2020 16th International Conference on Mobility, Sensing and Networking (MSN)最新文献_第3页

Energy-saving Strategy for Edge Computing by Collaborative Processing Tasks on Base Stations 基于基站协同处理任务的边缘计算节能策略

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00044

Zhongjun Ma, Zhenchun Wei, Wenjie Zhang, Zengwei Lyu, Junyi Xu, Benhong Zhang

{"title":"Energy-saving Strategy for Edge Computing by Collaborative Processing Tasks on Base Stations","authors":"Zhongjun Ma, Zhenchun Wei, Wenjie Zhang, Zengwei Lyu, Junyi Xu, Benhong Zhang","doi":"10.1109/MSN50589.2020.00044","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00044","url":null,"abstract":"Mobile Edge Computing (MEC) can significantly save the energy consumption of small-cell base stations(SBSs) by using Dynamic Voltage Scaling (DVS) technology. In this paper, we study how to reduce energy consumption of SBSs by using DVS technology. We propose a scheme to divide base station groups in wireless MEC network, and SBSs in the same base station group collaboratively process tasks. For cooperating tasks in a base station group, we propose a task-offloading strategy, which can effectively reduce the energy consumption of the base station groups. We use Task-offloading Decision Algorithm (TDA) to decide the number of task fragments allocated to each SBSs in the base station group. For processing task fragments in the task queue of SBSs, we propose a computing resource allocation scheme, and we can get the processing time of every task fragments in the task queue by Task Fragments Processing Algorithm (TFPA). Experimental results show that the task-offloading strategy and computing source allocation scheme can reduce the energy consumption by 30%-40% in a cellular network composed of 100 base stations.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122280730","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

Trapdoor Assignment of PEKS-based NDN Strategy in Two-Tier Networks 两层网络中基于peks的NDN策略的Trapdoor分配

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00099

Kyi Thar Ko, M. Mambo

引用次数: 1

NoPTPeer:Protecting Android Devices from Stealthy Spoofing and Stealing in WLANs without Privilege NoPTPeer:保护Android设备从隐形欺骗和窃取在无线局域网没有特权

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00095

Shuying Wei, Xiaoliang Wang, Ke Xu

{"title":"NoPTPeer:Protecting Android Devices from Stealthy Spoofing and Stealing in WLANs without Privilege","authors":"Shuying Wei, Xiaoliang Wang, Ke Xu","doi":"10.1109/MSN50589.2020.00095","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00095","url":null,"abstract":"Android devices are prone to spoofing attacks in Wireless Local Area Networks (WLANs), and many of them access numerous unknown networks in daily use. Moreover, because of the weak authentication between Android smartphones, attackers can steal data in a stealthier way based on Address Resolution Protocol (ARP) spoofing. These facts bring a gap in the study of device-side spoofing defense for Android devices. So in this paper a framework is proposed which requires No Privilege but can guarantee the True identity of Peers (NoPTPeer), to protect Android’s device-to-device communication in WLANs. Its main features include realizing strong authentication between Android devices, controlling dangerous outgoing connections, and monitoring suspicious incoming connections. These features are realized by an Identity-Based-Signature (IBS) scheme, an Android base class VpnService, and information read from Android system files, which all require no root privilege and are independent of network infrastructures. We implement this framework as an Android smartphone application. The experiments show its effectiveness in detecting spoofing and monitoring stealing, as well as acceptable overhead in memory, Central Processing Unit (CPU) usage and communication latency.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134291621","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

Magic Wand: Towards Plug-and-Play Gesture Recognition on Smartwatch 魔术棒:智能手表的即插即用手势识别

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00054

Zhipeng Song, Zhichao Cao, Zhenjiang Li, Jiliang Wang

{"title":"Magic Wand: Towards Plug-and-Play Gesture Recognition on Smartwatch","authors":"Zhipeng Song, Zhichao Cao, Zhenjiang Li, Jiliang Wang","doi":"10.1109/MSN50589.2020.00054","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00054","url":null,"abstract":"We propose Magic Wand which automatically recognizes 2D gestures (e.g., symbol, circle, polygon, letter) performed by users wearing a smartwatch in real-time manner. Meanwhile, users can freely choose their convenient way to perform those gestures in 3D space. In comparison with existing motion sensor based methods, Magic Wand develops a white-box model which adaptively copes with diverse hardware noises and user habits with almost zero overhead. The key principle behind Magic Wand is to utilize 2D stroke sequence for gesture recognition. Magic Wand defines 8 strokes in a unified 2D plane to represent various gestures. While a user is freely performing gestures in 3D space, Magic Wand collects motion data from accelerometer and gyroscope. Meanwhile, Magic Wand removes various acceleration noises and reduces the dimension of 3D acceleration sequences of user gestures. Moreover, Magic Wand develops stroke sequence extraction and matching methods to timely and accurately recognize gestures. We implement Magic Wand and evaluate its performance with 4 smartwatches and 6 users. The evaluation results show that the median recognition accuracy is 94.0% for a set of 20 gestures. For each gesture, the processing overhead is tens of milliseconds.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134618642","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: 2

