2022 18th International Conference on Mobility, Sensing and Networking (MSN)最新文献_第3页

Efficient Semantic Segmentation Backbone Evaluation for Unmanned Surface Vehicles based on Likelihood Distribution Estimation 基于似然分布估计的无人水面车辆高效语义分割主干评估

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00076

Yulong Zhang, Jingtao Sun, Mingkang Chen, Qiang Wang

{"title":"Efficient Semantic Segmentation Backbone Evaluation for Unmanned Surface Vehicles based on Likelihood Distribution Estimation","authors":"Yulong Zhang, Jingtao Sun, Mingkang Chen, Qiang Wang","doi":"10.1109/MSN57253.2022.00076","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00076","url":null,"abstract":"Obstacle detection using semantic segmentation shows a great promise for unmanned surface vehicles(USVs) in unstable marine environments. Unlike traditional machine learning, semantic segmentation models need to define suitable backbones in advance to extract features of key pixels. However, although the variety and number of backbones are massive, choosing the best one for the developer's environment in the practical application can be a daunting task. Past researches attempt to explore the ranking of backbones in specific scenarios by retraining all mainstream backbone models, which has a certain effect on some single and unchanged land scenes, but cannot be adapted to the unstable marine environment. Therefore, this paper proposes a method to quickly evaluate the suitable backbone, by extracting the representation models of different backbones without retraining and fine-tuning, separating the super-pixels of their feature distribution maps, comparing the features of different models according to likelihood distribution,and finally providing corresponding evaluation scores to give reference for backbone selection. Experimental results show that the proposed approach can provide precise backbone evaluation scores without increasing the computational effort, which can help developers quickly and accurately select the best backbone suitable for their own environment, and further design more accurate semantic segmentation models for unmanned surface vehicles.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision 边缘辅助移动视觉的自适应渐进图像增强

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00121

Daipeng Feng, Liekang Zeng, Lingjun Pu, Xu Chen

{"title":"Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision","authors":"Daipeng Feng, Liekang Zeng, Lingjun Pu, Xu Chen","doi":"10.1109/MSN57253.2022.00121","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00121","url":null,"abstract":"Recent advances in deep learning models have pushed Super-Resolution (SR) techniques to an unprecedented altitude, enabling high-quality image rendering with variable scaling size and natural fidelity. To deploy them on resource-constrained mobile devices, however, confronts significant chal-lenges of excessively long latency and poor user experience. To this end, we propose Apie, an edge-assisted adaptive image rendering system that allows low-latency, progressive image enhancement for a smooth user experience. Apie adopts a data parallel strategy across the end device and the edge server, along with a residual learning mechanism to judiciously retrieve information for SR models. Besides, a novel progressive image reconstruction is developed by exploiting content-aware image blocking and incremental image rendering, towards improved quality of user experience. Furthermore, Apie can dynamically adjust the choice of employed SR models with respect to the networking conditions, striking a good balance upon the latency-quality trade-off. Extensive evaluations show that Apie performs 7.33x faster than on-device GPU execution and 1.42x faster compared to the partial offloading method, while achieves 2.84dB higher PSNR compared to the interpolation method using conventional JPEG image compression and 0.74dB higher PSNR compared to the partial offloading method.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133388206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV Swarm Trajectory and Cooperative Beamforming Design in Double-IRS Assisted Wireless Communications 双irs辅助无线通信中无人机群轨迹与协同波束形成设计

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00099

Yangzhe Liao, Shuang Xia, Ke Zhang, X. Zhai

{"title":"UAV Swarm Trajectory and Cooperative Beamforming Design in Double-IRS Assisted Wireless Communications","authors":"Yangzhe Liao, Shuang Xia, Ke Zhang, X. Zhai","doi":"10.1109/MSN57253.2022.00099","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00099","url":null,"abstract":"Non-terrestrial communications have emerged as a technological enabler for seamless connectivity and ubiquitous computation services in the upcoming beyond fifth generation (B5G) and sixth generation (6G) networks. However, there exist numerous practical technical limitations, such as high deployment cost, massive energy consumption, high probability of information transmission blockage and dynamic propagation environments and so forth. Thanks to the rapid developments of meta-materials, the cost-effective and energy-efficiency intelligent reconfigurable surface (IRS) has been globally recognized as a revolutionized technology to construct smart radio environments. In this paper, a novel double-IRS assisted unmanned aerial vehicles (UAV)-swarm-enabled communication network architecture is proposed, where two UAV swarms are integrated with the main IRS reflector and subreflector, respectively. The energy minimization problem of UAV swarm carried main IRS is formulated, subject to a list of quality of service (QoS) constraints. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where the enhanced differential evolution (DE) algorithm is proposed to optimize the UAV swarm trajectory and the alternate optimization algorithm is utilized to optimize the cooperative reflect beamforming vector. Numerical results validate that the proposed algorithm outperforms several selected advanced algorithms regarding UAV swarm energy consumption. Moreover, the network performance under the different number of IRS elements is investigated.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Unknown Worker Recruitment for Heterogeneous Contextual Labeling Tasks Using Adversarial Multi-Armed Bandit 基于对抗性多臂强盗的异构上下文标记任务动态未知工人招募

