Pub Date : 2025-07-07DOI: 10.1109/TMC.2025.3586618
Tianlang He;Zhiqiu Xia;S.-H. Gary Chan
Knowing a pedestrian’s conveyor state of “elevator,” “escalator,” or “neither” is fundamental to many applications such as indoor navigation and people flow management. Previous studies on classifying the conveyor state often rely on specially designed body-worn sensors or make strong assumptions on pedestrian behaviors, which greatly strangles their deployability. To overcome this, we study the classification problem under arbitrary pedestrian behaviors using the inertial navigation system (INS) of the commonly available smartphones (including accelerometer, gyroscope, and magnetometer). This problem is challenging, because the INS signals of the conveyor states are entangled by the arbitrary and diverse pedestrian behaviors. We propose ELESON, a novel and lightweight deep-learning approach that uses phone INS to classify a pedestrian to elevator, escalator, or neither. Using causal decomposition and adversarial learning, ELESON extracts the motion and magnetic features of conveyor state independent of pedestrian behavior, based on which it estimates the state confidence by means of an evidential classifier. We curate a large and diverse dataset with 36,420 instances of pedestrians randomly taking elevators and escalators under arbitrary unknown behaviors. Our extensive experiments show that ELESON is robust against pedestrian behavior, achieving a high accuracy of over 0.9 in F1 score, strong confidence discriminability of 0.81 in AUROC (Area Under the Receiver Operating Characteristics), and low computational and memory requirements fit for common smartphone deployment.
了解行人的传送带状态是“电梯”、“自动扶梯”还是“两者都不是”，对于室内导航和人流管理等许多应用来说都是至关重要的。以往的传送带状态分类研究往往依赖于专门设计的穿戴式传感器，或者对行人行为进行了较强的假设，极大地限制了其可部署性。为了克服这一问题，我们使用常用智能手机（包括加速度计、陀螺仪和磁力计）的惯性导航系统（INS）研究了任意行人行为下的分类问题。这是一个具有挑战性的问题，因为传送带状态的INS信号被任意和多样的行人行为所纠缠。我们提出了ELESON，这是一种新颖且轻量级的深度学习方法，它使用手机INS将行人分类为电梯、自动扶梯或两者都不分类。利用因果分解和对抗学习，ELESON提取了与行人行为无关的运输状态的运动和磁性特征，并在此基础上通过证据分类器估计状态置信度。我们制作了一个庞大而多样的数据集，其中包含36,420个行人在任意未知行为下随机乘坐电梯和自动扶梯的实例。我们的大量实验表明，ELESON对行人行为具有鲁棒性，在F1得分中达到0.9以上的高精度，在AUROC （Receiver Operating Characteristics Area Under Area）中达到0.81的强置信度，并且适合普通智能手机部署的低计算和内存要求。
{"title":"Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone Under Arbitrary Pedestrian Behavior","authors":"Tianlang He;Zhiqiu Xia;S.-H. Gary Chan","doi":"10.1109/TMC.2025.3586618","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586618","url":null,"abstract":"Knowing a pedestrian’s <italic>conveyor state</i> of “elevator,” “escalator,” or “neither” is fundamental to many applications such as indoor navigation and people flow management. Previous studies on classifying the conveyor state often rely on specially designed body-worn sensors or make strong assumptions on pedestrian behaviors, which greatly strangles their deployability. To overcome this, we study the classification problem under arbitrary pedestrian behaviors using the inertial navigation system (INS) of the commonly available smartphones (including accelerometer, gyroscope, and magnetometer). This problem is challenging, because the INS signals of the conveyor states are entangled by the arbitrary and diverse pedestrian behaviors. We propose ELESON, a novel and lightweight deep-learning approach that uses phone INS to classify a pedestrian to <bold>el</b>evator, <bold>es</b>calator, <bold>o</b>r <bold>n</b>either. Using causal decomposition and adversarial learning, ELESON extracts the motion and magnetic features of conveyor state independent of pedestrian behavior, based on which it estimates the state confidence by means of an evidential classifier. We curate a large and diverse dataset with 36,420 instances of pedestrians randomly taking elevators and escalators under arbitrary unknown behaviors. Our extensive experiments show that ELESON is robust against pedestrian behavior, achieving a high accuracy of over 0.9 in F1 score, strong confidence discriminability of 0.81 in AUROC (Area Under the Receiver Operating Characteristics), and low computational and memory requirements fit for common smartphone deployment.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12626-12639"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223682","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586447
Linfeng Liu;Wenzhe Zhang;Xingyu Li;Jia Xu
