首页 > 最新文献

Pervasive and Mobile Computing最新文献

英文 中文
Navigating transient content: PFC caching approach for NDN-based IoT networks 导航瞬时内容:基于 NDN 的物联网网络的 PFC 缓存方法
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-24 DOI: 10.1016/j.pmcj.2025.102031
Sumit Kumar , Rajeev Tiwari
The emergence of Internet-of-Things (IoT) has revolutionized communication among devices. IoT devices autonomously collect and disseminate contents to end-users via network routers. There is growing interest in integrating IoT communications with Named Data Networking (NDN) architecture to retrieve and distribute content efficiently. The content caching characteristics of NDN are pivotal in improving Quality-of-Service (QoS) for IoT. However, unlike multimedia content traffic, which tends to remain static, IoT-generated content is inherently transient in nature, and each content has a finite lifespan. As a result, without efficient caching solutions for IoT contents, the network efficiency and user experience would be degraded. Existing caching approaches often overlook the importance of IoT content freshness, its access pattern and the position of routers during content placement decisions in the IoT networks. In this paper, a novel Popularity and Freshness-based Caching (PFC) scheme has been proposed that aims to strategically cache popular and fresh IoT contents on routers located close to the end-user devices. In the proposed solution, the popularity of content is determined using the request history queue deployed on all network routers. For efficient caching decisions, the hop count metric favors routers in close proximity to end-users. Rigorous simulations with realistic network parameters are performed on the realistic IoT network topologies. The simulation results demonstrate that the PFC approach outperforms existing state-of-the-art caching approaches (LCE, LCC, Consumer-Driven, Consumer-Cache, etc.) on several performance parameters: cache hit ratio, network delay, hop count, network traffic, and energy consumption. This makes the PFC caching approach well-suited for NDN-based IoT networks by enabling efficient content caching decisions.
物联网(IoT)的出现彻底改变了设备之间的通信。物联网设备通过网络路由器自动收集和传播内容给最终用户。人们对将物联网通信与命名数据网络(NDN)架构集成以有效检索和分发内容的兴趣越来越大。NDN的内容缓存特性对于提高物联网的服务质量(QoS)至关重要。然而,与倾向于保持静态的多媒体内容流量不同,物联网生成的内容本质上是短暂的,每个内容都有有限的生命周期。因此,如果没有高效的物联网内容缓存解决方案,网络效率和用户体验将会降低。现有的缓存方法往往忽略了物联网内容新鲜度、访问模式和路由器在物联网网络中内容放置决策中的位置的重要性。本文提出了一种新颖的基于流行度和新鲜度的缓存(PFC)方案,旨在战略性地缓存位于终端用户设备附近的路由器上的流行和新鲜物联网内容。在提出的解决方案中,使用部署在所有网络路由器上的请求历史队列来确定内容的流行程度。对于高效的缓存决策,跳数度量倾向于靠近最终用户的路由器。在真实的物联网网络拓扑结构上进行了具有真实网络参数的严格模拟。仿真结果表明,PFC方法在几个性能参数上优于现有的最先进的缓存方法(LCE、LCC、消费者驱动、消费者缓存等):缓存命中率、网络延迟、跳数、网络流量和能耗。通过实现高效的内容缓存决策,PFC缓存方法非常适合基于ndn的物联网网络。
{"title":"Navigating transient content: PFC caching approach for NDN-based IoT networks","authors":"Sumit Kumar ,&nbsp;Rajeev Tiwari","doi":"10.1016/j.pmcj.2025.102031","DOIUrl":"10.1016/j.pmcj.2025.102031","url":null,"abstract":"<div><div>The emergence of Internet-of-Things (IoT) has revolutionized communication among devices. IoT devices autonomously collect and disseminate contents to end-users via network routers. There is growing interest in integrating IoT communications with Named Data Networking (NDN) architecture to retrieve and distribute content efficiently. The content caching characteristics of NDN are pivotal in improving Quality-of-Service (QoS) for IoT. However, unlike multimedia content traffic, which tends to remain static, IoT-generated content is inherently transient in nature, and each content has a finite lifespan. As a result, without efficient caching solutions for IoT contents, the network efficiency and user experience would be degraded. Existing caching approaches often overlook the importance of IoT content freshness, its access pattern and the position of routers during content placement decisions in the IoT networks. In this paper, a novel Popularity and Freshness-based Caching (PFC) scheme has been proposed that aims to strategically cache popular and fresh IoT contents on routers located close to the end-user devices. In the proposed solution, the popularity of content is determined using the request history queue deployed on all network routers. For efficient caching decisions, the hop count metric favors routers in close proximity to end-users. Rigorous simulations with realistic network parameters are performed on the realistic IoT network topologies. The simulation results demonstrate that the PFC approach outperforms existing state-of-the-art caching approaches (LCE, LCC, Consumer-Driven, Consumer-Cache, etc.) on several performance parameters: cache hit ratio, network delay, hop count, network traffic, and energy consumption. This makes the PFC caching approach well-suited for NDN-based IoT networks by enabling efficient content caching decisions.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102031"},"PeriodicalIF":3.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Investment-driven budget allocation and dynamic pricing strategies in edge cache network 边缘缓存网络中投资驱动的预算分配与动态定价策略
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-22 DOI: 10.1016/j.pmcj.2025.102040
Quyuan Wang , Pengyang Chen , Jiadi Liu , Ying Wang , Zhiwei Guo
Edge Caching is an application with great commercial potential in accelerating content acquisition by near-client content caching. To provide high-quality services for customers, it is indispensable for content providers to purchase or rent sufficient wireless channels and cache storage resources from edge suppliers. However, few work has investigated how to allocate limited budget to the appropriate resources in an economically way for caching at a network edge. In this paper, we construct a Fisher cache market to tackle the budget allocation problem and the price adjustment problem in edge caching by using the portfolio approach. In the budget allocation problem, we utilize the Iso-cost line and threshold settings to narrow search space and propose an algorithm termed as Gradient descent based Portfolio Search (GBPS) to acquire an optimal portfolio within a limited search field. With the aid of market supply and demand in micro economic theory, we put forward K-popular Suppliers Price Adjustment algorithm (KSPA) and Elastic Supply and Demand Price Adjustment algorithm (ESDPA) price adjustment algorithms to achieve market equilibrium within a limited budget. Finally, numerical results demonstrate that the proposed algorithms perform better in terms of trading success rate and total payoff by the comparisons of different algorithms.