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00088

Wucheng Xiao, Mingjun Xiao, Yin Xu

{"title":"Dynamic Unknown Worker Recruitment for Heterogeneous Contextual Labeling Tasks Using Adversarial Multi-Armed Bandit","authors":"Wucheng Xiao, Mingjun Xiao, Yin Xu","doi":"10.1109/MSN57253.2022.00088","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00088","url":null,"abstract":"Nowadays, crowdsourcing has become an increasingly popular paradigm for large-scale data annotation. It is crucial to ensure label quality by selecting the most suitable workers for labeling tasks. Many previous works have studied the reliability of unknown workers for crowdsourcing tasks with a stochastic assumption. However, each worker's reliability varies when performing tasks with different categories. Meanwhile, the reliability of each worker is usually unknown and doesn't follow any stochastic distribution. In this paper, we propose an Adversarial multi-armed Bandit-base algorithm to handle the Unknown Worker Recruitment (ABUWR) problem without any prior stochastic assumption. In ABUWR, we determine suitable workers for each task to maximize the accumulated average accuracy of the labeling tasks under a limited budget. Specifically, $w$e model this unknown worker recruitment problem as an adversarial multi-armed bandit game and use the least confidence scheme to ensure the total accumulate accuracy. Meanwhile, $w$e theoretically prove that ABUWR has a sub-linear regret upper bound. Furthermore, we demonstrate its significant performance through extensive simulations on real-world data traces.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"323 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115842096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory 零和博弈理论启发的进化离散优化

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00159

Ruiran Yu

{"title":"Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory","authors":"Ruiran Yu","doi":"10.1109/MSN57253.2022.00159","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00159","url":null,"abstract":"In a zero-sum game, the two players compete against each other, and the gain of one player means the loss of the other. Generative adversarial networks (GANs) are models of this kind of thinking. Evolutionary algorithms (EAs) are popular and high robust methods to solve combinatorial optimization problems. However, in the middle stages of evolution, EAs usually suffer from the problem of a serious lack of population diversity. This often results in that EAs fall into local optima. This paper presents a cooperative evolutionary algorithm driven by policy-based GANs (PGAN-CEA) for solving traveling salesman problems (TSPs). PGAN-CEA adopts a policy-gradient method in reinforcement learning to train GANs to generate discrete data. First, GANs are used to construct an initial population. Then, a cooperative evolution strategy driven by GANs is used in the middle of the evolution. Further, a dual-population mechanism is utilized to assist the co-evolution of the dominant solutions generated by GANs and the solutions from the population of EAs. Test cases from TSPLIB and the Mona Lisa Problems are used to evaluate the proposed algorithm. Compared with other GAN-based algorithms, the proposed algorithm can mitigate the problem of local convergence and achieves certain improvements in quite a few performance indicators.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116096959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Reliable AI Applications via Algorithm-Based Fault Tolerance on NVDLA 基于NVDLA容错算法的可靠AI应用