At present, Unmanned Aerial Vehicle (UAV) swarm has been extensively applied in various fields. In the application of detection and localization of electronic signals, some UAVs could become disabled due to some abnormal events (e.g. electromagnetic interference and battery electricity exhaustion), and the topology connectivity of UAV swarm could be impaired, i.e., the topology of UAV swarm could be partitioned. For the topology recovery issue, we first propose Robust Topology Recovery Algorithm of UAV swarm (RTRA) to recover the topology connectivity of UAV swarm and enhance the topology robustness (reduce the number of potential topology recoveries in future) by relocating some UAVs to new positions with shortest flight distance. Furthermore, we note that the relocated UAVs are easy to exhaust the battery electricity and fail due to the extra flight movements for the topology recoveries, which affects the topology robustness. To this end, we present Cascading Robust Recovery Topology Algorithm of UAV swarm (CRTRA), which adopts a cascading movement strategy to share the flight movements among multiply relocated UAVs, thus avoiding the battery electricity exhaustion of the relocated UAVs. Extensive simulations and comparisons demonstrate that our proposed CRTRA can effectively recover the topology connectivity of UAV swarm while enhancing the topology robustness and shortening the flight distance of relocated UAVs, and CRTRA is especially suitable for some missions such as the detection and localization of electronic signals where UAVs are prone to fail.
目前，无人机群已广泛应用于各个领域。在电子信号的检测与定位应用中，一些无人机可能会因为某些异常事件（如电磁干扰、电池电量耗尽）而导致无人机失能，破坏无人机群的拓扑连通性，即对无人机群的拓扑进行分区。针对拓扑恢复问题，首先提出了无人机群鲁棒拓扑恢复算法（Robust topology recovery Algorithm of UAV swarm， RTRA），通过将部分无人机重新定位到飞行距离最短的新位置，恢复无人机群的拓扑连通性，增强拓扑鲁棒性（减少未来可能的拓扑恢复次数）。此外，我们注意到重新定位的无人机容易耗尽电池电量，并且由于拓扑恢复的额外飞行运动而失效，这影响了拓扑的鲁棒性。为此，提出了无人机群的级联鲁棒恢复拓扑算法（CRTRA），该算法采用级联运动策略，在多个重新定位的无人机之间共享飞行运动，从而避免了重新定位无人机的电池电量耗尽。大量的仿真和比较表明，该算法可以有效地恢复无人机群的拓扑连通性，同时增强了拓扑鲁棒性，缩短了重新定位无人机的飞行距离，特别适用于无人机容易失效的电子信号检测和定位等任务。
{"title":"On the Robust Topology Recovery of UAV Swarm for Detection and Localization of Electronic Signals","authors":"Linfeng Liu;Wenzhe Zhang;Xingyu Li;Jia Xu","doi":"10.1109/TMC.2025.3586447","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586447","url":null,"abstract":"At present, Unmanned Aerial Vehicle (UAV) swarm has been extensively applied in various fields. In the application of detection and localization of electronic signals, some UAVs could become disabled due to some abnormal events (e.g. electromagnetic interference and battery electricity exhaustion), and the topology connectivity of UAV swarm could be impaired, i.e., the topology of UAV swarm could be partitioned. For the topology recovery issue, we first propose Robust Topology Recovery Algorithm of UAV swarm (RTRA) to recover the topology connectivity of UAV swarm and enhance the topology robustness (reduce the number of potential topology recoveries in future) by relocating some UAVs to new positions with shortest flight distance. Furthermore, we note that the relocated UAVs are easy to exhaust the battery electricity and fail due to the extra flight movements for the topology recoveries, which affects the topology robustness. To this end, we present Cascading Robust Recovery Topology Algorithm of UAV swarm (CRTRA), which adopts a cascading movement strategy to share the flight movements among multiply relocated UAVs, thus avoiding the battery electricity exhaustion of the relocated UAVs. Extensive simulations and comparisons demonstrate that our proposed CRTRA can effectively recover the topology connectivity of UAV swarm while enhancing the topology robustness and shortening the flight distance of relocated UAVs, and CRTRA is especially suitable for some missions such as the detection and localization of electronic signals where UAVs are prone to fail.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12595-12610"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223668","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586457
Yundi Wang;Xiaoyu Wang;He Huang;Haipeng Dai
Unmanned Aerial Vehicles (UAVs) can be easily deployed as auxiliary base stations due to their convenience and flexibility. However, limited battery capacity becomes a bottleneck. Promising wireless power transfer (WPT) technologies can provide a continuous power supply for UAVs. Many of the recent works treat the UAV battery capacity as a constraint, which hinders the assurance of continuous UAV operation. Furthermore, most studies employ intelligent path-planning algorithms that lack explicit performance guarantees. In this paper, we study the problem of Practical Optimizing UAV Trajectory in Wireless Charging Networks (POTWCN), which involves planning the trajectory of the wireless-powered UAV in the practical environment with obstacles by selecting candidate passing positions and determining the access order in the charging network. The goal is to maximize the benefit, i.e., balancing the total task completion time and the number of charging stations visited, so as to minimize path length and flight time, and ensure energy constraints with performance bound. To solve this problem, we first formalize the problem and prove its submodularity. Then, we propose the obstacle-aware weighted graph generation algorithm (OWGGA) to deal with the obstacles in the environment, which forms an obstacle-avoidance path using tangents and arcs between two hovering positions and the blocking obstacles. Next, we propose a dynamic charging station selection algorithm (ACSA), which maximizes the UAV’s energy utilization by limiting the number of charging stations that can be included. In the algorithm, we introduce the Christofides algorithm and use the path length calculated by OWGGA as the edge weights of the graph. Subsequently, considering the UAV’s energy constraints, we iteratively solve the UAV trajectory planning problem by adding the charging station with a maximized marginal benefit to the path. We prove that the proposed algorithm achieves an approximation ratio $1 - 1/e$ as well as the path length is at most $3pi /4$ times the optimal solution. Simulation results show that our algorithm reduces the flight distance by 38.01% and the task completion time by 34.00% on average.