边缘缓存是一种具有巨大商业潜力的应用,可通过近客户端内容缓存加速内容获取。为了向客户提供高质量的服务,内容提供商必须向边缘供应商购买或租用足够的无线信道和缓存存储资源。然而,很少有人研究如何以经济的方式将有限的预算分配给网络边缘缓存的适当资源。在本文中,我们构建了一个费雪缓存市场,利用组合方法解决边缘缓存中的预算分配问题和价格调整问题。在预算分配问题中,我们利用等成本线和阈值设置来缩小搜索空间,并提出了一种称为基于梯度下降的组合搜索(GBPS)的算法,以在有限的搜索范围内获得最佳组合。借助微观经济理论中的市场供求关系,我们提出了 K-Popular Supplier Price Adjustment algorithm (KSPA) 和 Elastic Supply and Demand Price Adjustment algorithm (ESDPA) 价格调整算法,以在有限预算内实现市场均衡。最后,数值结果表明,通过对不同算法的比较,所提出的算法在交易成功率和总报酬方面表现更好。
{"title":"Investment-driven budget allocation and dynamic pricing strategies in edge cache network","authors":"Quyuan Wang ,&nbsp;Pengyang Chen ,&nbsp;Jiadi Liu ,&nbsp;Ying Wang ,&nbsp;Zhiwei Guo","doi":"10.1016/j.pmcj.2025.102040","DOIUrl":"10.1016/j.pmcj.2025.102040","url":null,"abstract":"<div><div>Edge Caching is an application with great commercial potential in accelerating content acquisition by near-client content caching. To provide high-quality services for customers, it is indispensable for content providers to purchase or rent sufficient wireless channels and cache storage resources from edge suppliers. However, few work has investigated how to allocate limited budget to the appropriate resources in an economically way for caching at a network edge. In this paper, we construct a Fisher cache market to tackle the budget allocation problem and the price adjustment problem in edge caching by using the portfolio approach. In the budget allocation problem, we utilize the Iso-cost line and threshold settings to narrow search space and propose an algorithm termed as Gradient descent based Portfolio Search (GBPS) to acquire an optimal portfolio within a limited search field. With the aid of market supply and demand in micro economic theory, we put forward K-popular Suppliers Price Adjustment algorithm (KSPA) and Elastic Supply and Demand Price Adjustment algorithm (ESDPA) price adjustment algorithms to achieve market equilibrium within a limited budget. Finally, numerical results demonstrate that the proposed algorithms perform better in terms of trading success rate and total payoff by the comparisons of different algorithms.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102040"},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
EPCM: Efficient privacy-preserving charging matching scheme with data integrity for electric vehicles EPCM:基于数据完整性的电动汽车高效隐私保护充电匹配方案
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-22 DOI: 10.1016/j.pmcj.2025.102042
Tingting Jin, Peng Hu, Kaizhong Zuo, Tianjiao Ni, Dong Xie, Zhangyi Shen, Fulong Chen
Compared to traditional charging stations, the Vehicle-to-Vehicle (V2V) charging mode can expand the coverage of the charging network and is expected to become an important supplementary method for future electric vehicle charging. However, the leakage of location privacy in charging matching has become one of the main concerns of users. To tackle this problem, we propose an efficient privacy preserving charging matching scheme, named EPCM, which ensures data integrity without compromising the location privacy of vehicles. Firstly, we utilize the modified Paillier cryptosystem and identity based batch signature to achieve location privacy and data integrity. Secondly, our scheme operates in a round-by-round manner, ensuring immediate task completion and allowing vehicles to dynamically join or leave. The security proof and analysis indicates that EPCM can achieve security features including confidentiality, location privacy, authentication, and data integrity. Furthermore, by carrying out extensive experiments, the experimental results demonstrate that our scheme performs excellently in terms of computational and communication overhead, as well as total transmission delay.