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00120

Mustafa Sanic, Cong Guo, Jingwen Leng, Minyi Guo, Weiyin Ma

{"title":"Towards Reliable AI Applications via Algorithm-Based Fault Tolerance on NVDLA","authors":"Mustafa Sanic, Cong Guo, Jingwen Leng, Minyi Guo, Weiyin Ma","doi":"10.1109/MSN57253.2022.00120","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00120","url":null,"abstract":"With the development of deep neural networks (DNNs), more complex accelerators have been designed for more sophisticated networks. Naturally, the complexity of accelerators makes them vulnerable to transient errors. Also, some DNN accelerators are widely used the safety-critical systems, such as autonomous vehicles. Therefore, the susceptibility to transient errors makes research on mitigation techniques more significant, and errors of accelerators should be limited to none. Some researchers proposed the modular redundancy method, which offers a highly reliable way but also considerably increases overhead. In this regard, algorithm-based solutions offer cheaper solutions. However, their implementation is primarily observed in software-based error injections. In this study, we propose a novel approach that focuses on implementing algorithm-based error detection (ABED) for RTL-level (hardware-based) error injections. Previous studies generally focused on the impact of soft errors in memory structures of embedded system-based accelerators. However, the main goal of this research is to study the impact of soft errors in processing elements and how to mitigate them. We implement an algorithm-based error detection that utilizes checksums for verifying convolution operations with low overhead. We first explain how to overcome the challenges of implementing ABED on FPGA-based accelerators, then how to implement it. We implement and evaluate our solution on an industry-level DNN accelerator called NVIDIA deep learning accelerator (NVDLA). In this study, our error injection method is constructed to test the most common soft error scenarios in processing units. The results of the research show that algorithm-based fault tolerance can detect all silent data corruptions (SDC) while maintaining a very low overhead (6-23%) on runtime.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122155999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CDTP: A Copyright-preserving Decentralized Data Trading Platform Based on Blockchain CDTP:基于区块链的版权保护去中心化数据交易平台

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00069

Heng Tian, Mingjun Xiao

引用次数: 0

Application Identification under Multi-Service Integration Platform 多业务集成平台下的应用识别

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00139

Ziyang Wu, Yi Xie

{"title":"Application Identification under Multi-Service Integration Platform","authors":"Ziyang Wu, Yi Xie","doi":"10.1109/MSN57253.2022.00139","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00139","url":null,"abstract":"Multi-service integration platform (MIP) is becoming a new way for mobile applications to provide services, such as the ChatBot of Facebook and the applet of WeChat. However, currently there are no special means and filtering strategies to supervise the services running on various MIPs. Existing solutions for program detection and traffic analysis are not suitable for MIP scenarios, which creates favorable conditions for the dissemination of illegal content through MIP. To address this issue, in this work we propose a new approach to identify mobile applications running on MIP platforms. The proposed approach uses IP flow to reconstruct data units of both transport and ap-plication layers respectively. By this way, we can capture the data transmission behavior of multi-protocol layers and obtain richer semantic features for application identification. Then, multi-kernel convolutional neural networks (CNN s) and long short term memory (LSTM) neural networks are employed to extract and aggregate the multi-scale features from the perspective of both protocol layer and time series. Finally, the fused features generated by the models are used to identify the category of the pending applications by a classifier composed of a fully connected neural network. We validate the proposed approach by three real datasets. The experimental results show that the proposed approach outperforms most existing benchmark methods in performance.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126346401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximate Shortest Distance Queries with Advanced Graph Analytics over Large-scale Encrypted Graphs 近似最短距离查询与高级图分析在大规模加密图

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00056

Yuchuan Luo, Dongsheng Wang, Shaojing Fu, Ming Xu, Yingwen Chen, Kai Huang

{"title":"Approximate Shortest Distance Queries with Advanced Graph Analytics over Large-scale Encrypted Graphs","authors":"Yuchuan Luo, Dongsheng Wang, Shaojing Fu, Ming Xu, Yingwen Chen, Kai Huang","doi":"10.1109/MSN57253.2022.00056","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00056","url":null,"abstract":"Understanding graph characteristics is of great importance for graph analytics. Among the many properties, shortest path distance is the fundamental and widely used one. With the advent of cloud computing, it is a natural choice for the data owners to host their massive graphs on the cloud and outsource the shortest distance querying service to it. However, the new paradigm brings serious security concerns as graph data and shortest distance queries may contain sensitive information of data owners and users. In this paper, we propose a novel scheme to support privacy-preserving approximate shortest distance queries with advanced graph analytics over large-scale encrypted graphs, which enables an untrusted cloud to answer shortest distance queries as well as advanced graph metrics (e.g., node centrality) without knowing the content of queries and the sensitive information of outsourced graphs. Compared with the state-of-the-art solutions, our design can support not only efficient and accurate shortest distance approximation, but also advanced graph analytics. We prove that our scheme is secure under the chosen-plaintext model. Experimental results over real-world datasets show that our scheme achieves high approximation accuracy with practical efficiency.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129327373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent optimization and allocation strategy of emergency repair resources based on big data 基于大数据的应急抢修资源智能优化与配置策略

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00168

Jiangdong Liu, Yue Zhao, Bo Wang, Jie Ying Gao, Li Xu, Ying Ma

引用次数: 0