{"title":"Practical Optimizing UAV Trajectory in Wireless Charging Networks: An Approximated Approach","authors":"Yundi Wang;Xiaoyu Wang;He Huang;Haipeng Dai","doi":"10.1109/TMC.2025.3586457","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586457","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) can be easily deployed as auxiliary base stations due to their convenience and flexibility. However, limited battery capacity becomes a bottleneck. Promising wireless power transfer (WPT) technologies can provide a continuous power supply for UAVs. Many of the recent works treat the UAV battery capacity as a constraint, which hinders the assurance of continuous UAV operation. Furthermore, most studies employ intelligent path-planning algorithms that lack explicit performance guarantees. In this paper, we study the problem of <u>P</u>ractical <u>O</u>ptimizing UAV <u>T</u>rajectory in <u>W</u>ireless <u>C</u>harging <u>N</u>etworks (POTWCN), which involves planning the trajectory of the wireless-powered UAV in the practical environment with obstacles by selecting candidate passing positions and determining the access order in the charging network. The goal is to maximize the benefit, i.e., balancing the total task completion time and the number of charging stations visited, so as to minimize path length and flight time, and ensure energy constraints with performance bound. To solve this problem, we first formalize the problem and prove its submodularity. Then, we propose the obstacle-aware weighted graph generation algorithm (OWGGA) to deal with the obstacles in the environment, which forms an obstacle-avoidance path using tangents and arcs between two hovering positions and the blocking obstacles. Next, we propose a dynamic charging station selection algorithm (ACSA), which maximizes the UAV’s energy utilization by limiting the number of charging stations that can be included. In the algorithm, we introduce the Christofides algorithm and use the path length calculated by OWGGA as the edge weights of the graph. Subsequently, considering the UAV’s energy constraints, we iteratively solve the UAV trajectory planning problem by adding the charging station with a maximized marginal benefit to the path. We prove that the proposed algorithm achieves an approximation ratio <inline-formula><tex-math>$1 - 1/e$</tex-math></inline-formula> as well as the path length is at most <inline-formula><tex-math>$3pi /4$</tex-math></inline-formula> times the optimal solution. Simulation results show that our algorithm reduces the flight distance by 38.01% and the task completion time by 34.00% on average.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12550-12566"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223687","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586638
Zhijian Lin;Yang Xiao;Yi Fang;Hongbing Chen;Xiaoqiang Lu
Rate-splitting multiple access (RSMA), space division multiple access (SDMA), and non-orthogonal multiple access (NOMA) have gained significant popularity and are extensively utilized across various domains. However, it is still unclear whether hybrid RSMA-SDMA-NOMA (HybridRDN) would seamlessly combine the advantages of RSMA, SDMA, and NOMA to contribute to the computation offloading of autonomous vehicle systems. To address the above issue, this paper introduces a novel HybridRDN-assisted computation offloading fleet (COF) scheme tailored for autonomous vehicle systems. First, we propose a stochastic-geometry-aided method to model the offloading framework. Afterwards, the task vehicles (TVs) ingeniously employ the proposed HybridRDN scheme to offload tasks to the resource vehicles (RVs) in each COF to relieve their computational burden. Diverging from the sole optimization of the task segmentation ratio or the transmission rate, a joint optimization problem involving the transmission weighting factor, the HybridRDN precoding matrix, the common rate, and the task segmentation ratio, is formulated, which aims to minimize the average delay of the COF system while approaching the rate performance of the ideal HybridRDN. Furthermore, a delay-optimal alternating optimization algorithm (DOAOA) is developed to obtain the solution for the optimization problem. Experimental results validate the plausibility and superiority of the proposed framework compared to the state-of-the-art schemes.