与传统充电站相比,车对车(V2V)充电模式可以扩大充电网络的覆盖范围,有望成为未来电动汽车充电的重要补充方式。然而,充电匹配中的位置隐私泄露问题已成为用户关注的焦点之一。为解决这一问题,我们提出了一种高效的隐私保护充电匹配方案,命名为 EPCM,它能在不损害车辆位置隐私的情况下确保数据完整性。首先,我们利用改进的 Paillier 密码系统和基于身份的批量签名来实现位置隐私和数据完整性。其次,我们的方案以逐轮方式运行,确保任务立即完成,并允许车辆动态加入或离开。安全证明和分析表明,EPCM 可以实现保密性、位置隐私性、身份验证和数据完整性等安全特性。此外,通过大量实验,实验结果表明我们的方案在计算和通信开销以及总传输延迟方面表现出色。
{"title":"EPCM: Efficient privacy-preserving charging matching scheme with data integrity for electric vehicles","authors":"Tingting Jin,&nbsp;Peng Hu,&nbsp;Kaizhong Zuo,&nbsp;Tianjiao Ni,&nbsp;Dong Xie,&nbsp;Zhangyi Shen,&nbsp;Fulong Chen","doi":"10.1016/j.pmcj.2025.102042","DOIUrl":"10.1016/j.pmcj.2025.102042","url":null,"abstract":"<div><div>Compared to traditional charging stations, the Vehicle-to-Vehicle (V2V) charging mode can expand the coverage of the charging network and is expected to become an important supplementary method for future electric vehicle charging. However, the leakage of location privacy in charging matching has become one of the main concerns of users. To tackle this problem, we propose an efficient privacy preserving charging matching scheme, named EPCM, which ensures data integrity without compromising the location privacy of vehicles. Firstly, we utilize the modified Paillier cryptosystem and identity based batch signature to achieve location privacy and data integrity. Secondly, our scheme operates in a round-by-round manner, ensuring immediate task completion and allowing vehicles to dynamically join or leave. The security proof and analysis indicates that EPCM can achieve security features including confidentiality, location privacy, authentication, and data integrity. Furthermore, by carrying out extensive experiments, the experimental results demonstrate that our scheme performs excellently in terms of computational and communication overhead, as well as total transmission delay.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102042"},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Load-balancing model using game theory in edge-based IoT network 基于博弈论的边缘物联网负载均衡模型
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-16 DOI: 10.1016/j.pmcj.2025.102041
Zaineb Naaz , Gamini Joshi , Vidushi Sharma
To manage increasing volume of IoT data, edge computing offers scalable solutions, but increasing data loads can overwhelm edge nodes, depleting resources and extending processing times. This paper proposes a load-balancing model using game theory (LMGT) in edge computing-assisted IoT networks by considering nodes lifetime as their primary resource to reduce IoT task execution times, especially for time-sensitive tasks. Simulation results demonstrate that LMGT outperforms existing methods—Preference-Based Stable Mechanism (PBSM), Centralized, Min-Min, and Max-Min—in terms of execution time reductions achieving improvements of, on average, 40 %, 56 %, 91 %, and 93 %, respectively, across various combinations of edge and IoT nodes. Furthermore, the proposed scheme ensures a more uniform distribution of data load across edge nodes compared to the existing schemes.
为管理日益增长的物联网数据量,边缘计算提供了可扩展的解决方案,但不断增加的数据负载会使边缘节点不堪重负,从而耗尽资源并延长处理时间。本文提出了边缘计算辅助物联网网络中的博弈论负载平衡模型(LMGT),将节点寿命视为其主要资源,以减少物联网任务的执行时间,尤其是对时间敏感的任务。仿真结果表明,LMGT 在缩短执行时间方面优于现有方法--基于偏好的稳定机制 (PBSM)、集中式、最小最小和最大最小,在不同的边缘和物联网节点组合中平均分别提高了 40%、56%、91% 和 93%。此外,与现有方案相比,拟议方案可确保数据负载在边缘节点间的分布更加均匀。
{"title":"Load-balancing model using game theory in edge-based IoT network","authors":"Zaineb Naaz ,&nbsp;Gamini Joshi ,&nbsp;Vidushi Sharma","doi":"10.1016/j.pmcj.2025.102041","DOIUrl":"10.1016/j.pmcj.2025.102041","url":null,"abstract":"<div><div>To manage increasing volume of IoT data, edge computing offers scalable solutions, but increasing data loads can overwhelm edge nodes, depleting resources and extending processing times. This paper proposes a load-balancing model using game theory (LMGT) in edge computing-assisted IoT networks by considering nodes lifetime as their primary resource to reduce IoT task execution times, especially for time-sensitive tasks. Simulation results demonstrate that LMGT outperforms existing methods—Preference-Based Stable Mechanism (PBSM), Centralized, Min-Min, and Max-Min—in terms of execution time reductions achieving improvements of, on average, 40 %, 56 %, 91 %, and 93 %, respectively, across various combinations of edge and IoT nodes. Furthermore, the proposed scheme ensures a more uniform distribution of data load across edge nodes compared to the existing schemes.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102041"},"PeriodicalIF":3.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bio-inspired recruiting strategies for on-demand connectivity over a multi-layer hybrid CubeSat-UAV networks in emergency scenarios 紧急情况下多层立方体卫星-无人机混合网络按需连接的仿生招聘策略
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-10 DOI: 10.1016/j.pmcj.2025.102030
Mauro Tropea , Alex Ramiro Masaquiza Caiza , Floriano De Rango
