To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.
{"title":"IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data","authors":"H. Ignatious, Hesham El-Sayed, Salah Bouktif","doi":"10.3390/jsan13010015","DOIUrl":"https://doi.org/10.3390/jsan13010015","url":null,"abstract":"To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797128","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}
In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.
{"title":"Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios","authors":"David Naseh, S. Shinde, D. Tarchi","doi":"10.3390/jsan13010014","DOIUrl":"https://doi.org/10.3390/jsan13010014","url":null,"abstract":"In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795547","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}
To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.
{"title":"IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data","authors":"H. Ignatious, Hesham El-Sayed, Salah Bouktif","doi":"10.3390/jsan13010015","DOIUrl":"https://doi.org/10.3390/jsan13010015","url":null,"abstract":"To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857037","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}
In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.
{"title":"Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios","authors":"David Naseh, S. Shinde, D. Tarchi","doi":"10.3390/jsan13010014","DOIUrl":"https://doi.org/10.3390/jsan13010014","url":null,"abstract":"In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855627","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}
Jongsuk Kongsen, Doungsuda Chantaradsuwan, Peeravit Koad, May Thu, Chanankorn Jandaeng
This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
{"title":"A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation","authors":"Jongsuk Kongsen, Doungsuda Chantaradsuwan, Peeravit Koad, May Thu, Chanankorn Jandaeng","doi":"10.3390/jsan13010013","DOIUrl":"https://doi.org/10.3390/jsan13010013","url":null,"abstract":"This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805110","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}
Jongsuk Kongsen, Doungsuda Chantaradsuwan, Peeravit Koad, May Thu, Chanankorn Jandaeng
This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
{"title":"A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation","authors":"Jongsuk Kongsen, Doungsuda Chantaradsuwan, Peeravit Koad, May Thu, Chanankorn Jandaeng","doi":"10.3390/jsan13010013","DOIUrl":"https://doi.org/10.3390/jsan13010013","url":null,"abstract":"This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864978","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}
Salim Sabah Bulbul, Z. Abduljabbar, Duaa Fadhel Najem, V. O. Nyangaresi, Junchao Ma, Abdulla J. Y. Aldarwish
Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.
{"title":"Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control","authors":"Salim Sabah Bulbul, Z. Abduljabbar, Duaa Fadhel Najem, V. O. Nyangaresi, Junchao Ma, Abdulla J. Y. Aldarwish","doi":"10.3390/jsan13010012","DOIUrl":"https://doi.org/10.3390/jsan13010012","url":null,"abstract":"Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870060","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}
Salim Sabah Bulbul, Z. Abduljabbar, Duaa Fadhel Najem, V. O. Nyangaresi, Junchao Ma, Abdulla J. Y. Aldarwish
Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.
{"title":"Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control","authors":"Salim Sabah Bulbul, Z. Abduljabbar, Duaa Fadhel Najem, V. O. Nyangaresi, Junchao Ma, Abdulla J. Y. Aldarwish","doi":"10.3390/jsan13010012","DOIUrl":"https://doi.org/10.3390/jsan13010012","url":null,"abstract":"Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810165","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}
Alaa AlZailaa, Hao Ran Chi, A. Radwan, Rui L. Aguiar
Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.
{"title":"Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM","authors":"Alaa AlZailaa, Hao Ran Chi, A. Radwan, Rui L. Aguiar","doi":"10.3390/jsan13010010","DOIUrl":"https://doi.org/10.3390/jsan13010010","url":null,"abstract":"Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892155","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}
There is an urgent need to introduce security-by-design in networks [...]
迫切需要在网络中引入安全设计[......]
{"title":"Featured Papers on Network Security and Privacy","authors":"Jordi Mongay Batalla","doi":"10.3390/jsan13010011","DOIUrl":"https://doi.org/10.3390/jsan13010011","url":null,"abstract":"There is an urgent need to introduce security-by-design in networks [...]","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139880315","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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