Pervasive and Mobile Computing最新文献

{"title":"Deep reinforcement learning based mobility management in a MEC-Enabled cellular IoT network","authors":"Homayun Kabir ,&nbsp;Mau-Luen Tham ,&nbsp;Yoong Choon Chang ,&nbsp;Chee-Onn Chow","doi":"10.1016/j.pmcj.2024.101987","DOIUrl":"10.1016/j.pmcj.2024.101987","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) has paved the way for new Cellular Internet of Things (CIoT) paradigm, where resource constrained CIoT Devices (CDs) can offload tasks to a computing server located at either a Base Station (BS) or an edge node. For CDs moving in high speed, seamless mobility is crucial during the MEC service migration from one base station (BS) to another. In this paper, we investigate the problem of joint power allocation and Handover (HO) management in a MEC network with a Deep Reinforcement Learning (DRL) approach. To handle the hybrid action space (continuous: power allocation and discrete: HO decision), we leverage Parameterized Deep Q-Network (P-DQN) to learn the near-optimal solution. Simulation results illustrate that the proposed algorithm (P-DQN) outperforms the conventional approaches, such as the nearest BS +random power and random BS +random power, in terms of reward, HO cost, and total power consumption. According to simulation results, HO occurs almost in the edge point of two BS, which means the HO is almost perfectly managed. In addition, the total power consumption is around 0.151 watts in P-DQN while it is about 0.75 watts in nearest BS +random power and random BS +random power.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101987"},"PeriodicalIF":3.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312699","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}

{"title":"Service placement strategies in mobile edge computing based on an improved genetic algorithm","authors":"Ruijuan Zheng,&nbsp;Junwei Xu,&nbsp;Xueqi Wang,&nbsp;Muhua Liu,&nbsp;Junlong Zhu","doi":"10.1016/j.pmcj.2024.101986","DOIUrl":"10.1016/j.pmcj.2024.101986","url":null,"abstract":"<div><div>In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program’s running speed.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101986"},"PeriodicalIF":3.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531087","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}

{"title":"Security protocol for securing notifications about dangerous events in the agglomeration","authors":"Sabina Szymoniak","doi":"10.1016/j.pmcj.2024.101977","DOIUrl":"10.1016/j.pmcj.2024.101977","url":null,"abstract":"<div><p>Our everyday lives cannot function without intelligent devices, which create the so-called Internet of Things networks. Internet of Things devices have various sensors and software to manage the work environment and perform specific tasks without human intervention. Internet of Things networks require appropriate security at various levels of their operation. In this article, we present a new security protocol that protects communication in IoT networks and enables interconnected devices to communicate and exchange information to increase the security of people living in urban agglomerations. The Control Station device evaluates the collected data about events that may threaten the life or health of residents and then notifies the Emergency Notification Center about it. The protocol guarantees the security of devices and transmitted data. We verified this using automatic verification technology, formal verification using Burrows, Abadi and Needham logic and informal analysis. The proposed protocol ensures mutual authentication, anonymity and revocation. Also, it is resistant to Man-in-the-middle, modification, replay and impersonation attacks. Compared to other protocols, our solution uses simple cryptographic techniques that are lightweight, stable and do not cause problems related to high communication costs. It does not require specialist equipment, so we can implement it using typical hardware. At each stage of protocol execution, communication occurs between two entities, so it does not require interaction between different entities, which may limit its adaptability in the context of interoperability.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101977"},"PeriodicalIF":3.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224001020/pdfft?md5=3cef85ebf780a1e504204af1828772d5&pid=1-s2.0-S1574119224001020-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242827","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}

{"title":"Energy-aware human activity recognition for wearable devices: A comprehensive review","authors":"Chiara Contoli,&nbsp;Valerio Freschi,&nbsp;Emanuele Lattanzi","doi":"10.1016/j.pmcj.2024.101976","DOIUrl":"10.1016/j.pmcj.2024.101976","url":null,"abstract":"<div><p>With the rapid advancement of wearable devices, sensor-based human activity recognition has emerged as a fundamental research area with broad applications in various domains. While significant progress has been made in this research field, energy consumption remains a critical aspect that deserves special attention. Recognizing human activities while optimizing energy consumption is essential for prolonging device battery life, reducing charging frequency, and ensuring uninterrupted monitoring and functionality.</p><p>The primary objective of this survey paper is to provide a comprehensive review of energy-aware wearable human activity recognition techniques based on wearable sensors without considering vision-based systems. In particular, it aims to explore the state-of-the-art approaches and methodologies that integrate activity recognition with energy management strategies. Finally, by surveying the existing literature, this paper aims to shed light on the challenges, opportunities and potential solutions for energy-aware human activity recognition.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"104 ","pages":"Article 101976"},"PeriodicalIF":3.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224001019/pdfft?md5=3a75aef0c582dd105ddf3b32830e2e31&pid=1-s2.0-S1574119224001019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148898","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}