In emergency scenarios, the network infrastructure must remain reliable and continuously available to ensure connectivity to people and optimal performance in supporting different types of applications, including real-time services. When terrestrial infrastructure is compromised during emergencies, Flying Ad Hoc Networks (FANETs) can offer a quick and effective solution for re-establishing connectivity in affected areas. The dynamic coverage provided by a swarm of UAVs (Unmanned Aerial Vehicles) during a disaster could be crucial for people inside the affected areas. In high-demand and critical situations, the performance of FANETs may deteriorate due to several factors, including simultaneous user connections, high traffic volumes, limited energy resources of network devices, and interference arising within the same geographic region. To address these challenges, this paper proposes a novel, bio-inspired recruitment algorithm that aims to guarantee good performance of FANETs in energy constrained scenarios by efficiently recruiting UAVs to cover the demand of end users connected to the network. In such a scenario, when additional UAVs cannot be reachable using the on-earth network infrastructure and multi-hop routing, the recruiting can be supported through a multi-layer hybrid architecture that integrates CubeSats to forward recruiting requests to potential UAVs located far from the network. This approach not only enhances the connectivity of end users but also ensures that the network can efficiently be adapted to the demands of users in emergency situations.
在紧急情况下,网络基础设施必须保持可靠和持续可用,以确保与人员的连接和支持不同类型应用程序(包括实时服务)的最佳性能。当地面基础设施在紧急情况下受到破坏时,飞行自组织网络(fanet)可以为受影响地区重建连接提供快速有效的解决方案。在灾难期间,一群无人机(无人驾驶飞行器)提供的动态覆盖对受灾地区的人们来说至关重要。在高需求和紧急情况下,由于用户同时连接、流量大、网络设备能源有限、同一地理区域内产生干扰等因素,fanet的性能可能会下降。为了解决这些挑战,本文提出了一种新颖的仿生招募算法,旨在通过有效招募无人机来满足连接到网络的最终用户的需求,从而保证在能量受限的情况下fanet的良好性能。在这种情况下,当使用地面网络基础设施和多跳路由无法到达额外的无人机时,可以通过集成立方体卫星的多层混合架构来支持招募,从而将招募请求转发给远离网络的潜在无人机。这种方法不仅增强了终端用户的连通性,而且保证了网络在紧急情况下能够有效地适应用户的需求。
{"title":"Bio-inspired recruiting strategies for on-demand connectivity over a multi-layer hybrid CubeSat-UAV networks in emergency scenarios","authors":"Mauro Tropea ,&nbsp;Alex Ramiro Masaquiza Caiza ,&nbsp;Floriano De Rango","doi":"10.1016/j.pmcj.2025.102030","DOIUrl":"10.1016/j.pmcj.2025.102030","url":null,"abstract":"<div><div>In emergency scenarios, the network infrastructure must remain reliable and continuously available to ensure connectivity to people and optimal performance in supporting different types of applications, including real-time services. When terrestrial infrastructure is compromised during emergencies, Flying Ad Hoc Networks (FANETs) can offer a quick and effective solution for re-establishing connectivity in affected areas. The dynamic coverage provided by a swarm of UAVs (Unmanned Aerial Vehicles) during a disaster could be crucial for people inside the affected areas. In high-demand and critical situations, the performance of FANETs may deteriorate due to several factors, including simultaneous user connections, high traffic volumes, limited energy resources of network devices, and interference arising within the same geographic region. To address these challenges, this paper proposes a novel, bio-inspired recruitment algorithm that aims to guarantee good performance of FANETs in energy constrained scenarios by efficiently recruiting UAVs to cover the demand of end users connected to the network. In such a scenario, when additional UAVs cannot be reachable using the on-earth network infrastructure and multi-hop routing, the recruiting can be supported through a multi-layer hybrid architecture that integrates CubeSats to forward recruiting requests to potential UAVs located far from the network. This approach not only enhances the connectivity of end users but also ensures that the network can efficiently be adapted to the demands of users in emergency situations.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102030"},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
引用次数: 0
In-bed gesture recognition to support the communication of people with Aphasia 床上手势识别,以支持失语症患者的交流
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-02-26 DOI: 10.1016/j.pmcj.2025.102029
Ana Patrícia Rocha, Afonso Guimarães, Ilídio C. Oliveira, José Maria Fernandes, Miguel Oliveira e Silva, Samuel Silva, António Teixeira
People with language impairments can have difficulties expressing themselves to others, leading to major limitations to their safety, independence, and quality of life in general. Aphasia is an example of an acquired language impairment that affects many people (around 2 million in the United States), being commonly caused by stroke, but also by other brain injuries. Several augmentative and alternative communication solutions are available to help people with communication difficulties, but they are generally not suitable for all contexts of use (e.g., lying in bed). In the scope of the “APH-ALARM” project, which aimed at developing solutions to support people with Aphasia, we envision a system for the bedroom that enables conveying messages to be sent to a caregiver or relative, for example. Focusing on gesture input, in this contribution, we investigated if smartwatch sensors and machine learning (ML) can be used to recognise arm gestures executed while lying. We explored different factors, namely the feature set, size of the sliding window used for feature extraction, and ML classifier. The results obtained with data gathered from ten subjects are promising, with the best factor combinations for the user-independent solution leading to a mean macro F1 score of 94% or 95%. They demonstrate the potential of using wearables to develop a gesture input modality for the in-bed scenario, which can also potentially be extended to other contexts (e.g., sitting in a bed, chair, or sofa, or standing). This research also provides useful insights that inform future work, including the development and deployment of communication support systems that can benefit not only people with communication difficulties (e.g., more independence), but also those caring for them (e.g., more peace of mind).
