Pub Date : 2025-02-04DOI: 10.1016/j.future.2025.107739
Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz
Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security.
{"title":"Separation and optimization of encryption and erasure coding in decentralized storage systems","authors":"Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz","doi":"10.1016/j.future.2025.107739","DOIUrl":"10.1016/j.future.2025.107739","url":null,"abstract":"<div><div>Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107739"},"PeriodicalIF":6.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.future.2025.107742
Siaka Konaté , Changli Li , Lizhong Xu
In recent years, LoRa backscatter has been seen as a promising technology to enable long-range communication among low-power IoT devices. Several designs and potential applications of LoRa backscatter have been proposed in the literature. This paper aims to provide a fundamental background for general readers to understand the basic concepts, operation methods, and mechanisms and discusses future potential applications of LoRa backscatter as well as research issues related to such applications.
{"title":"Review on LoRa backscatter technology","authors":"Siaka Konaté , Changli Li , Lizhong Xu","doi":"10.1016/j.future.2025.107742","DOIUrl":"10.1016/j.future.2025.107742","url":null,"abstract":"<div><div>In recent years, LoRa backscatter has been seen as a promising technology to enable long-range communication among low-power IoT devices. Several designs and potential applications of LoRa backscatter have been proposed in the literature. This paper aims to provide a fundamental background for general readers to understand the basic concepts, operation methods, and mechanisms and discusses future potential applications of LoRa backscatter as well as research issues related to such applications.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107742"},"PeriodicalIF":6.2,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143328367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-02DOI: 10.1016/j.future.2025.107750
Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng
Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem of imbalance data classification. The most common method is to start from the data level and realize data balance by resampling method. However, it remains a challenge to ensure that more valuable data is learned during the resampling process. Therefore, this study proposes an undersampling framework (UFIDSF) based on feature importance and double side filter. The first novelty of this framework is the use of double side filter to filter noise data in both majority and minority class samples. The second novelty is the projection of data samples into one dimension. UFIDSF is realized by calculating the distance between the feature of each dimension of the sample and its nearest neighbor and combining the feature importance. Experiments were conducted on 30 common benchmark imbalanced datasets, comparing the performance of 10 methods across four classifiers. Experimental results show that the proposed UFIDSF is effective and stable, and can significantly improve the adverse effects of machine learning algorithms on imbalanced data.
{"title":"UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification","authors":"Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng","doi":"10.1016/j.future.2025.107750","DOIUrl":"10.1016/j.future.2025.107750","url":null,"abstract":"<div><div>Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem of imbalance data classification. The most common method is to start from the data level and realize data balance by resampling method. However, it remains a challenge to ensure that more valuable data is learned during the resampling process. Therefore, this study proposes an undersampling framework (UFIDSF) based on feature importance and double side filter. The first novelty of this framework is the use of double side filter to filter noise data in both majority and minority class samples. The second novelty is the projection of data samples into one dimension. UFIDSF is realized by calculating the distance between the feature of each dimension of the sample and its nearest neighbor and combining the feature importance. Experiments were conducted on 30 common benchmark imbalanced datasets, comparing the performance of 10 methods across four classifiers. Experimental results show that the proposed UFIDSF is effective and stable, and can significantly improve the adverse effects of machine learning algorithms on imbalanced data.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107750"},"PeriodicalIF":6.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.future.2025.107745
Loris Belcastro , Jesus Carretero , Domenico Talia
In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.
{"title":"Edge-cloud solutions for big data analysis and distributed machine learning - 2","authors":"Loris Belcastro , Jesus Carretero , Domenico Talia","doi":"10.1016/j.future.2025.107745","DOIUrl":"10.1016/j.future.2025.107745","url":null,"abstract":"<div><div>In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107745"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.future.2025.107720
Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo
The emergence of Mobile Crowd Sensing (MCS) has provided a new paradigm for data sensing. An effective task allocation can ensure the stability and efficiency of the system in MCS. In this paper, we propose a privacy-preserving multi-objective, multi-task allocation optimization scheme, P2-TaskMP (Privacy-Preserving Task Allocation Optimization based on Mobility Prediction), to solve the multi-objective optimization task assignment problem while preserving users’ privacy. The scheme evaluates participants’ task completion capabilities by introducing mobility prediction based on fuzzy logic, which then informs task pre-allocation to form the initial population, unlike traditional methods that initialize populations randomly. To address potential privacy leaks of participants’ spatiotemporal information during mobility prediction, we employ differential privacy techniques to add Laplace noise to participants’ historical trajectory records, achieving adequate privacy protection. P2-TaskMP achieves Pareto-optimal solutions using the NSGA-II-DE (Non-dominated Sorting Genetic Algorithm II with Differential Evolution) algorithm and realizes satisfactory results with fast solution speed for large-scale task allocation problems. Simulations conducted on two real-world datasets demonstrate that our proposed method achieves higher accuracy, and the task allocation algorithm performs better than the compared algorithms in maximizing task completion rate and minimizing cost.