{"title":"Accelerating the neural network controller embedded implementation on FPGA with novel dropout techniques for a solar inverter","authors":"Jordan Sturtz ,&nbsp;Kushal Kalyan Devalampeta Surendranath ,&nbsp;Maxwell Sam ,&nbsp;Xingang Fu ,&nbsp;Chanakya Dinesh Hingu ,&nbsp;Rajab Challoo ,&nbsp;Letu Qingge","doi":"10.1016/j.pmcj.2024.101975","DOIUrl":"10.1016/j.pmcj.2024.101975","url":null,"abstract":"<div><p>Accelerating neural network (NN) controllers is important for improving the performance, efficiency, scalability, and reliability of real-time systems, particularly in resource-constrained embedded systems. This paper introduces a novel weight-dropout method for training neural network controllers in real-time closed-loop systems, aimed at accelerating the embedded implementation for solar inverters. The core idea is to eliminate small-magnitude weights during training, thereby reducing the number of necessary connections while ensuring the network’s convergence. To maintain convergence, only non-diagonal elements of the weight matrices were dropped. This dropout technique was integrated into the Levenberg–Marquardt and Forward Accumulation Through Time algorithms, resulting in more efficient training for trajectory tracking. We executed the proposed training algorithm with dropout on the AWS cloud, observing a performance increase of approximately four times compared to local execution. Furthermore, implementing the neural network controller on the Intel Cyclone V Field Programmable Gate Array (FPGA) demonstrates significant improvements in computational and resource efficiency due to the proposed dropout technique leading to sparse weight matrices. This optimization enhances the suitability of the neural network controller for embedded environments. In comparison to Sturtz et al. (2023), which dropped 11 weights, our approach eliminated 18 weights, significantly boosting resource efficiency. This resulted in a 16.40% reduction in Adaptive Logic Modules (ALMs), decreasing the count to 47,426.5. Combinational Look-Up Tables (LUTs) and dedicated logic registers saw reductions of 17.80% and 15.55%, respectively. However, the impact on block memory bits is minimal, showing only a 1% improvement, indicating that memory resources are less affected by weight dropout. In contrast, the usage of Memory 10 Kilobits (MK10s) dropped from 97 to 87, marking a 10% improvement. We also propose an adaptive dropout technique to further improve the previous results.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"104 ","pages":"Article 101975"},"PeriodicalIF":3.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040615","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}

{"title":"Edge human activity recognition using federated learning on constrained devices","authors":"Angelo Trotta ,&nbsp;Federico Montori ,&nbsp;Leonardo Ciabattini ,&nbsp;Giulio Billi ,&nbsp;Luciano Bononi ,&nbsp;Marco Di Felice","doi":"10.1016/j.pmcj.2024.101972","DOIUrl":"10.1016/j.pmcj.2024.101972","url":null,"abstract":"<div><p>Human Activity Recognition (HAR) using wearable Internet of Things (IoT) devices represents a well investigated researched field encompassing various application domains. Many current approaches rely on cloud-based methodologies for gathering data from diverse users, resulting in the creation of extensive training datasets. Although this strategy facilitates the application of powerful Machine Learning (ML) techniques, it raises significant privacy concerns, which can become particularly severe given the sensitivity of HAR data. Moreover, the labeling process can be extremely time-consuming and even more challenging for IoT wearable devices due to the absence of efficient input systems. In this paper, we address both aforementioned challenges by designing, implementing, and validating edge-based Human Activity Recognition (HAR) systems that operate on resource-constrained IoT devices, which relies on the utilization of Self-Organizing Maps (SOM) for activity detection. We incorporate a feature selection process before training to reduce data dimensionality and, consequently, the SOM size, aligning with the resource limitations of wearable IoT devices. Additionally, we explore the application of Federated Learning (FL) techniques for HAR tasks, enabling new users to leverage SOM models trained by others on their respective datasets. Our federated Extreme Edge (EE)-aware HAR system is implemented on a wearable IoT device and rigorously tested against state-of-the-art and experimental datasets. The results demonstrate that our C++-based SOM implementation achieves a consistent reduction in model size compared to state-of-the-art approaches. Furthermore, our findings highlight the effectiveness of the FL-based approach in overcoming personalized training challenges, particularly in onboarding scenarios.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"104 ","pages":"Article 101972"},"PeriodicalIF":3.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S157411922400097X/pdfft?md5=00c8c39b201e7dc11581a2c5474e3422&pid=1-s2.0-S157411922400097X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001976","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}