有语言障碍的人很难向他人表达自己,这导致他们的安全、独立性和总体生活质量受到严重限制。失语症是后天语言障碍的一种,影响着许多人(美国约有200万人),通常由中风引起,但也有其他脑损伤引起。有几种辅助和替代通信解决方案可用于帮助有通信困难的人,但它们通常不适用于所有使用环境(例如,躺在床上)。在“APH-ALARM”项目的范围内,旨在开发支持失语症患者的解决方案,我们设想了一个卧室系统,可以将信息发送给照顾者或亲属。专注于手势输入,在这篇论文中,我们研究了智能手表传感器和机器学习(ML)是否可以用于识别撒谎时的手臂手势。我们探索了不同的因素,即特征集、用于特征提取的滑动窗口的大小和ML分类器。从10个受试者收集的数据得到的结果是有希望的,用户独立解决方案的最佳因子组合导致平均宏观F1得分为94%或95%。他们展示了使用可穿戴设备为床上场景开发手势输入模式的潜力,这也有可能扩展到其他场景(例如,坐在床上、椅子上、沙发上或站着)。这项研究还为未来的工作提供了有用的见解,包括开发和部署通信支持系统,不仅可以使有通信困难的人受益(例如,更独立),也可以使照顾他们的人受益(例如,更安心)。
{"title":"In-bed gesture recognition to support the communication of people with Aphasia","authors":"Ana Patrícia Rocha,&nbsp;Afonso Guimarães,&nbsp;Ilídio C. Oliveira,&nbsp;José Maria Fernandes,&nbsp;Miguel Oliveira e Silva,&nbsp;Samuel Silva,&nbsp;António Teixeira","doi":"10.1016/j.pmcj.2025.102029","DOIUrl":"10.1016/j.pmcj.2025.102029","url":null,"abstract":"<div><div>People with language impairments can have difficulties expressing themselves to others, leading to major limitations to their safety, independence, and quality of life in general. Aphasia is an example of an acquired language impairment that affects many people (around 2 million in the United States), being commonly caused by stroke, but also by other brain injuries. Several augmentative and alternative communication solutions are available to help people with communication difficulties, but they are generally not suitable for all contexts of use (e.g., lying in bed). In the scope of the “APH-ALARM” project, which aimed at developing solutions to support people with Aphasia, we envision a system for the bedroom that enables conveying messages to be sent to a caregiver or relative, for example. Focusing on gesture input, in this contribution, we investigated if smartwatch sensors and machine learning (ML) can be used to recognise arm gestures executed while lying. We explored different factors, namely the feature set, size of the sliding window used for feature extraction, and ML classifier. The results obtained with data gathered from ten subjects are promising, with the best factor combinations for the user-independent solution leading to a mean macro F1 score of 94% or 95%. They demonstrate the potential of using wearables to develop a gesture input modality for the in-bed scenario, which can also potentially be extended to other contexts (e.g., sitting in a bed, chair, or sofa, or standing). This research also provides useful insights that inform future work, including the development and deployment of communication support systems that can benefit not only people with communication difficulties (e.g., more independence), but also those caring for them (e.g., more peace of mind).</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102029"},"PeriodicalIF":3.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
引用次数: 0
A novel middleware for adaptive and efficient split computing for real-time object detection 一种用于实时目标检测的自适应高效分割计算中间件
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-02-22 DOI: 10.1016/j.pmcj.2025.102028
Matteo Mendula , Paolo Bellavista , Marco Levorato , Sharon Ladron de Guevara Contreras
Real-world applications requiring real-time responsiveness frequently rely on energy-intensive and compute-heavy neural network algorithms. Strategies include deploying distributed and optimized Deep Neural Networks on mobile devices, which can lead to considerable energy consumption and degraded performance, or offloading larger models to edge servers, which requires low-latency wireless channels. Here we present Furcifer, a novel middleware that autonomously adjusts the computing strategy (i.e., local computing, edge computing, or split computing) based on context conditions. Utilizing container-based services and low-complexity predictors that generalize across environments, Furcifer supports supervised compression as a viable alternative to pure local or remote processing in real-time environments. An extensive set of experiments coversdiverse scenarios, including both stable and highly dynamic channel environments with unpredictable changes in connection quality and load. In moderate-varying scenarios, Furcifer demonstrates significant benefits: achieving a 2x reduction in energy consumption, a 30% higher mean Average Precision score compared to local computing, and a three-fold FPS increase over static offloading. In highly dynamic environments with unreliable connectivity and rapid increases in concurrent clients, Furcifer’s predictive capabilities preserves up to 30% energy, achieving a 16% higher accuracy rate, and completing 80% more frame inferences compared to pure local computing and approaches without trend forecasting, respectively.