{"title":"HybridRDN: Delay-Optimal Computation Offloading for Autonomous Vehicle Fleets Based on RSMA","authors":"Zhijian Lin;Yang Xiao;Yi Fang;Hongbing Chen;Xiaoqiang Lu","doi":"10.1109/TMC.2025.3586638","DOIUrl":"10.1109/TMC.2025.3586638","url":null,"abstract":"Rate-splitting multiple access (RSMA), space division multiple access (SDMA), and non-orthogonal multiple access (NOMA) have gained significant popularity and are extensively utilized across various domains. However, it is still unclear whether hybrid <underline>R</u>SMA-S<underline>D</u>MA-<underline>N</u>OMA (HybridRDN) would seamlessly combine the advantages of RSMA, SDMA, and NOMA to contribute to the computation offloading of autonomous vehicle systems. To address the above issue, this paper introduces a novel HybridRDN-assisted computation offloading fleet (COF) scheme tailored for autonomous vehicle systems. First, we propose a stochastic-geometry-aided method to model the offloading framework. Afterwards, the task vehicles (TVs) ingeniously employ the proposed HybridRDN scheme to offload tasks to the resource vehicles (RVs) in each COF to relieve their computational burden. Diverging from the sole optimization of the task segmentation ratio or the transmission rate, a joint optimization problem involving the transmission weighting factor, the HybridRDN precoding matrix, the common rate, and the task segmentation ratio, is formulated, which aims to minimize the average delay of the COF system while approaching the rate performance of the ideal HybridRDN. Furthermore, a delay-optimal alternating optimization algorithm (DOAOA) is developed to obtain the solution for the optimization problem. Experimental results validate the plausibility and superiority of the proposed framework compared to the state-of-the-art schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12456-12470"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210130","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586606
Zijun Zhan;Yaxian Dong;Daniel Mawunyo Doe;Yuqing Hu;Shuai Li;Shaohua Cao;Lei Fan;Zhu Han
Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, we employ contract theory to model information asymmetry while utilizing DRO to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unity-based teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7% to 10.74% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory.
{"title":"Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation","authors":"Zijun Zhan;Yaxian Dong;Daniel Mawunyo Doe;Yuqing Hu;Shuai Li;Shaohua Cao;Lei Fan;Zhu Han","doi":"10.1109/TMC.2025.3586606","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586606","url":null,"abstract":"Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, we employ contract theory to model information asymmetry while utilizing DRO to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unity-based teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7% to 10.74% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12567-12579"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223673","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586429
Zhen Gao;Gang Wang;Lei Yang;Chenhao Ying
In mobile crowd sensing systems, existing flight control methods enable uncrewed aerial vehicles (UAVs) to provide high-quality data collection services for various applications. However, due to limited communication range, UAVs typically collect data under partial observability, hindering optimal performance without global environmental information. Additionally, many methods fail to enforce critical safety constraints. This paper proposes a communication-assisted safe multi-agent actor-critic-based UAV flight control method (CSMAAC). First, we propose an independent prediction communication partner model to address the partial observability problem. Based on the UAV’s local observation, causal inference is used to obtain prior communication information between UAVs through a feed-forward neural network to help UAVs determine potential communication partners. Second, we utilize a critic-network to predict and quantify inter-UAV influence and determine the necessity of communication. By exchanging necessary information inter-UAV, UAVs can perceive global information, thereby solving the UAV’s partial observability problem and reducing communication overhead. Moreover, we propose a similarity enhancement mechanism to improve the learning efficiency of the model by enhancing the connection between UAV observations and the policies of other UAVs. Finally, we introduce a safety layer to Actor-Network to ensure safe UAV flight. The simulation results show that the proposed method outperforms the baselines.
