amman city, which is regarded as a key point in the field of e-health, which improves the human health services by euthanizing the health system access. This study also seeks to find solutions to recent issues that ambulances face, such as late response time. The official local target is 9 min to calculate, while a 7-min threshold was used in GIS analysis to assess optimal coverage. The results show that GIS has a high impact on planning, location analysis, and network processes. Moreover, the results demonstrate that GIS can be effectively used to build a spatial allocation model for determining optimal ambulance service locations and thus help select the most suitable sites for service provision. However, the results show that the actual response time exceeded the 9-min target established by the Civil Defense Department, which is attributed to several factors such as road congestion, traffic light delays, bad weather conditions, and other reasons. This study suggests several recommendations to overcome ambulance traffic challenges in Amman city by implementing intelligent transportation systems in the management of EMS.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 314-333"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077019","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ijin.2025.04.002
Yu Wang, Hong Huang
As the boost of information technology, network security issues have been increasingly prominent. Therefore, it is crucial for maintaining network security to establish an efficient abnormal traffic detection system. The study first explained the width learning algorithm, which was used as the basic framework to introduce the elastic lightweight and gated neural networks for optimization. Finally, an online abnormal traffic detection model and an offline abnormal traffic detection model were proposed. The experimental results showed that the fastest iteration of the online detection model was 190, the prediction accuracy was 96 %, the prediction error floated only between −0.01 and 0.01, and the shortest computing time was 2.012 s. The minimum iteration for the offline detection model was 200, with the abnormal flow detection error of 0.11. The lowest average absolute percentage error was 0.141 and the normalized root mean square error was 0.207. The lowest root mean square error reached 0.175, and the highest R2 error was 0.884. In summary, the two proposed models have achieved significant improvements in the accuracy and efficiency of abnormal traffic detection, providing a feasible solution for network security.
{"title":"Online and offline collaborative abnormal traffic intelligent detection system based on elastic lightweight width learning algorithm","authors":"Yu Wang, Hong Huang","doi":"10.1016/j.ijin.2025.04.002","DOIUrl":"10.1016/j.ijin.2025.04.002","url":null,"abstract":"<div><div>As the boost of information technology, network security issues have been increasingly prominent. Therefore, it is crucial for maintaining network security to establish an efficient abnormal traffic detection system. The study first explained the width learning algorithm, which was used as the basic framework to introduce the elastic lightweight and gated neural networks for optimization. Finally, an online abnormal traffic detection model and an offline abnormal traffic detection model were proposed. The experimental results showed that the fastest iteration of the online detection model was 190, the prediction accuracy was 96 %, the prediction error floated only between −0.01 and 0.01, and the shortest computing time was 2.012 s. The minimum iteration for the offline detection model was 200, with the abnormal flow detection error of 0.11. The lowest average absolute percentage error was 0.141 and the normalized root mean square error was 0.207. The lowest root mean square error reached 0.175, and the highest R<sup>2</sup> error was 0.884. In summary, the two proposed models have achieved significant improvements in the accuracy and efficiency of abnormal traffic detection, providing a feasible solution for network security.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 27-35"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943164","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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