现实世界中需要实时响应的应用经常依赖于能源密集型和计算量大的神经网络算法。策略包括在移动设备上部署分布式优化深度神经网络,这可能会导致相当大的能耗和性能下降;或者将大型模型卸载到边缘服务器,这需要低延迟无线信道。我们在此介绍一种新型中间件 Furcifer,它能根据上下文条件自主调整计算策略(即本地计算、边缘计算或分离计算)。利用基于容器的服务和可跨环境通用的低复杂度预测器,Furcifer 支持将监督压缩作为实时环境中纯本地或远程处理的可行替代方案。大量实验涵盖了各种不同的场景,包括连接质量和负载发生不可预测变化的稳定和高度动态信道环境。在中度变化的场景中,Furcifer 显示了显著的优势:与本地计算相比,能耗降低了 2 倍,平均精度分数提高了 30%,FPS 提高了三倍。在连接不可靠、并发客户端迅速增加的高动态环境中,Furcifer 的预测能力可节省多达 30% 的能源,准确率提高了 16%,与纯本地计算和无趋势预测的方法相比,完成的帧推理分别增加了 80%。
{"title":"A novel middleware for adaptive and efficient split computing for real-time object detection","authors":"Matteo Mendula ,&nbsp;Paolo Bellavista ,&nbsp;Marco Levorato ,&nbsp;Sharon Ladron de Guevara Contreras","doi":"10.1016/j.pmcj.2025.102028","DOIUrl":"10.1016/j.pmcj.2025.102028","url":null,"abstract":"<div><div>Real-world applications requiring real-time responsiveness frequently rely on energy-intensive and compute-heavy neural network algorithms. Strategies include deploying distributed and optimized Deep Neural Networks on mobile devices, which can lead to considerable energy consumption and degraded performance, or offloading larger models to edge servers, which requires low-latency wireless channels. Here we present Furcifer, a novel middleware that autonomously adjusts the computing strategy (i.e., local computing, edge computing, or split computing) based on context conditions. Utilizing container-based services and low-complexity predictors that generalize across environments, Furcifer supports supervised compression as a viable alternative to pure local or remote processing in real-time environments. An extensive set of experiments coversdiverse scenarios, including both stable and highly dynamic channel environments with unpredictable changes in connection quality and load. In moderate-varying scenarios, Furcifer demonstrates significant benefits: achieving a 2x reduction in energy consumption, a 30% higher mean Average Precision score compared to local computing, and a three-fold FPS increase over static offloading. In highly dynamic environments with unreliable connectivity and rapid increases in concurrent clients, Furcifer’s predictive capabilities preserves up to 30% energy, achieving a 16% higher accuracy rate, and completing 80% more frame inferences compared to pure local computing and approaches without trend forecasting, respectively.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"108 ","pages":"Article 102028"},"PeriodicalIF":3.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
引用次数: 0
EncCluster: Scalable functional encryption in federated learning through weight clustering and probabilistic filters EncCluster:通过权重聚类和概率过滤器在联合学习中进行可扩展的功能加密
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-02-21 DOI: 10.1016/j.pmcj.2025.102021
Vasileios Tsouvalas , Samaneh Mohammadi , Ali Balador , Tanir Ozcelebi , Francesco Flammini , Nirvana Meratnia
Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate EncCluster scalability across encryption levels. Our findings reveal that EncCluster significantly reduces communication costs — below even conventional FedAvg — and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances.
