Pervasive and Mobile Computing最新文献

MicroFallNet: A lightweight model for real-time fall detection on smart wristbands

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-04-01 DOI: 10.1016/j.pmcj.2025.102046

Jun Hu , Feiyan Cheng , Meng Liu , Xuanle Xu , Xiaojing Li

{"title":"MicroFallNet: A lightweight model for real-time fall detection on smart wristbands","authors":"Jun Hu , Feiyan Cheng , Meng Liu , Xuanle Xu , Xiaojing Li","doi":"10.1016/j.pmcj.2025.102046","DOIUrl":"10.1016/j.pmcj.2025.102046","url":null,"abstract":"<div><div>Falls are a major public health concern for the aging population, leading to significant injuries, loss of independence, and increased healthcare costs. While wearable devices present promising solutions, existing algorithms are often hindered by the limitations of microcontroller units (MCU) in terms of computational power, memory, and energy consumption. To overcome these challenges, we introduce MicroFallNet, a lightweight convolutional neural network designed for accurate and efficient fall detection. MicroFallNet features a novel FireModel architecture, incorporating Squeeze and Expand layers to optimize computational efficiency and enhance feature extraction. The proposed algorithm demonstrates superior performance on the UMAFALL and FallAllD datasets, achieving geometric mean accuracies of 97.91 % and 97.86 %, respectively, significantly surpassing traditional methods. Additionally, MicroFallNet showcases excellent deployment efficiency across various microcontrollers, particularly excelling on the ESP32 smart wristband platform, where it achieves an inference time of just 30.3 milliseconds. This capability makes MicroFallNet ideally suited for real-time fall detection applications, advancing the development of wearable devices for the elderly and contributing substantially to the field of smart health monitoring. Our code will be publicly available at <span><span>https://github.com/qwer12330/MicroFallNet-A-Lightweight-Model-for-Real-Time-Fall-Detection-on-Smart-Wristbands-Using-Sm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102046"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759571","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

Distributed fault detection in sparse wireless sensor networks utilizing simultaneous likelihood ratio statistics

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-03-31 DOI: 10.1016/j.pmcj.2025.102043

Bhabani Sankar Gouda , Trilochan Panigrahi , Sudhakar Das , Meenakshi Panda , Linga Reddy Cenkeramaddi

{"title":"Distributed fault detection in sparse wireless sensor networks utilizing simultaneous likelihood ratio statistics","authors":"Bhabani Sankar Gouda , Trilochan Panigrahi , Sudhakar Das , Meenakshi Panda , Linga Reddy Cenkeramaddi","doi":"10.1016/j.pmcj.2025.102043","DOIUrl":"10.1016/j.pmcj.2025.102043","url":null,"abstract":"<div><div>Sensor nodes in wireless sensor networks (WSNs) for several remote applications are deployed in harsh environments and are coupled with low-cost components. Because of these factors, sensor nodes are becoming faulty, resulting in serious data inaccuracy in the network if not diagnosed in a timely manner. The current approaches to centralized or distributed fault detection algorithms are based on statistical methods or machine learning algorithms. Statistical methods require more data to achieve the desired detection accuracy and may be impractical for sparse networks. Machine learning approaches are computationally demanding. We know that the mean and variance of data from a faulty node differ from those from a healthy node. As a result, simultaneous likelihood ratio statistics are proposed here to determine the sensor node’s fault status in WSNs. The proposed hybrid method, in which the faulty status of the node connected to the anchor node is diagnosed by the anchor node, assumes that the anchor node has statistics for all connected nodes. During the diagnosis time, the simultaneous likelihood ratio statistics (SLRS) are computed using the data received by the anchor node over a specific time period. The fault status is determined by comparing the likelihood ratio to a predetermined threshold based on the tolerance limit. The algorithm’s performance is determined and compared to state-of-the-art algorithms using real-time measured data from the literature.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102043"},"PeriodicalIF":3.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785287","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

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

Pervasive and Mobile Computing Pub Date : 2025-03-24 DOI: 10.1016/j.pmcj.2025.102031

Sumit Kumar , Rajeev Tiwari

{"title":"Navigating transient content: PFC caching approach for NDN-based IoT networks","authors":"Sumit Kumar , 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

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-03-22 DOI: 10.1016/j.pmcj.2025.102040

Quyuan Wang , Pengyang Chen , Jiadi Liu , Ying Wang , Zhiwei Guo

{"title":"Investment-driven budget allocation and dynamic pricing strategies in edge cache network","authors":"Quyuan Wang , Pengyang Chen , Jiadi Liu , Ying Wang , 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

IF 3 3区计算机科学

Pervasive and Mobile Computing 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

{"title":"EPCM: Efficient privacy-preserving charging matching scheme with data integrity for electric vehicles","authors":"Tingting Jin, Peng Hu, Kaizhong Zuo, Tianjiao Ni, Dong Xie, Zhangyi Shen, 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

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-03-16 DOI: 10.1016/j.pmcj.2025.102041

Zaineb Naaz , Gamini Joshi , Vidushi Sharma

引用次数: 0

Bio-inspired recruiting strategies for on-demand connectivity over a multi-layer hybrid CubeSat-UAV networks in emergency scenarios

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-03-10 DOI: 10.1016/j.pmcj.2025.102030

Mauro Tropea , Alex Ramiro Masaquiza Caiza , Floriano De Rango

{"title":"Bio-inspired recruiting strategies for on-demand connectivity over a multi-layer hybrid CubeSat-UAV networks in emergency scenarios","authors":"Mauro Tropea , Alex Ramiro Masaquiza Caiza , 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

IF 3 3区计算机科学

Pervasive and Mobile Computing 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

{"title":"In-bed gesture recognition to support the communication of people with Aphasia","authors":"Ana Patrícia Rocha, Afonso Guimarães, Ilídio C. Oliveira, José Maria Fernandes, Miguel Oliveira e Silva, Samuel Silva, 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

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-02-22 DOI: 10.1016/j.pmcj.2025.102028

Matteo Mendula , Paolo Bellavista , Marco Levorato , Sharon Ladron de Guevara Contreras

{"title":"A novel middleware for adaptive and efficient split computing for real-time object detection","authors":"Matteo Mendula , Paolo Bellavista , Marco Levorato , 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

Pervasive and Mobile Computing Pub Date : 2025-02-21 DOI: 10.1016/j.pmcj.2025.102021

Vasileios Tsouvalas , Samaneh Mohammadi , Ali Balador , Tanir Ozcelebi , Francesco Flammini , Nirvana Meratnia

{"title":"EncCluster: Scalable functional encryption in federated learning through weight clustering and probabilistic filters","authors":"Vasileios Tsouvalas , Samaneh Mohammadi , Ali Balador , Tanir Ozcelebi , Francesco Flammini , 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