{"title":"Enhancing 5G network slicing for IoT traffic with a novel clustering framework","authors":"Ziran Min ,&nbsp;Swapna Gokhale ,&nbsp;Shashank Shekhar ,&nbsp;Charif Mahmoudi ,&nbsp;Zhuangwei Kang ,&nbsp;Yogesh Barve ,&nbsp;Aniruddha Gokhale","doi":"10.1016/j.pmcj.2024.101974","DOIUrl":"10.1016/j.pmcj.2024.101974","url":null,"abstract":"<div><p>The current extensive deployment of IoT devices, crucial for enhancing smart computing applications in diverse domains, necessitates the utilization of essential 5G features, notably network slicing, to ensure the provision of distinct and reliable services. However, the voluminous, dynamic, and varied nature of IoT traffic introduces complexities in network flow classification, traffic analysis, and the accurate determination of network requirements. These complexities pose a significant challenge in effectively provisioning 5G network slices across various applications. To address this, we propose an innovative approach for network traffic classification, comprising a pipeline that integrates Principal Component Analysis (PCA) with KMeans clustering and the Hellinger distance measure. The application of PCA as the initial step effectively reduces the dimensionality of the data while retaining most of the original information, which significantly lowers the computational demands for the subsequent KMeans clustering phase. KMeans, an unsupervised learning method, eliminates the labor-intensive and error-prone process of data labeling. Following this, a Hellinger distance-based recursive KMeans algorithm is employed to merge similar clusters, aiding in the determination of the optimal number of clusters. This results in final clustering outcomes that are both compact and intuitively interpretable, overcoming the inherent limitations of the traditional KMeans algorithm, such as its sensitivity to initial conditions and the requirement for manually specifying the number of clusters. An evaluation of our method using a real-world IoT dataset has shown that our pipeline can efficiently represent the dataset in three distinct clusters. The characteristics of these clusters can be readily understood and directly correlated with various types of network slices in the 5G network, demonstrating the efficacy of our approach in managing the complexities of IoT traffic for 5G network slice provisioning.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"104 ","pages":"Article 101974"},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000993/pdfft?md5=35a9df2be399c91dc234552871f71dd2&pid=1-s2.0-S1574119224000993-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001958","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}

{"title":"A stable and efficient dynamic ensemble method for pothole detection","authors":"Hiroo Bekku,&nbsp;Taiga Kume,&nbsp;Akira Tsuge,&nbsp;Jin Nakazawa","doi":"10.1016/j.pmcj.2024.101973","DOIUrl":"10.1016/j.pmcj.2024.101973","url":null,"abstract":"<div><p>Roads can develop potholes over time, posing hazards to traffic. However, regular road damage inspections is challenging due to the high cost of road surveys. By applying object detection models on footage acquired from dashboard cameras installed in garbage trucks that operate across the city, we can conduct road surveys at a low cost. In our previous work we introduced the Ensemble of Classification Mechanisms (ECM), which suppresses false positives by cross-verifying objects detected by an object detection model using a different image classification model. However, ECM faces challenges in achieving both fast inference speed and high detection performance simultaneously. It also struggles in environments where roads vary in their suitability for false positive suppression. To address these issues, we propose the Dynamic Ensemble of Classification Mechanisms (DynamicECM). This approach utilizes ECM selectively, enabling stable inference with minimal false positive suppression. To evaluate our new method, we constructed an evaluation dataset comprising objects that cause false positives in pothole detection. Our experiments demonstrate that ECM achieves higher precision, average precision (AP), and F1 scores compared to existing methods. Furthermore, DynamicECM improves the trade-off between speed and detection performance, outperforming ECM, and achieves stable inference even in challenging datasets where ECM would falter. Our method is highly scalable and expected to contribute to the stability and efficiency of inference across various object detection models. In our previous work we developed an Ensemble of Classification Mechanisms (ECM), which suppresses false positives by rechecking objects detected by an object detector with a different image classification model. However, ECM cannot achieve both fast inference speed and high detection performance at the same time. It also struggles in environments that have a mixture of roads suitable for false positive suppression and unsuited for false positive suppression. To solve these problems, we propose “Dynamic Ensemble of Classification Mechanisms”. Since this method uses ECM only when deemed necessary, stable inference can be achieved efficiently without excessive suppression of false positives. In order to evaluate our new method, we constructed an evaluation dataset that includes objects that cause false positives in pothole detection. Our evaluation experiments show that ECM achieves higher precision, AP, and F1 compared to existing methods. In addition, DynamicECM improves the trade-off between speed and detection performance better than ECM, and achieves stable inference on datasets that would ECM would struggle on. Our method is highly scalable and expected to contribute to the stability and efficiency of inference for various object detection models.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"104 ","pages":"Article 101973"},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049781","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}