联邦学习(FL)通过与聚合服务器单独通信本地模型更新,支持跨分散设备的模型训练。尽管这种有限的数据共享使FL比集中式方法更安全,但FL在模型更新传输过程中仍然容易受到推理攻击。现有的安全聚合方法依赖于差分隐私或像功能加密(Functional Encryption, FE)这样的加密方案来保护单个客户端数据。然而,这种策略可能会降低性能,或者在资源有限的边缘设备上运行的客户端上引入不可接受的计算和通信开销。在这项工作中,我们提出了EncCluster,这是一种新颖的方法,它通过权重聚类集成模型压缩与最近的分散FE和使用概率过滤器增强隐私的数据编码,在FL中提供强大的隐私保证,而不会影响模型性能或给客户端增加不必要的负担。我们进行了全面的评估,涵盖了各种数据集和架构,以展示EncCluster跨加密级别的可扩展性。我们的研究结果表明,EncCluster显著降低了通信成本——甚至低于传统的fedag——并将加密速度提高了四倍以上;同时保持了较高的模型精度,增强了隐私保障。
{"title":"EncCluster: Scalable functional encryption in federated learning through weight clustering and probabilistic filters","authors":"Vasileios Tsouvalas ,&nbsp;Samaneh Mohammadi ,&nbsp;Ali Balador ,&nbsp;Tanir Ozcelebi ,&nbsp;Francesco Flammini ,&nbsp;Nirvana Meratnia","doi":"10.1016/j.pmcj.2025.102021","DOIUrl":"10.1016/j.pmcj.2025.102021","url":null,"abstract":"<div><div>Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present <span>EncCluster</span>, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate <span>EncCluster</span> scalability across encryption levels. Our findings reveal that <span>EncCluster</span> significantly reduces communication costs — below even conventional FedAvg — and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"108 ","pages":"Article 102021"},"PeriodicalIF":3.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
引用次数: 0
HearDrinking: Drunkenness detection and BACs predictions based on acoustic signal 听觉饮酒:基于声信号的醉酒检测和BACs预测
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-02-10 DOI: 10.1016/j.pmcj.2025.102020
Yuan Wu , Gaorong Zhao , Likairui Zhang , Xinrong Hu , Lei Ding
Alcohol poisoning is a severe health concern resulting from excessive drinking and can be life-threatening. By utilizing home monitoring, individuals can quickly determine their blood alcohol content, thus preventing it from reaching hazardous levels. However, most existing systems for drunkenness detection require extra hardware or much effort from the user, making these systems impractical for detecting drunkenness in real life. Motivated by this, we present a device-free, noise-resistant drunkenness detection system named HearDrinking based on smartphone, which utilizes microphone of smartphone to record human’s voice activity, then mine drunkenness related features to yield accurate drunkenness detection. However, using acoustic signal to detect drunkenness is non-trivial since voice activities are prone to be interfered by ambient noise, and extracting fine-grained representations related to drunkenness from voice activities remains unresolved. On one hand, HearDrinking employs a multi-modal fusion method to realize noise-resistant voice activity detection. On the other hand, HearDrinking initially calculates the log-Mel spectrograms from the speech signal. The log-Mel spectrograms contain temporal and spectral information absent in image data. Therefore, conventional convolutions designed for images often have limited effectiveness in extracting features from log-Mel spectrograms. To overcome this limitation, we integrate Omni-dimensional Dynamic Convolution (ODConv) with ShuffleNetV2, creating OD-ShuffleNetV2. ODConv replaces certain conventional convolutions in the ShuffleNetV2 network. Multiple convolution cores are fused based on the log-Mel spectrogram, taking into account multi-dimensional attention, thereby optimizing the network structure. Comprehensive experiments with 15 participants reveal drunkenness detection accuracy of 96.08% and Blood Alcohol Content (BAC) predictions with an average error of 5 mg/dl.
酒精中毒是由过量饮酒引起的严重健康问题,可能危及生命。通过使用家庭监控,个人可以快速确定他们的血液酒精含量,从而防止其达到危险水平。然而,大多数现有的醉酒检测系统需要额外的硬件或用户的大量努力,使得这些系统在现实生活中检测醉酒不现实。基于此,我们提出了一种基于智能手机的无设备、抗噪声的醉酒检测系统——HearDrinking。该系统利用智能手机的麦克风记录人的语音活动,进而挖掘醉酒相关特征,实现准确的醉酒检测。然而,由于语音活动容易受到环境噪声的干扰,并且从语音活动中提取与醉酒相关的细粒度表示仍然没有解决,因此使用声学信号来检测醉酒是非常重要的。一方面,HearDrinking采用多模态融合方法实现抗噪声的语音活动检测。另一方面,HearDrinking首先从语音信号中计算log-Mel谱图。对数mel谱图包含了图像数据中没有的时间和光谱信息。因此,为图像设计的传统卷积在从对数-梅尔谱图中提取特征方面往往效果有限。为了克服这一限制,我们将全维动态卷积(ODConv)与ShuffleNetV2集成,创建了OD-ShuffleNetV2。ODConv取代了ShuffleNetV2网络中的某些传统卷积。基于log-Mel谱图融合多个卷积核,考虑到多维关注,从而优化网络结构。15名参与者的综合实验表明,醉酒检测准确率为96.08%,血液酒精含量(BAC)预测平均误差为5 mg/dl。
{"title":"HearDrinking: Drunkenness detection and BACs predictions based on acoustic signal","authors":"Yuan Wu ,&nbsp;Gaorong Zhao ,&nbsp;Likairui Zhang ,&nbsp;Xinrong Hu ,&nbsp;Lei Ding","doi":"10.1016/j.pmcj.2025.102020","DOIUrl":"10.1016/j.pmcj.2025.102020","url":null,"abstract":"<div><div>Alcohol poisoning is a severe health concern resulting from excessive drinking and can be life-threatening. By utilizing home monitoring, individuals can quickly determine their blood alcohol content, thus preventing it from reaching hazardous levels. However, most existing systems for drunkenness detection require extra hardware or much effort from the user, making these systems impractical for detecting drunkenness in real life. Motivated by this, we present a device-free, noise-resistant drunkenness