{"title":"CSMAAC: Multi-Agent Reinforcement Learning Based Flight Control in Partially Observable Multi-UAV Assisted Crowd Sensing Systems","authors":"Zhen Gao;Gang Wang;Lei Yang;Chenhao Ying","doi":"10.1109/TMC.2025.3586429","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586429","url":null,"abstract":"In mobile crowd sensing systems, existing flight control methods enable uncrewed aerial vehicles (UAVs) to provide high-quality data collection services for various applications. However, due to limited communication range, UAVs typically collect data under partial observability, hindering optimal performance without global environmental information. Additionally, many methods fail to enforce critical safety constraints. This paper proposes a communication-assisted safe multi-agent actor-critic-based UAV flight control method (CSMAAC). First, we propose an independent prediction communication partner model to address the partial observability problem. Based on the UAV’s local observation, causal inference is used to obtain prior communication information between UAVs through a feed-forward neural network to help UAVs determine potential communication partners. Second, we utilize a critic-network to predict and quantify inter-UAV influence and determine the necessity of communication. By exchanging necessary information inter-UAV, UAVs can perceive global information, thereby solving the UAV’s partial observability problem and reducing communication overhead. Moreover, we propose a similarity enhancement mechanism to improve the learning efficiency of the model by enhancing the connection between UAV observations and the policies of other UAVs. Finally, we introduce a safety layer to Actor-Network to ensure safe UAV flight. The simulation results show that the proposed method outperforms the baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12672-12691"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223698","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586587
Weijia Han;Chuan Huang;Yanjie Dong;Yangyingzi Zhang;Yuxiang Yue;Wei Teng
Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.
{"title":"A Novel Information-Theoretical Framework for Quantifying Coding Performance in Scalable Mobile Video Streaming","authors":"Weijia Han;Chuan Huang;Yanjie Dong;Yangyingzi Zhang;Yuxiang Yue;Wei Teng","doi":"10.1109/TMC.2025.3586587","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586587","url":null,"abstract":"Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12611-12625"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223669","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}
Low Earth orbit satellite networks (LSNs) are envisioned as key enablers of 6G by offering ubiquitous, low-latency connectivity. Their mesh topology enables multipath differential routing, which improves bandwidth utilization and reduces transmission delay. However, the growing demand for data and the dynamic, self-organizing nature of LSNs pose significant challenges for joint multipath routing and traffic scheduling under strict latency and energy constraints. To address these challenges, this paper proposes a multipath routing optimization (MRO) and traffic scheduling method tailored for multipath differential routing. Specifically, a dynamic multi-attribute graph model is developed to precisely capture the dynamic properties of LSNs. Building on this model, a MRO algorithm, integrated with a Stackelberg game framework, is introduced. The MRO algorithm employs a decomposition-based approach to identify multiple optimal paths that minimize delay and energy consumption, while the Stackelberg game framework ensures efficient traffic distribution across these paths. Numerical results demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving cumulative reward improvements of 26.77% to 43.8% across four real-world network topologies and exhibiting better Pareto front coverage. Furthermore, by leveraging the rapid convergence properties of the Stackelberg game model, the proposed method enhances network throughput by 12% to 43% and reduces transmission time by 14% to 49%.
