IEEE Transactions on Mobile Computing最新文献

{"title":"Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?","authors":"Jiajie Li;Bo Gu;Shimin Gong;Zhou Su;Mohsen Guizani","doi":"10.1109/TMC.2025.3526277","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526277","url":null,"abstract":"Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4451-4465"},"PeriodicalIF":7.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786351","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":"Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context","authors":"Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu","doi":"10.1109/TMC.2025.3525463","DOIUrl":"https://doi.org/10.1109/TMC.2025.3525463","url":null,"abstract":"In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3764-3778"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776239","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":"Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction","authors":"Xi Yu;Tiejun Lv;Weicai Li;Wei Ni;Dusit Niyato;Ekram Hossain","doi":"10.1109/TMC.2025.3525477","DOIUrl":"https://doi.org/10.1109/TMC.2025.3525477","url":null,"abstract":"Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/ transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as <inline-formula><tex-math>$frac{1}{12}$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4371-4388"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786383","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":"AugSSO: Secure Threshold Single-Sign-On Authentication With Popular Password Collection","authors":"Changsong Jiang;Chunxiang Xu;Guomin Yang","doi":"10.1109/TMC.2024.3525453","DOIUrl":"https://doi.org/10.1109/TMC.2024.3525453","url":null,"abstract":"Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-of-failure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve popular password collection, which is an effective means of enhancing system security. In this paper, we propose a secure PbTA scheme with popular password collection, dubbed AugSSO. In AugSSO, we conceive an efficient key renewal mechanism that allows identity servers to periodically update secret key shares in batches, and require storage of hardened password-derived public keys in credentials for user authentication, thereby resisting PSLA. We also present a popular password collection mechanism, where an aggregation server is introduced to identify popular passwords without disclosing unpopular ones. We provide security analysis and performance evaluation to demonstrate security and efficiency of AugSSO.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4355-4370"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786336","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":"On User Scheduling for Fixed Wireless Access via Channel Statistics","authors":"Xin Guan;Zhixing Chen;Yibin Kang;Qi Yan;Qingjiang Shi","doi":"10.1109/TMC.2024.3524565","DOIUrl":"https://doi.org/10.1109/TMC.2024.3524565","url":null,"abstract":"Conventional multi-user scheduling in cellular networks are required to make a decision every transmission time interval (TTI) of at most several milliseconds. Only quite simple schemes can be implemented under the stringent time constraint, resulting in far-from-optimum performance. In this paper, we focus on the case of scheduling multiple users in a fixed wireless access (FWA) network with stable channel characteristics. We propose a scheduling approach by which a high-quality scheduling decision based on statistical channel state information (CSI) is made across all TTIs instead of making simple TTI-level decisions. The proposed design is essentially a mixed-integer non-smooth non-convex stochastic problem. We first replace the indicator functions in the formulation by smooth sigmoid functions to tackle nonsmoothness. By leveraging deterministic equivalents (D.E.), we then convert the original stochastic problem into an approximated deterministic one, followed by linear relaxation of the integer constraints. However, the converted problem is still nonconvex due to implicit equation constraints formerly introduced by D.E. Therefore, we employ implicit optimization technique to compute the gradient explicitly, with which we further propose an algorithm design based on a modified version of Frank–Wolfe method. Numerical results verify the effectiveness of our proposed scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4040-4052"},"PeriodicalIF":7.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783256","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":"NeuroBalancer: Balancing System Frequencies With Punctual Laziness for Timely and Energy-Efficient DNN Inferences","authors":"Kyungmin Bin;Seyeon Kim;Sangtae Ha;Song Chong;Kyunghan Lee","doi":"10.1109/TMC.2024.3524628","DOIUrl":"https://doi.org/10.1109/TMC.2024.3524628","url":null,"abstract":"On-device deep neural network (DNN) inference is often desirable for user experience and privacy. Existing solutions have fully utilized resources to minimize inference latency. However, they result in severe energy inefficiency by completing DNN inference much earlier than the required service interval. It poses a new challenge of how to make DNN inferences in a punctual and energy-efficient manner. To tackle this challenge, we propose a new resource allocation strategy for DNN processing, namely <italic>punctual laziness</i> that disperses its workload as efficiently as possible over time within its strict delay constraint. This strategy is particularly beneficial for neural workloads since a DNN comprises a set of popular operators whose latency and energy consumption are predictable. Through this understanding, we propose NeuroBalancer, an operator-aware core and memory frequency scaling framework that balances those frequencies as efficiently as possible while making timely inferences. We implement and evaluate NeuroBalancer on off-the-shelf Android devices with various state-of-the-art DNN models. Our results show that NeuroBalancer successfully meets a given inference latency requirements while saving energy consumption up to 43.9% and 21.1% compared to the Android’s default governor and up to 42.1% and 18.6% compared to SysScale, the state-of-the-art mobile governor on CPU and GPU, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4339-4354"},"PeriodicalIF":7.