{"title":"GTDIM: Grid-based Two-stage Dynamic Incentive Mechanism for Mobile Crowd Sensing","authors":"Xin-Wei Yao ,&nbsp;Wei-Wei Xing ,&nbsp;Ke-Chen Zheng ,&nbsp;Chu-Feng Qi ,&nbsp;Xiang-Yang Li ,&nbsp;Qi Song","doi":"10.1016/j.pmcj.2024.101964","DOIUrl":"10.1016/j.pmcj.2024.101964","url":null,"abstract":"<div><p>Mobile Crowd Sensing (MCS) technology, as an emerging data collection paradigm, offers distinct advantages, particularly in applications like smart city management. However, existing researches inadequately address the comprehensive solution to the problem of reliable task allocation according to the requirements such as task budget, sensory data quality, and real-time data collection, especially under varying participant engagement in MCS systems. To bridge this gap, we propose the Grid-based Two-stage Dynamic Incentive Mechanism (GTDIM). In the first stage, the Candidate Participant Set (CPS) establishment phase, participants receive compensation for collecting sensory data when a sufficient number are available. When participants are insufficient, additional rewards inspired by the grid division of sensing areas are progressively offered to attract more participants. In the subsequent stage, utilizing the established CPS, participants are selected through a greedy algorithm based on the newly devised Participant Matching Index (PMI), which integrates various participant features. Extensive simulation results reveal the impact of PMI on participant selection. Numerical findings conclusively demonstrate GTDIM’s superior performance over baseline incentive mechanisms in terms of task assignment ratio, participant payment, and especially when dealing with larger sensing tasks.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101964"},"PeriodicalIF":3.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711433","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}

{"title":"WiCAR: A class-incremental system for WiFi activity recognition","authors":"Zhihua Li ,&nbsp;Shuli Ning ,&nbsp;Bin Lian ,&nbsp;Chao Wang ,&nbsp;Zhongcheng Wei","doi":"10.1016/j.pmcj.2024.101963","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101963","url":null,"abstract":"<div><p>The proposal of Integrated Sensing and Communications has once again drawn researchers’ attention to WiFi sensing, propelling applications based on WiFi sensing into an advanced stage. However, the current field of activity recognition only identifies fixed categories of activities, neglecting the growing demand for perceiving activity types in real applications over time. In response to the issue, we present WiCAR, a WiFi activity recognition system designed for class incremental scenarios. WiCAR takes antenna array-fused image data as input, employing the Wi-RA model with parallel stacked activation functions as its backbone network. To alleviate the typical catastrophic forgetting issue in class-incremental learning, WiCAR employs a strategy of replaying known data. Additionally, we adopts knowledge distillation to improve accuracy among old samples during the incremental process. To tackle the imbalance in the number of samples between old and new classes, the model is updated through weight alignment. This serious of strategies endows the system with the capability to progressively learn and handle new classes. We conducted extensive experiments to evaluate the system performance. The experimental results demonstrate that our system exhibits excellent performance regardless of the number of tasks, whether tasks are uniform or non-uniform, and the order of task arrivals. The highest average accuracy reaches 96.429%, and even in the presence of six incremental stages, the average accuracy remains at 92.867%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101963"},"PeriodicalIF":3.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540006","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}