Pub Date : 2024-08-29DOI: 10.1109/TMC.2024.3450919
Awais Ahmed;Panlong Yang;Adeel Feroz Mirza;Taha Khan;Muhammad Rizwan;Ammar Hawbani;Miao Pan;Zhu Han
The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication. SipDeep , proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments, SipDeep demonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.
{"title":"SipDeep: Swallowing-Based Transparent Authentication via Bone-Conducted In-Ear Acoustics","authors":"Awais Ahmed;Panlong Yang;Adeel Feroz Mirza;Taha Khan;Muhammad Rizwan;Ammar Hawbani;Miao Pan;Zhu Han","doi":"10.1109/TMC.2024.3450919","DOIUrl":"10.1109/TMC.2024.3450919","url":null,"abstract":"The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication. \u0000<italic>SipDeep</i>\u0000, proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments, \u0000<italic>SipDeep</i>\u0000 demonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1109/tmc.2024.3451991
Yuchen Zhu, Min Liu, Yali Chen, Sheng Sun, Zhongcheng Li
{"title":"SkyOrbs: A Fast 3-D Directional Neighbor Discovery Algorithm for UAV Networks","authors":"Yuchen Zhu, Min Liu, Yali Chen, Sheng Sun, Zhongcheng Li","doi":"10.1109/tmc.2024.3451991","DOIUrl":"https://doi.org/10.1109/tmc.2024.3451991","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1109/TMC.2024.3450542
Xigui Wang;Haiyang Yu;Yuwen Chen;Richard O. Sinnott;Zhen Yang
In the era emphasizing the privacy of personal data, verifiable federated learning has garnered significant attention as a machine learning approach to safeguard user privacy while simultaneously validating aggregated result. However, there are some unresolved issues when deploying verifiable federated learning in edge computing. Due to the constraint resources, edge computing demands cost saving measurements in model training such as model pruning. Unfortunately, there is currently no protocol capable of enabling users to verify pruning results. Therefore, in this paper, we introduce PrVFL, a verifiable federated learning framework that supports model pruning verification and heterogeneous edge computing. In this scheme, we innovatively utilize zero-knowledge range proof protocol to achieve pruning result verification. Additionally, we first propose a heterogeneous delayed verification scheme supporting the validation of aggregated result for pruned heterogeneous edge models. Addressing the prevalent scenario of performance-heterogeneous edge clients, our scheme empowers each edge user to autonomously choose the desired pruning ratio for each training round based on their specific performance. By employing a global residual model, we ensure that every parameter has an opportunity for training. The extensive experimental results demonstrate the practical performance of our proposed scheme.
{"title":"PrVFL: Pruning-Aware Verifiable Federated Learning for Heterogeneous Edge Computing","authors":"Xigui Wang;Haiyang Yu;Yuwen Chen;Richard O. Sinnott;Zhen Yang","doi":"10.1109/TMC.2024.3450542","DOIUrl":"10.1109/TMC.2024.3450542","url":null,"abstract":"In the era emphasizing the privacy of personal data, verifiable federated learning has garnered significant attention as a machine learning approach to safeguard user privacy while simultaneously validating aggregated result. However, there are some unresolved issues when deploying verifiable federated learning in edge computing. Due to the constraint resources, edge computing demands cost saving measurements in model training such as model pruning. Unfortunately, there is currently no protocol capable of enabling users to verify pruning results. Therefore, in this paper, we introduce PrVFL, a verifiable federated learning framework that supports model pruning verification and heterogeneous edge computing. In this scheme, we innovatively utilize zero-knowledge range proof protocol to achieve pruning result verification. Additionally, we first propose a heterogeneous delayed verification scheme supporting the validation of aggregated result for pruned heterogeneous edge models. Addressing the prevalent scenario of performance-heterogeneous edge clients, our scheme empowers each edge user to autonomously choose the desired pruning ratio for each training round based on their specific performance. By employing a global residual model, we ensure that every parameter has an opportunity for training. The extensive experimental results demonstrate the practical performance of our proposed scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1109/TMC.2024.3450577
Wentong Li;Hang Li;Long Yang;Lei Qiao;Liang Shi
The prevalence of cross-device resource sharing enables users to utilize various device resources of the connected mobile devices seamlessly. Since there are often numerous connected mobile devices under the same network, cross-device tasks are often executed concurrently. However, the existing resource sharing schemes suffer from significant performance degradation for the parallel cross-device tasks due to competition for limited system resources (e.g., network and CPU). This paper first analyzes the performance penalty in parallel execution of the cross-device resource sharing tasks. Then, a novel multi-task perceiving and scheduling framework (MTPS) is proposed to guarantee the quality of service of the parallel tasks. The basic idea of MTPS is to first build a master-slave system model to reorganize mobile devices under the same network. Then, MTPS perceives the running cross-device resource sharing tasks and schedules the parallel execution of multiple tasks to avoid mutual interference. Experimental results on real devices show that MTPS can reduce the average completion time of file sharing by 63.5%, and maintain at least 24 frames per second for screen casting at optimal levels in the presence of other tasks.