detection system named HearDrinking based on smartphone, which utilizes microphone of smartphone to record human’s voice activity, then mine drunkenness related features to yield accurate drunkenness detection. However, using acoustic signal to detect drunkenness is non-trivial since voice activities are prone to be interfered by ambient noise, and extracting fine-grained representations related to drunkenness from voice activities remains unresolved. On one hand, HearDrinking employs a multi-modal fusion method to realize noise-resistant voice activity detection. On the other hand, HearDrinking initially calculates the log-Mel spectrograms from the speech signal. The log-Mel spectrograms contain temporal and spectral information absent in image data. Therefore, conventional convolutions designed for images often have limited effectiveness in extracting features from log-Mel spectrograms. To overcome this limitation, we integrate Omni-dimensional Dynamic Convolution (ODConv) with ShuffleNetV2, creating OD-ShuffleNetV2. ODConv replaces certain conventional convolutions in the ShuffleNetV2 network. Multiple convolution cores are fused based on the log-Mel spectrogram, taking into account multi-dimensional attention, thereby optimizing the network structure. Comprehensive experiments with 15 participants reveal drunkenness detection accuracy of 96.08% and Blood Alcohol Content (BAC) predictions with an average error of 5 mg/dl.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"108 ","pages":"Article 102020"},"PeriodicalIF":3.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Climate smart computing: A perspective 气候智能计算:一个视角
IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-02-07 DOI: 10.1016/j.pmcj.2025.102019
Mingzhou Yang, Bharat Jayaprakash, Subhankar Ghosh, Hyeonjung Tari Jung, Matthew Eagon, William F. Northrop, Shashi Shekhar
Climate change is a societal grand challenge and many nations have signed the Paris Agreement (2015) aiming for net-zero emissions. The computing community has an opportunity to contribute significantly to addressing climate change across all its dimensions, including understanding, resilience, mitigation, and adaptation. Traditional computing methods face major challenges. For example, machine learning is overwhelmed due to non-stationarity (e.g., climate change), data paucity (e.g., rare climate events), the high cost of ground truth collection, and the need to observe natural laws (e.g., conservation of mass). This paper shares a perspective on a range of climate-smart computing challenges and opportunities based on multi-decade scholarly activities and acknowledges the broader societal debate on climate solutions. Moreover, it envisions advancements in computing methods specifically designed to tackle the challenges posed by climate change. It calls for a broad array of computer science strategies and innovations to be developed to address the multifaceted challenges of climate change.
气候变化是一个巨大的社会挑战,许多国家签署了旨在实现净零排放的《巴黎协定》(2015年)。计算界有机会为解决气候变化的所有方面作出重大贡献,包括了解、复原力、缓解和适应。传统的计算方法面临重大挑战。例如,由于非平稳性(如气候变化)、数据缺乏(如罕见的气候事件)、地面真相收集的高成本以及需要遵守自然规律(如质量守恒),机器学习不堪重负。本文分享了基于数十年学术活动的一系列气候智能计算挑战和机遇的观点,并承认关于气候解决方案的更广泛的社会辩论。此外,它还设想了专门为应对气候变化带来的挑战而设计的计算方法的进步。它要求开发一系列广泛的计算机科学战略和创新,以应对气候变化带来的多方面挑战。
{"title":"Climate smart computing: A perspective","authors":"Mingzhou Yang,&nbsp;Bharat Jayaprakash,&nbsp;Subhankar Ghosh,&nbsp;Hyeonjung Tari Jung,&nbsp;Matthew Eagon,&nbsp;William F. Northrop,&nbsp;Shashi Shekhar","doi":"10.1016/j.pmcj.2025.102019","DOIUrl":"10.1016/j.pmcj.2025.102019","url":null,"abstract":"<div><div>Climate change is a societal grand challenge and many nations have signed the Paris Agreement (2015) aiming for net-zero emissions. The computing community has an opportunity to contribute significantly to addressing climate change across all its dimensions, including understanding, resilience, mitigation, and adaptation. Traditional computing methods face major challenges. For example, machine learning is overwhelmed due to non-stationarity (e.g., climate change), data paucity (e.g., rare climate events), the high cost of ground truth collection, and the need to observe natural laws (e.g., conservation of mass). This paper shares a perspective on a range of climate-smart computing challenges and opportunities based on multi-decade scholarly activities and acknowledges the broader societal debate on climate solutions. Moreover, it envisions advancements in computing methods specifically designed to tackle the challenges posed by climate change. It calls for a broad array of computer science strategies and innovations to be developed to address the multifaceted challenges of climate change.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"108 ","pages":"Article 102019"},"PeriodicalIF":3.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Pervasive and Mobile Computing
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1