{"title":"P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction","authors":"Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo","doi":"10.1016/j.future.2025.107720","DOIUrl":"10.1016/j.future.2025.107720","url":null,"abstract":"<div><div>The emergence of Mobile Crowd Sensing (MCS) has provided a new paradigm for data sensing. An effective task allocation can ensure the stability and efficiency of the system in MCS. In this paper, we propose a privacy-preserving multi-objective, multi-task allocation optimization scheme, P2-TaskMP (Privacy-Preserving Task Allocation Optimization based on Mobility Prediction), to solve the multi-objective optimization task assignment problem while preserving users’ privacy. The scheme evaluates participants’ task completion capabilities by introducing mobility prediction based on fuzzy logic, which then informs task pre-allocation to form the initial population, unlike traditional methods that initialize populations randomly. To address potential privacy leaks of participants’ spatiotemporal information during mobility prediction, we employ differential privacy techniques to add Laplace noise to participants’ historical trajectory records, achieving adequate privacy protection. P2-TaskMP achieves Pareto-optimal solutions using the NSGA-II-DE (Non-dominated Sorting Genetic Algorithm II with Differential Evolution) algorithm and realizes satisfactory results with fast solution speed for large-scale task allocation problems. Simulations conducted on two real-world datasets demonstrate that our proposed method achieves higher accuracy, and the task allocation algorithm performs better than the compared algorithms in maximizing task completion rate and minimizing cost.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107720"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.future.2025.107740
Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li
Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency. To address the challenge, in this paper, a novel quantization-based privacy-preserving localization (QPPL) algorithm is proposed to estimate the target’s location with accuracy, privacy preservation and communication efficiency at the same time. Firstly, the location information is quantized, i.e., deviate the location data, to preserve the private location information during the localization process. With the quantization on the location information, the data scale is compressed to reduce communication cost and improve localization efficiency. Then, to improve the localization accuracy, an optimal weight allocation scheme is designed to aggregate the location estimates from the heterogeneous anchor devices. By minimizing the weighted sum of squared quantization errors of all anchor devices, a closed form optimal weight allocation scheme is derived by using convex optimization theory. Finally, through theoretical analysis, we prove the accuracy, privacy preservation and efficiency of the QPPL algorithm. Experimental evaluation demonstrates that QPPL has superior performance compared with existing methods.
{"title":"Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach","authors":"Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li","doi":"10.1016/j.future.2025.107740","DOIUrl":"10.1016/j.future.2025.107740","url":null,"abstract":"<div><div>Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency. To address the challenge, in this paper, a novel quantization-based privacy-preserving localization (QPPL) algorithm is proposed to estimate the target’s location with accuracy, privacy preservation and communication efficiency at the same time. Firstly, the location information is quantized, i.e., deviate the location data, to preserve the private location information during the localization process. With the quantization on the location information, the data scale is compressed to reduce communication cost and improve localization efficiency. Then, to improve the localization accuracy, an optimal weight allocation scheme is designed to aggregate the location estimates from the heterogeneous anchor devices. By minimizing the weighted sum of squared quantization errors of all anchor devices, a closed form optimal weight allocation scheme is derived by using convex optimization theory. Finally, through theoretical analysis, we prove the accuracy, privacy preservation and efficiency of the QPPL algorithm. Experimental evaluation demonstrates that QPPL has superior performance compared with existing methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107740"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1016/j.future.2025.107723
Bilel Benjdira, Anis Koubaa, Anas M. Ali
Despite significant advancements in robotics and AI, existing systems often struggle to integrate diverse modalities (e.g., image, sound, actuator data) into a unified framework, resulting in fragmented architectures that limit adaptability, scalability, and explainability. To address these gaps, this paper introduces Prompting Robotic Modalities (PRM), a novel architecture that centralizes language models for controlling and managing complex systems through natural language. In PRM, each system modality (e.g., image, sound, actuator) is handled independently by a Modality Language Model (MLM), while a central Task Modality, powered by a Large Language Model (LLM), orchestrates complex tasks using information from the MLMs. Each MLM is trained on datasets that pair modality-specific data with rich textual descriptions, enabling intuitive, language-based interaction. We validate PRM with two main contributions: (1) ROSGPT_Vision, a new open-source ROS 2 package (available at https://github.com/bilel-bj/ROSGPT_Vision) for visual modality tasks, achieving up to 66% classification accuracy in driver-focus monitoring—surpassing other tested models in its category; and (2) CarMate, a driver-distraction detection application that significantly reduces development time and cost by allowing rapid adaptation to new monitoring tasks via simple prompt adjustments. In addition, we develop a Navigation Language Model (NLM) that converts free-form human language orders into detailed ROS commands, underscoring PRM’s modality-agnostic adaptability. Experimental results demonstrate that PRM simplifies system development, outperforms baseline vision-language approaches in specialized tasks (e.g., driver monitoring), reduces complexity through prompt engineering rather than extensive coding, and enhances explainability via natural-language-based diagnostics. Hence, PRM lays a promising foundation for next-generation complex and robotic systems by integrating advanced language model capabilities at their core, making them more adaptable to new environments, cost-effective, and user-friendly.
