Pervasive and Mobile Computing最新文献

A survey of wearable devices to capture human factors for human-robot collaboration

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-04-21 DOI: 10.1016/j.pmcj.2025.102048

Hooman Sarvghadi , Andreas Reinhardt , Esther A. Semmelhack

Technology has rapidly evolved over the course of the last decades, and drastically transformed our way of life. Robots are no longer just mechanical aides, but have become collaborators on many tasks. Wearable gadgets have become virtually ubiquitous due to their ability to collect data, monitor health parameters, and assist users in various day-to-day tasks. In recent years, there has been a surge in interest around the use of wearable technologies to collect human psychological parameters for human–robot collaboration. With the field of robotics advancing, there is a growing need for robots to interact with humans seamlessly. To achieve this seamless human–robot connection, robots must be able to interpret human emotions and react appropriately. While understanding human emotions and behavior is a complex task in itself, wearable sensor systems contribute valuable insights. This survey provides a comprehensive overview of wearable gadgets and technologies proposed for measuring five key human factors — trust, cognitive workload, stress, safety perception, and fatigue — within the scope of human–robot collaboration, based on the systematic review of papers published between 2015 and the end of 2024 in six major databases. Our analysis indicates that trust and cognitive workload have received greater attention from researchers in recent years, as compared to other human factors. The Empatica E4 wristband, Shimmer3 GSR+ and EPOC X EEG headset are among the most widely used wearable devices, capable of capturing essential physiological parameters widely used for human–robot collaboration, including electrodermal activity, heart rate variability, skin temperature, and electroencephalogram. Besides reviewing the potentials and capabilities of these gadgets, we highlight their shortcomings and offer directions for future research in this domain.

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引用次数: 0

Contrastive-representation IMU-based fitness activity recognition enhanced by bio-impedance sensing

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-04-12 DOI: 10.1016/j.pmcj.2025.102047

Mengxi Liu, Vitor Fortes Rey, Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz

While IMU-based Human Activity Recognition (HAR) has achieved significant success in wearable and pervasive computing areas over the past decade, the potential for further improvement of IMU-based HAR performance through the contrastive representation method enhanced by other sensing modalities remains underexplored. In this work, we propose a contrastive representation learning framework to demonstrate that bio-impedance can enhance IMU-based fitness activity recognition beyond the common sensor fusion method, which requires all sensing modalities to be available during both training and inference phases. Instead, in our proposed framework, only the target sensing modality (IMU) is required at inference time. To evaluate our method, we collected both IMU and bio-impedance sensing data through an experiment involving ten subjects performing six types of upper-body and four kinds of lower-body exercises over five days. The bio-impedance-alone classification model achieved an average Macro F1 score of 75.49% and 71.57% for upper-body and lower-body fitness activities, respectively, which was lower than that of the IMU-alone model (83.10% and 78.61%). However, with our proposed method, significant performance improvement (2.66% for upper-body activities and 3.2% for lower-body activities) was achieved by the IMU-only classification model. This improvement leverages the contrastive representation learning framework, where the information from bio-impedance sensing guides the training procedure of the IMU-only model. The results highlight the potential of contrastive representation learning as a valuable tool for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.

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引用次数: 0

A black-box assessment of authentication and reliability in consumer IoT devices

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-04-08 DOI: 10.1016/j.pmcj.2025.102045

Sara Lazzaro , Vincenzo De Angelis , Anna Maria Mandalari , Francesco Buccafurri

In the context of consumer Internet of Things (IoT) devices, the identification of vulnerabilities is becoming increasingly relevant. In this paper, we propose a scalable black-box assessment methodology for identifying authentication and reliability issues in IoT devices without the need for prior knowledge of device models or communication protocols. Our methodology consists of a suite of five black-box tests focusing on two specific aspects: authentication and reliability. One of these tests required the development of a tool, called REPLIOT, specifically aimed at discovering replay attacks on the local network. To the best of our knowledge, the development of such a tool is a significant contribution, as there was no similar tool previously available in the literature. We applied these tests to a testbed consisting of 51 consumer IoT devices. Our experiments reveal that 88% of the tested devices fail at least one of the proposed tests. Further manual investigation reveals severe implications of these results in terms of privacy, security, and reliability. Our findings underline a strong need to improve consumer IoT devices security practices to minimize these potential risks and protect smart home environments.

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引用次数: 0

Optimized secure and energy-efficient approach for IoT-enabled wireless sensor networks

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-04-07 DOI: 10.1016/j.pmcj.2025.102049

Jay Kumar Jain , Dipti Chauhan

Wireless communication is pivotal in the modern era, enabling seamless connectivity across diverse applications. However, the increasing complexity and sophistication of cyber threats pose significant challenges to the security of wireless communication systems. This paper proposes an innovative approach to enhance wireless communication security through integrating artificial intelligence (AI) techniques. First, we construct the network using the Horizontal Partitioning Sierpinski Triangle to reduce the network's high traffic and perform the authentication process. After successful authentication, we perform the clustering process and Game Theory-Driven Clustering (GT-DC) allows nodes to strategically optimize energy utilization while forming clusters as rational entities in a cooperative game. Perform the beacon injection and detect the attacks using the Improved Random Forest (IRF) that signifies the accurate identification of cyber-attacks, IRF is improving the Bootstrap Sampling, Class Weights, and Anomaly Score Threshold. In Routing implement Improved Cache LEACH Protocol (ICLP) which discovers the effective routing establishing the Cache nodes (Cn), to obtain optimal routing by lowering latency, improving data access, enhancing data reliability, and reducing data redundancy. The proposed work is compared with evaluation metrics such as authentication time, throughput, attack detection rate, energy consumption, packet delivery rate, and delay.

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引用次数: 0

Continual learning in sensor-based human activity recognition with dynamic mixture of experts

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-04-04 DOI: 10.1016/j.pmcj.2025.102044

Fahrurrozi Rahman, Martin Schiemer, Andrea Rosales Sanabria, Juan Ye

Human activity recognition (HAR) is a key enabler for many applications in healthcare, factory automation, and smart home. It detects and predicts human behaviours or daily activities via a range of wearable sensors or ambient sensors embedded in an environment. As more and more HAR applications are deployed in the real-world environments, there is a pressing need for the ability of continually and incrementally learning new activities over time without retraining the HAR model. Recently, various continual learning techniques have been applied to HAR; however, most of them commit to a large architecture, which might not suit to devices that deploy HAR models. In addition, these techniques often require to deploy the same large architecture on the devices and cannot customise the architecture for different requirements. To tackle this challenge, we present a dynamic mixture-of-experts approach, which grows an expert for each new task and allows flexible composition of experts to suit individual needs of applications. We have empirically evaluated our technique on 4 third-party, publicly available datasets and compared with 11 state-of-the-art continual learning techniques. Our results demonstrate that our technique can achieve better or comparable performance but with much less parameter spaces and training time.

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引用次数: 0

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

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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 https://github.com/qwer12330/MicroFallNet-A-Lightweight-Model-for-Real-Time-Fall-Detection-on-Smart-Wristbands-Using-Sm.

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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.

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

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.

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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。

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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。

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引用次数: 0