IEEE Transactions on Mobile Computing最新文献

{"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":"23 12","pages":"14171-14185"},"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}

{"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":"10.1109/TMC.2024.3451991","url":null,"abstract":"Neighbor discovery (ND) is a critical network initialization stage, particularly challenging for highly-dynamic unmanned aerial vehicle (UAVs) with directional antennas. Considering that directional antennas focus signal energy in one direction, successful ND requires a pair of UAVs to point antennas towards each other simultaneously. However, due to the inherent constraints of autonomous UAVs (e.g., high mobility and decentralized coordination), spatial alignment of directional beams is difficult. Existing works resort to ideal assumptions (e.g., clock synchronization, assistance of omni-directional antennas and prior information) for simplification. Moreover, previous ND algorithms assume unlimited switching capability for directional antennas, often unrealistic for traditional mechanically steered antennas. In this paper, we propose \u0000<italic>SkyOrbs</i>\u0000, a fast directional ND algorithm for UAV networks without these ideal assumptions. To reduce ND latency, \u0000<italic>SkyOrbs</i>\u0000 presents a skip scanning strategy, dynamically adjusting antenna rotation speed to enhance discovery probability. Furthermore, to mitigate the uncertain rotation overhead induced by time-variant angular speed, \u0000<italic>SkyOrbs</i>\u0000 designs a novel antenna scanning path that accommodates limited mechanical rotation capacity. We analyze the theoretical delay performance of \u0000<italic>SkyOrbs</i>\u0000, and expand its applicability to broader scenarios. Evaluation results show that \u0000<italic>SkyOrbs</i>\u0000 can reduce discovery latency by 40.8% and rotation overhead by 55.0% compared to the baseline method.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14768-14786"},"PeriodicalIF":7.7,"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}

{"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":"23 12","pages":"15062-15079"},"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}

{"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":"23 12","pages":"15048-15061"},"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":"Truthful Auction Mechanisms for Dependent Task Offloading in Vehicular Edge Computing","authors":"Hualing Ren;Kai Liu;Guozhi Yan;Chunhui Liu;Yantao Li;Chuzhao Li;Weiwei Wu","doi":"10.1109/TMC.2024.3450504","DOIUrl":"10.1109/TMC.2024.3450504","url":null,"abstract":"This work investigates the truthful auction for dependent task offloading in vehicular edge computing by considering the selfishness and rationality of participating nodes. Specifically, we first illustrate a truthfulness-guaranteed dependent task offloading architecture. Then, we formulate the Truthfulness-Guaranteed Dependent Task Offloading problem, aiming at maximizing the system utility (SU) while ensuring truthfulness and individual rationality in dynamic environments. Further, we design both centralized and distributed auction mechanisms to derive the optimal and approximate solutions, respectively. For centralized auction mechanism, we adopt the branch-and-price algorithm to determine the offloaded nodes, which yields maximum SU. Then, we adopt VCG mechanism to determine the payment of buyers. For distributed auction mechanism, each seller independently chooses the winning bid, and the buyer greedily chooses the offloaded node with maximum utility. Then, a novel payment mechanism regarding the cost of failed buyers is designed to guarantee the truthfulness and individual rationality. Finally, we build the simulation model and conduct the performance evaluation based on realistic vehicular trajectories. The results demonstrate that the proposed distributed auction mechanism achieves performance within approximately 4% of the optimal method, while significantly reducing computational complexity. Additionally, it significantly outperforms other methods in terms of system utility across various task requirements.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14987-15002"},"PeriodicalIF":7.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180573","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":"${sf NetDPI}$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":"23 12","pages":"15031-15047"},"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}

{"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":"10.1109/TMC.2024.3450549","url":null,"abstract":"Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14934-14945"},"PeriodicalIF":7.7,"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}

{"title":"Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering","authors":"Yinqiu Liu;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Shiwen Mao;Ping Zhang;Xuemin Shen","doi":"10.1109/TMC.2024.3449645","DOIUrl":"10.1109/TMC.2024.3449645","url":null,"abstract":"Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal \u0000<underline>G</u>\u0000enerative \u0000<underline>Sem</u>\u0000antic \u0000<underline>Com</u>\u0000munications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14871-14888"},"PeriodicalIF":7.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180582","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":"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":"23 12","pages":"14085-14098"},"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}

{"title":"PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles","authors":"Zhengru Fang;Senkang Hu;Haonan An;Yuang Zhang;Jingjing Wang;Hangcheng Cao;Xianhao Chen;Yuguang Fang","doi":"10.1109/TMC.2024.3449371","DOIUrl":"10.1109/TMC.2024.3449371","url":null,"abstract":"Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novel \u0000<underline>P</u>\u0000riority-\u0000<underline>A</u>\u0000ware \u0000<underline>C</u>\u0000ollaborative \u0000<underline>P</u>\u0000erception (\u0000<bold>PACP</b>\u0000) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27% and 13.60%, respectively, in terms of utility and precision of the Intersection over Union.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15003-15018"},"PeriodicalIF":7.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180578","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}