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786329","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":"Usability and Security Analysis of the Compare-and-Confirm Method in Mobile Push-Based Two-Factor Authentication","authors":"Mohammed Jubur;Nitesh Saxena;Faheem A. Reegu","doi":"10.1109/TMC.2024.3524093","DOIUrl":"https://doi.org/10.1109/TMC.2024.3524093","url":null,"abstract":"Push-based two-factor authentication (2FA) methods, such as the ”Just-Confirm” approach, are popular due to their user-friendly design, requiring users to simply approve or deny a push notification on their mobile device. However, these methods are vulnerable to ”concurrency attacks,” where an attacker attempts to log in immediately after the legitimate user, causing multiple push notifications that may lead to users inadvertently approving fraudulent access. This vulnerability arises because the login notifications are not uniquely bound to individual login attempts. To address this issue, Push-Compare-and-Confirm 2FA method enhances security by associating each login notification with a unique code displayed on both the authentication terminal and the push notification. Users are required to match these codes before confirming access, thereby binding the notification to a specific login attempt. Recognizing the ubiquity of mobile devices in daily life, we conducted a comprehensive user study with 65 participants to evaluate the usability and security of Push-Compare-and-Confirm. The study considered two scenarios: one where the user’s second-factor device (phone) is physically separate from the authentication terminal (e.g., logging in on a PC and confirming on the phone), and another where the phone serves as both the authentication terminal and the second-factor device. Participants completed 24 login trials, including both benign and attack scenarios, with varying code lengths (four characters and six characters). Our results indicate that while Push-Compare-and-Confirm maintains high usability in benign scenarios, with True Positive Rates (<italic>TPR</i>) exceeding 95%, it presents significant challenges in attack detection. Participants correctly identified only about 50% of fraudulent login attempts, indicating a substantial vulnerability remains. These findings suggest that although Push-Compare-and-Confirm enhances security over standard push-based 2FA methods, additional measures—such as more intuitive interface designs, clearer visual cues, and user education on the importance of code verification—are necessary to improve attack detection rates without compromising usability.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4623-4638"},"PeriodicalIF":7.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924977","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":"Intelligent Task Offloading and Resource Allocation in Knowledge Defined Edge Computing Networks","authors":"Chuangchuang Zhang;Qiang He;Fuliang Li;Keping Yu","doi":"10.1109/TMC.2024.3522253","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522253","url":null,"abstract":"As an emerging architecture, edge computing enables resource limited terminal devices to offload their computation tasks to edge servers in the vicinity, to efficiently reduce delay and energy consumption. However, the continuous expansion of network scale and rapid growth of network traffic in recent years have brought huge challenges to task offloading and resource allocation. To tackle the challenges, by integrating Knowledge Defined Networking (KDN) and edge computing technologies, we design a novel Knowledge defined Edge Computing (KEC) architecture, to achieve intelligent resource allocation and task offloading in dynamic large-scale edge computing networks. We formulate the task offloading and resource allocation optimization problem, to minimize delay and energy consumption, by considering resource requirements and controller deployment. To solve it, we present an intelligent Resource Allocation based Task Offloading (TORA) mechanism, where a Multi-Agent SD3 based resource allocation (MASD3) algorithm is devised to perform efficient resource allocation. To adapt to the rapid expansion of network scale, we design a resource Allocation based Controller Deployment and task offloading Decision (DACD) algorithm, to perform the optimal controller deployment and task offloading. Extensive simulation experiments demonstrate the effectiveness and efficiency of our proposed solution, and TORA mechanism outperforms comparison mechanisms on delay and energy consumption.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4312-4325"},"PeriodicalIF":7.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786335","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":"Resource Allocation for the Uplink of a Multi-User Massive MIMO System","authors":"Haseen Rahman;Catherine Rosenberg","doi":"10.1109/TMC.2024.3522207","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522207","url":null,"abstract":"We study the uplink resource management of a multi-user multiple-input-multiple-output single cell for Zero-Forcing receive combining transmission. We consider jointly power allocation, user selection and modulation and coding scheme selection over multiple subchannels. Our contributions are twofold: we first propose a quasi-optimal offline algorithm that provides a target performance and then design and validate an efficient online proportional fair algorithm that performs the above steps. Due to user power constraints, the offline optimization is conducted jointly for all subchannels within a time slot, a computationally intensive task, prompting the proposal of a greedy offline algorithm that we validate in two ways: 1) for a small number of users, by solving the general problem to quasi-optimality and 2) for a larger number of users, by solving again to quasi-optimality a transformed version of the general problem when the channels are assumed flat. From the offline study, we find that, given the right user selection, equal power allocation can be employed without much degradation in performance. We also see that the number of channels allocated to users varies widely depending upon their channel gains. Using these insights, we propose our efficient real-time online algorithm that has runtime competitiveness with a state-of-the-art benchmark.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4326-4338"},"PeriodicalIF":7.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786385","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":"Laser-Powered UAV Trajectory and Charging Optimization for Sustainable Data-Gathering in the Internet of Things","authors":"Yue-Shiuan Liau;Y.-W. Peter Hong;Jang-Ping Sheu","doi":"10.1109/TMC.2024.3523281","DOIUrl":"https://doi.org/10.1109/TMC.2024.3523281","url":null,"abstract":"This work examines the trajectory design and energy charging strategy of a data-gathering unmanned aerial vehicle (UAV). The UAV utilizes laser charging from high-altitude platforms (HAPs) to replenish its battery, enabling sustained travel across multiple data-gathering points. The trajectory is determined by a sequence of hovering positions at which the UAV stays to perform both data collection and energy charging. The UAV's hovering positions affect both the sensors’ transmission rates and the laser-charging efficiency. To minimize the total task completion time, it is necessary to choose hovering positions that consider both data upload and energy charging times. In this work, we first propose the Minimum Completion Time Trajectory and Charging Optimization (MinTime-TCO) algorithm, where the hovering positions and charging energies are optimized in turn using a block coordinate descent approach. Given the UAV's hovering positions, we propose the Minimum Charge Rate Search (MCRS) algorithm to optimize the charging energies at these positions. We show that MCRS is optimal in terms of minimizing the total task completion time. Then, given the charging energies, we propose the Hovering Position Optimization (HPO) algorithm, employing successive convex approximation to address the non-convexity of the optimization problem. We also propose a low-complexity alternative based on dynamic programming to further reduce computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms against several baseline strategies.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4278-4295"},"PeriodicalIF":7.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786343","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}