{"title":"MTPS: A Multi-Task Perceiving and Scheduling Framework Across Multiple Mobile Devices","authors":"Wentong Li;Hang Li;Long Yang;Lei Qiao;Liang Shi","doi":"10.1109/TMC.2024.3450577","DOIUrl":"10.1109/TMC.2024.3450577","url":null,"abstract":"The prevalence of cross-device resource sharing enables users to utilize various device resources of the connected mobile devices seamlessly. Since there are often numerous connected mobile devices under the same network, cross-device tasks are often executed concurrently. However, the existing resource sharing schemes suffer from significant performance degradation for the parallel cross-device tasks due to competition for limited system resources (e.g., network and CPU). This paper first analyzes the performance penalty in parallel execution of the cross-device resource sharing tasks. Then, a novel multi-task perceiving and scheduling framework (MTPS) is proposed to guarantee the quality of service of the parallel tasks. The basic idea of MTPS is to first build a master-slave system model to reorganize mobile devices under the same network. Then, MTPS perceives the running cross-device resource sharing tasks and schedules the parallel execution of multiple tasks to avoid mutual interference. Experimental results on real devices show that MTPS can reduce the average completion time of file sharing by 63.5%, and maintain at least 24 frames per second for screen casting at optimal levels in the presence of other tasks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities","authors":"Liran Dong, Yiqing Zhou, Ling Liu, Yanli Qi, Yu Zhang","doi":"10.1109/tmc.2024.3450549","DOIUrl":"https://doi.org/10.1109/tmc.2024.3450549","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we advocate ${sf NetDPI}$ , a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose ${sf FIVE}$ , the first Fi ltering-plus- Ve rification algorithm tailored to programmable switches to achieve efficient multiple pattern matching (i.e., the core of DPI). Briefly, the filtering phase introduces a multi-window parallel shift-or algorithm to rapidly screen out all the “suspicious” packet payloads. Meanwhile, the verification phase innovates a level-based state encoding scheme for the Aho–Corasick (AC) algorithm, which substantially increases the number of supported patterns and consequently figures out more “guilty” payloads. We implement the prototype of ${sf NetDPI}$ in both software and hardware programmable switches (i.e., BMv2 and Barefoot Tofino2) and make them publicly available. Extensive evaluations indicate that ${sf NetDPI}$ provides orders of magnitude improvement in throughput compared to the typical cloud-delivered DPI solutions, and besides ${sf FIVE}$ greatly reduces the memory consumption compared to the alternative in-network exact match algorithms under a variety of system settings including different DPI pattern sets and malware-packet percentages.
