Pervasive and Mobile Computing最新文献

{"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}

{"title":"Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning","authors":"Rukhsar Sultana,&nbsp;Jyoti Grover,&nbsp;Meenakshi Tripathi","doi":"10.1016/j.pmcj.2024.101962","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101962","url":null,"abstract":"<div><p>Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101962"},"PeriodicalIF":3.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480439","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":"Localizing unknown nodes with an FPGA-enhanced edge computing UAV in wireless sensor networks: Implementation and evaluation","authors":"Rahma Mani ,&nbsp;Antonio Rios-Navarro ,&nbsp;Jose Luis Sevillano Ramos ,&nbsp;Noureddine Liouane","doi":"10.1016/j.pmcj.2024.101961","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101961","url":null,"abstract":"<div><p>Great interest is directed toward real-time applications to determine the exact location of sensor nodes deployed in an area of interest. In this paper, we present a novel approach using a combination of the Kalman filter and regularized bounding box method for localizing unknown nodes in an area using an FPGA-enhanced edge computing UAV whose trajectory is known and is represented as the position of many anchors. The UAV is equipped with a GPS system that allows it to gather location data of sensor nodes as it moves around its environment. We employ a regularized bounding box to predict the positions of the unknown nodes using regularization factors and we use the Kalman filter algorithm to smooth and improve the accuracy of the sensor nodes to be localized. In order to localize the unknown nodes, the UAV receives the number of hops from each node and uses this information as input to the localization algorithm. Furthermore, the use of an FPGA board allows for real-time processing of sensory data, enabling the UAV to make fast and accurate decisions in dynamic environments. The localization algorithm was implemented on the FPGA board “Zynq MiniZed 7007s evaluation board” using Xilinx blocks in Simulink, and the generated code was converted into VHDL using Xilinx System Generator. The algorithm was simulated and synthesized using “Vivado” software. In fact, the proposed system was evaluated by comparing the performances achieved through two different implementations: Hardware and Software implementation. In effect, the performance of FPGA hardware implementation presents a new achievement in localization due to its easy testing and fast implementation. Our results show that this approach can efficiently locate unknown nodes with good latency and high accuracy. In fact, the execution time of the FPGA-integrated algorithm is reduced by about 60 times compared to the software implementation and the power consumption is about 100 mW, which proves the suitability of FPGA for localization in WSNs, offering a promising solution for various mobile WSN applications.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101961"},"PeriodicalIF":3.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000865/pdfft?md5=4248b9a002154820ef25d24827509c64&pid=1-s2.0-S1574119224000865-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479544","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":"Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network","authors":"Simran Chaudhary,&nbsp;Fatema Kapadia,&nbsp;Avinesh Singh,&nbsp;Nidhi Kumari,&nbsp;Prasanta K. Jana","doi":"10.1016/j.pmcj.2024.101960","DOIUrl":"10.1016/j.pmcj.2024.101960","url":null,"abstract":"<div><p>Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101960"},"PeriodicalIF":4.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141398460","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":"IoT data encryption and phrase search-based efficient processing using a Fully Homomorphic-based SE (FHSE) scheme","authors":"S. Hamsanandhini,&nbsp;P. Balasubramanie","doi":"10.1016/j.pmcj.2024.101952","DOIUrl":"10.1016/j.pmcj.2024.101952","url":null,"abstract":"<div><p>In this study, the Efficient Multikeyword Fully Homomorphic Search Encryption (EMK-FHSE) model is proposed to improve cloud storage security for sensitive data. When fully homomorphic encryption (FHE) and search encryption (SE) technologies are coupled, Fully Homomorphic Search Encryption (FHSE) is a strategy that realizes the shared information's controlled privacy and search security. As more and more encrypted data is kept on cloud servers (CSs), a single-keyword SE approach may cause multiple keyword index duplication concerns, making it challenging for CSs to search for the encrypted information. To reduce these problems, a novel efficiency bottleneck has been developed. An Adaptive Privacy-Preserving Fuzzy Multi-Keyword Search (APPFMK) approach is presented to address the difficulties of low search effectiveness in a single-keyword searching strategy and the high processing cost of the existing multi-keyword schemes. Cloud servers (CS) hold enormous volumes of encrypted data, and the necessary encrypted index is transmitted to the closest edge node (EN) to enable multi-keyword searches and supported decryption. According to security research, the EMK-FHSE multi-keyword index is safe in distinguishability under chosen keyword attacks. The results section compares the proposed model's search, storage, trapdoor, calculation, storage and validation times to those of several other models. The proposed model could achieve the following values: 60.81 kb for storage, 10.92 for the trapdoor, 6.85 ms for search, 0.44 ms for computation cost by changing the keyword in a trapdoor, 156.31 ms for computation cost by changing the keyword in a dictionary, 0.44 kb for storage cost by changing the keyword in a trapdoor, 1.81 kb for storage cost by changing the keyword in a dictionary and 0.016seconds for verification time, respectively.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101952"},"PeriodicalIF":3.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141399409","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}