{"title":"Efficient Multipath Differential Routing and Traffic Scheduling in Ultra-Dense LEO Satellite Networks: A DRL With Stackelberg Game Approach","authors":"Shuyang Li;Qiang Wu;Ran Wang;Long Chen;Hongke Zhang","doi":"10.1109/TMC.2025.3586262","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586262","url":null,"abstract":"Low Earth orbit satellite networks (LSNs) are envisioned as key enablers of 6G by offering ubiquitous, low-latency connectivity. Their mesh topology enables multipath differential routing, which improves bandwidth utilization and reduces transmission delay. However, the growing demand for data and the dynamic, self-organizing nature of LSNs pose significant challenges for joint multipath routing and traffic scheduling under strict latency and energy constraints. To address these challenges, this paper proposes a multipath routing optimization (MRO) and traffic scheduling method tailored for multipath differential routing. Specifically, a dynamic multi-attribute graph model is developed to precisely capture the dynamic properties of LSNs. Building on this model, a MRO algorithm, integrated with a Stackelberg game framework, is introduced. The MRO algorithm employs a decomposition-based approach to identify multiple optimal paths that minimize delay and energy consumption, while the Stackelberg game framework ensures efficient traffic distribution across these paths. Numerical results demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving cumulative reward improvements of 26.77% to 43.8% across four real-world network topologies and exhibiting better Pareto front coverage. Furthermore, by leveraging the rapid convergence properties of the Stackelberg game model, the proposed method enhances network throughput by 12% to 43% and reduces transmission time by 14% to 49%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12424-12440"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223670","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 : 2025-07-07DOI: 10.1109/TMC.2025.3586660
Kanghuai Liu;Jihong Yu;Lin Chen
We study the problem of target object information collection in multi-tagged COTS RFID systems. Unlike its single-tagged peers, the multi-tagged COTS RFID scenario poses new challenges in devising information collection algorithms: 1) Tags attached to the same object carry identical information. Hence, reusing single-tagged information collection algorithms leads to unnecessary redundancy; 2) Multi-tagged RFID systems are often deployed in applications where tags are vulnerable to damage. Such faulty tags may severely degrade the performance of information collection; 3) Most state-of-the-art information collection algorithms rely heavily on the hashing operation that is not seamlessly supported by the C1G2 standard, rendering these solutions inefficient and impractical. To tackle these technical challenges, this paper makes three contributions. First, we develop an efficient and compact tag pseudo-ID design, enabling the reader to select a single tag from each target object to collect information with only one Select command. Second, we construct a robust fault-handling mechanism capable of recognizing faulty tags without executing the entire slot. Third, armed with the above two techniques, we develop a novel information collection algorithm by leveraging the functionality offered by C1G2 to optimize the information collection sequence, thus minimizing the overall execution time. Empirical experiments on a COTS RFID system prototype demonstrate that our algorithm outperforms the best existing solution by 35–50% on average.
{"title":"On Information Collection in Multi-Tagged COTS RFID Systems","authors":"Kanghuai Liu;Jihong Yu;Lin Chen","doi":"10.1109/TMC.2025.3586660","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586660","url":null,"abstract":"We study the problem of target object information collection in multi-tagged COTS RFID systems. Unlike its single-tagged peers, the multi-tagged COTS RFID scenario poses new challenges in devising information collection algorithms: 1) Tags attached to the same object carry identical information. Hence, reusing single-tagged information collection algorithms leads to unnecessary redundancy; 2) Multi-tagged RFID systems are often deployed in applications where tags are vulnerable to damage. Such faulty tags may severely degrade the performance of information collection; 3) Most state-of-the-art information collection algorithms rely heavily on the hashing operation that is not seamlessly supported by the C1G2 standard, rendering these solutions inefficient and impractical. To tackle these technical challenges, this paper makes three contributions. First, we develop an efficient and compact tag pseudo-ID design, enabling the reader to select a single tag from each target object to collect information with only one <sc>Select</small> command. Second, we construct a robust fault-handling mechanism capable of recognizing faulty tags without executing the entire slot. Third, armed with the above two techniques, we develop a novel information collection algorithm by leveraging the functionality offered by C1G2 to optimize the information collection sequence, thus minimizing the overall execution time. Empirical experiments on a COTS RFID system prototype demonstrate that our algorithm outperforms the best existing solution by 35–50% on average.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12505-12516"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223672","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}
Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.
{"title":"Performance Analysis of Direct Acyclic Graph-Based Ledgers in Low-to-High Load Regime","authors":"Qingwen Wei;Shuping Dang;Zhihui Ge;Xiangcheng Li;Zhenrong Zhang","doi":"10.1109/TMC.2025.3586668","DOIUrl":"https://doi.org/10.1109/TMC.2025.3586668","url":null,"abstract":"Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 11","pages":"12441-12455"},"PeriodicalIF":9.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223681","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：