{"title":"Prompting Robotic Modalities (PRM): A structured architecture for centralizing language models in complex systems","authors":"Bilel Benjdira, Anis Koubaa, Anas M. Ali","doi":"10.1016/j.future.2025.107723","DOIUrl":"10.1016/j.future.2025.107723","url":null,"abstract":"<div><div>Despite significant advancements in robotics and AI, existing systems often struggle to integrate diverse modalities (e.g., image, sound, actuator data) into a unified framework, resulting in fragmented architectures that limit adaptability, scalability, and explainability. To address these gaps, this paper introduces Prompting Robotic Modalities (PRM), a novel architecture that centralizes language models for controlling and managing complex systems through natural language. In PRM, each system modality (e.g., image, sound, actuator) is handled independently by a Modality Language Model (MLM), while a central Task Modality, powered by a Large Language Model (LLM), orchestrates complex tasks using information from the MLMs. Each MLM is trained on datasets that pair modality-specific data with rich textual descriptions, enabling intuitive, language-based interaction. We validate PRM with two main contributions: (1) ROSGPT_Vision, a new open-source ROS 2 package (available at <span><span>https://github.com/bilel-bj/ROSGPT_Vision</span><svg><path></path></svg></span>) for visual modality tasks, achieving up to 66% classification accuracy in driver-focus monitoring—surpassing other tested models in its category; and (2) CarMate, a driver-distraction detection application that significantly reduces development time and cost by allowing rapid adaptation to new monitoring tasks via simple prompt adjustments. In addition, we develop a Navigation Language Model (NLM) that converts free-form human language orders into detailed ROS commands, underscoring PRM’s modality-agnostic adaptability. Experimental results demonstrate that PRM simplifies system development, outperforms baseline vision-language approaches in specialized tasks (e.g., driver monitoring), reduces complexity through prompt engineering rather than extensive coding, and enhances explainability via natural-language-based diagnostics. Hence, PRM lays a promising foundation for next-generation complex and robotic systems by integrating advanced language model capabilities at their core, making them more adaptable to new environments, cost-effective, and user-friendly.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107723"},"PeriodicalIF":6.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1016/j.future.2025.107735
Lichuan Ma , Lu Zhou , Hang Huang , Youyang Qu , Xuefeng Liu
Nowadays, a new generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the same time, in order to fully stimulate users to create and share contents in the era of Web3.0, the importance of digital assets, e.g., digit tokens and cryptocurrencies, is increasing. As a result, whether digital asset transfer can be securely and efficiently accommodated determines the further development of Web3.0. Blockchain is believed to be one effective solution to guarantee the security of digital asset transfer. However, existing works either target fully decentralized scenarios or fail to settle digital asset transfers with high efficiency. Thus in this paper, a formal yet novel service provider-intermediated architecture is firstly proposed to closely align with the practical requirements of Web3.0. Then, an efficient privacy-preserving distributed ledger construction protocol, coined as EPPDL, is proposed to safeguard digital asset transfer among users registered at different service providers. Concrete security analysis proves that the proposed EPPDL is secure against different types of adversaries, while comprehensive experiments verify its efficiency and effectiveness.