{"title":"${sf NetDPI}$NetDPI: Efficient Deep Packet Inspection via Filtering-Plus-Verification in Programmable 5G Data Plane for Multi-Access Edge Computing","authors":"Chengjin Zhou;Qiao Xiang;Lingjun Pu;Zheli Liu;Yuan Zhang;Xinjing Yuan;Jingdong Xu","doi":"10.1109/TMC.2024.3450691","DOIUrl":"10.1109/TMC.2024.3450691","url":null,"abstract":"In this paper, we advocate \u0000<inline-formula><tex-math>${sf NetDPI}$</tex-math></inline-formula>\u0000, a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose \u0000<inline-formula><tex-math>${sf FIVE}$</tex-math></inline-formula>\u0000, the first \u0000<u>Fi</u>\u0000ltering-plus-\u0000<u>Ve</u>\u0000rification algorithm tailored to programmable switches to achieve efficient multiple pattern matching (i.e., the core of DPI). Briefly, the filtering phase introduces a multi-window parallel shift-or algorithm to rapidly screen out all the “suspicious” packet payloads. Meanwhile, the verification phase innovates a level-based state encoding scheme for the Aho–Corasick (AC) algorithm, which substantially increases the number of supported patterns and consequently figures out more “guilty” payloads. We implement the prototype of \u0000<inline-formula><tex-math>${sf NetDPI}$</tex-math></inline-formula>\u0000 in both software and hardware programmable switches (i.e., BMv2 and Barefoot Tofino2) and make them publicly available. Extensive evaluations indicate that \u0000<inline-formula><tex-math>${sf NetDPI}$</tex-math></inline-formula>\u0000 provides orders of magnitude improvement in throughput compared to the typical cloud-delivered DPI solutions, and besides \u0000<inline-formula><tex-math>${sf FIVE}$</tex-math></inline-formula>\u0000 greatly reduces the memory consumption compared to the alternative in-network exact match algorithms under a variety of system settings including different DPI pattern sets and malware-packet percentages.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/TMC.2024.3439511
Songwei Zhang;Xiaobo Zhou;Tie Qiu;Dapeng Oliver Wu
The robustness of scale-free Internet of Things (IoT) topology is seriously affected by malicious attacks. Improving the tolerance to node failures is critical to the stability of IoT systems. Heuristic algorithms, especially genetic algorithms, enhance the stability of network topology through the evolution of population chromosomes. However, the loss of genetic diversity makes the optimization easily fall into local optimum. Although the problem can be alleviated by adjusting population size and genetic probability, the genetic diversity is still not guaranteed in the limited number of iterations. Inspired by the quantum superposition that simultaneously operates on an exponential number of states, we propose a quantum-inspired robust networking model with multiverse co-evolution for the scale-free IoT (Q-Robust). This model designs quantum chromosomes with double-chain structures to represent the connections between all nodes. Then we present the quantum measurement method of quantum chromosomes based on the degree distribution of nodes. Furthermore, this model constructs a primary-secondary quantum multiverse co-evolution mechanism to improve the convergence efficiency of topology evolution. The experimental results show that the topology robustness optimized by Q-Robust is about 60% and 10% higher than the initial topology and the state-of-the-art topology evolution algorithm, respectively.
{"title":"Quantum-Inspired Robust Networking Model With Multiverse Co-Evolution for Scale-Free IoT","authors":"Songwei Zhang;Xiaobo Zhou;Tie Qiu;Dapeng Oliver Wu","doi":"10.1109/TMC.2024.3439511","DOIUrl":"10.1109/TMC.2024.3439511","url":null,"abstract":"The robustness of scale-free Internet of Things (IoT) topology is seriously affected by malicious attacks. Improving the tolerance to node failures is critical to the stability of IoT systems. Heuristic algorithms, especially genetic algorithms, enhance the stability of network topology through the evolution of population chromosomes. However, the loss of genetic diversity makes the optimization easily fall into local optimum. Although the problem can be alleviated by adjusting population size and genetic probability, the genetic diversity is still not guaranteed in the limited number of iterations. Inspired by the quantum superposition that simultaneously operates on an exponential number of states, we propose a quantum-inspired robust networking model with multiverse co-evolution for the scale-free IoT (Q-Robust). This model designs quantum chromosomes with double-chain structures to represent the connections between all nodes. Then we present the quantum measurement method of quantum chromosomes based on the degree distribution of nodes. Furthermore, this model constructs a primary-secondary quantum multiverse co-evolution mechanism to improve the convergence efficiency of topology evolution. The experimental results show that the topology robustness optimized by Q-Robust is about 60% and 10% higher than the initial topology and the state-of-the-art topology evolution algorithm, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/tmc.2024.3449818
Jing Li, Song Guo, Weifa Liang, Jianping Wang, Quan Chen, Zicong Hong, Zichuan Xu, Wenzheng Xu, Bin Xiao
{"title":"AoI-Aware Service Provisioning in Edge Computing for Digital Twin Network Slicing Requests","authors":"Jing Li, Song Guo, Weifa Liang, Jianping Wang, Quan Chen, Zicong Hong, Zichuan Xu, Wenzheng Xu, Bin Xiao","doi":"10.1109/tmc.2024.3449818","DOIUrl":"https://doi.org/10.1109/tmc.2024.3449818","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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