{"title":"EPPDL: An efficient privacy-preserving distributed ledger for digital asset transfer in Web3.0","authors":"Lichuan Ma , Lu Zhou , Hang Huang , Youyang Qu , Xuefeng Liu","doi":"10.1016/j.future.2025.107735","DOIUrl":"10.1016/j.future.2025.107735","url":null,"abstract":"<div><div>Nowadays, a new generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the same time, in order to fully stimulate users to create and share contents in the era of Web3.0, the importance of digital assets, e.g., digit tokens and cryptocurrencies, is increasing. As a result, whether digital asset transfer can be securely and efficiently accommodated determines the further development of Web3.0. Blockchain is believed to be one effective solution to guarantee the security of digital asset transfer. However, existing works either target fully decentralized scenarios or fail to settle digital asset transfers with high efficiency. Thus in this paper, a formal yet novel service provider-intermediated architecture is firstly proposed to closely align with the practical requirements of Web3.0. Then, an efficient privacy-preserving distributed ledger construction protocol, coined as EPPDL, is proposed to safeguard digital asset transfer among users registered at different service providers. Concrete security analysis proves that the proposed EPPDL is secure against different types of adversaries, while comprehensive experiments verify its efficiency and effectiveness.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107735"},"PeriodicalIF":6.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1016/j.future.2025.107710
Damien Landré , Laurent Philippe , Jean-Marc Pierson
Datacenters are an essential part of the internet, but their continuous development requires finding sustainable solutions to limit their impact on climate change. The Datazero2 project aims to design datacenters running solely on local renewable energy. In this paper, we tackle the problem of computing the minimum power demand to process a workload under quality of service constraint in a datacenter operated with renewable energy. To solve this problem, we propose a binary search algorithm that requires the computation of machine configurations with maximum computing power. When machines are heterogeneous, we face the problem of choosing the machines and their DVFS (Dynamic Voltage and Frequency Scaling) state. A MILP (Mixed-Integer Linear Programming), to find the optimal solution, and four heuristics that give satisfactory results in a reasonable time are proposed. simulations show that the best heuristics reach an average deviation from the optimal solution of 0.03% to 0.65%. The binary search algorithm is challenged against a real workload to assess the impact of flexibility on the quality of service.
{"title":"Sufficiency power consideration to run a workload on renewable energy operated datacenter","authors":"Damien Landré , Laurent Philippe , Jean-Marc Pierson","doi":"10.1016/j.future.2025.107710","DOIUrl":"10.1016/j.future.2025.107710","url":null,"abstract":"<div><div>Datacenters are an essential part of the internet, but their continuous development requires finding sustainable solutions to limit their impact on climate change. The <span>Datazero2</span> project aims to design datacenters running solely on local renewable energy. In this paper, we tackle the problem of computing the minimum power demand to process a workload under quality of service constraint in a datacenter operated with renewable energy. To solve this problem, we propose a binary search algorithm that requires the computation of machine configurations with maximum computing power. When machines are heterogeneous, we face the problem of choosing the machines and their DVFS (Dynamic Voltage and Frequency Scaling) state. A MILP (Mixed-Integer Linear Programming), to find the optimal solution, and four heuristics that give satisfactory results in a reasonable time are proposed. simulations show that the best heuristics reach an average deviation from the optimal solution of 0.03% to 0.65%. The binary search algorithm is challenged against a real workload to assess the impact of flexibility on the quality of service.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107710"},"PeriodicalIF":6.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.future.2025.107726
Yinfu Deng , Hua Dai , Zhangchen Li , Haiping Huang , Qian Zhou , Jian Xu , Geng Yang
Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing will be accelerated. To improve the search efficiency, we propose an encrypted two-layer balance binary tree index-based multi-keyword ranked search scheme (ETMRS) to address this problem in this paper. An equal-length dictionary partition-based compressed vector space model (EDP-CVSM) is presented, which introduces the dictionary partition strategy. It effectively compresses the document and search vectors, which benefits the efficiency of relevance score computation in search processing. In addition, to further improves the search efficiency, a two-layer balance binary tree index (TBBT-index) is proposed, which adopts secure inner product and symmetric encryption to preserve the privacy. The index is able to filter out the sub-dictionaries having no search keywords in the upper layer and identify the result documents in the lower layer, which speeds up the search processing. Experimental results show a good performance of the proposed scheme in file coverage rate, search precision, rank privacy, search efficiency and space consumption.
{"title":"EDP-CVSM model-based multi-keyword ranked search scheme over encrypted cloud data","authors":"Yinfu Deng , Hua Dai , Zhangchen Li , Haiping Huang , Qian Zhou , Jian Xu , Geng Yang","doi":"10.1016/j.future.2025.107726","DOIUrl":"10.1016/j.future.2025.107726","url":null,"abstract":"<div><div>Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing will be accelerated. To improve the search efficiency, we propose an encrypted two-layer balance binary tree index-based multi-keyword ranked search scheme (ETMRS) to address this problem in this paper. An equal-length dictionary partition-based compressed vector space model (EDP-CVSM) is presented, which introduces the dictionary partition strategy. It effectively compresses the document and search vectors, which benefits the efficiency of relevance score computation in search processing. In addition, to further improves the search efficiency, a two-layer balance binary tree index (TBBT-index) is proposed, which adopts secure inner product and symmetric encryption to preserve the privacy. The index is able to filter out the sub-dictionaries having no search keywords in the upper layer and identify the result documents in the lower layer, which speeds up the search processing. Experimental results show a good performance of the proposed scheme in file coverage rate, search precision, rank privacy, search efficiency and space consumption.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107726"},"PeriodicalIF":6.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}