Online Distributed Job Dispatching with Outdated and Partially-Observable Information 具有过时信息和部分可观察信息的在线分布式作业调度

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00059

Yuncong Hong, B. Lv, Rui Wang, Haisheng Tan, Zhenhua Han, Hao Zhou, F. Lau

{"title":"Online Distributed Job Dispatching with Outdated and Partially-Observable Information","authors":"Yuncong Hong, B. Lv, Rui Wang, Haisheng Tan, Zhenhua Han, Hao Zhou, F. Lau","doi":"10.1109/MSN50589.2020.00059","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00059","url":null,"abstract":"In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125683940","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

Study on Location and Sizing Programming of Regional Substation 区域变电站选址与规模规划研究

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00128

Xiaolin Tan, Shiyao Hu, Chunguang He, P. Sun, Jing Zhang, Ying Ma

{"title":"Study on Location and Sizing Programming of Regional Substation","authors":"Xiaolin Tan, Shiyao Hu, Chunguang He, P. Sun, Jing Zhang, Ying Ma","doi":"10.1109/MSN50589.2020.00128","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00128","url":null,"abstract":"This paper proposes a research method for the location and capacity planning of regional substations. This method mainly studies and analyzes the active distribution network space based on load forecasting to complete the location and capacity analysis of regional substations. Combining the GIS information system and the power sensor network, based on the spatial load prediction results, and combined with the regional distributed power planning results, the location and capacity planning of the regional substations are carried out, and finally based on the location selection and capacity results, the power supply range of the substation and Economic calculation indicators have formed a set of technologically advanced, comprehensive and practical planning schemes, which can not only meet the needs of the distribution network at all levels in Xiongan New District for substation location and capacity, but also can be extended to urban areas of six cities, such as A, It is used in Class B areas to provide data support and scientific basis for the location and capacity of substations in the power grid, realize accurate planning, and assist relevant technical personnel in making decisions.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"290 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131991711","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: 3

Load balancing and resource management in distributed B5G networks 分布式B5G网络的负载均衡与资源管理

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00053

Lu Ma, Weiling Chang, Chao Li, Shanjin Ni, Jianming Cui, Ming Liu

引用次数: 0

Deep Reinforcement Learning for Optimal Resource Allocation in Blockchain-based IoV Secure Systems 基于区块链的车联网安全系统资源优化配置的深度强化学习

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00036

Hongzhi Xiao, Chen Qiu, Qinglin Yang, Huakun Huang, Junbo Wang, Chunhua Su

{"title":"Deep Reinforcement Learning for Optimal Resource Allocation in Blockchain-based IoV Secure Systems","authors":"Hongzhi Xiao, Chen Qiu, Qinglin Yang, Huakun Huang, Junbo Wang, Chunhua Su","doi":"10.1109/MSN50589.2020.00036","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00036","url":null,"abstract":"Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge Computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102570","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: 4

Analyzing Eye-movements of Drivers with Different Experiences When Making a Turn 不同经验驾驶员转弯时眼球运动分析

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00085

Bo Wu, Shoji Nishimura, Qun Jin, Yishui Zhu

{"title":"Analyzing Eye-movements of Drivers with Different Experiences When Making a Turn","authors":"Bo Wu, Shoji Nishimura, Qun Jin, Yishui Zhu","doi":"10.1109/MSN50589.2020.00085","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00085","url":null,"abstract":"The driver’s driving experience is one of the important factors affecting his or her behaviors. Prior studies have noted the effect of driving experiences on drivers’ eye-movements when right-turning. However, related studies usually focused on the accident scenarios, for drivers’ eye-movements on daily driving when facing right-turn, the effects of drivers’ experience remain unknown. Therefore, according to design and apply a set of experiments, this paper focused on the analysis of driver’s eye-movements during the daily right-turn task to compare the differences between Experienced and Novice drivers. A total of 10 drivers were invited and be classified into two groups (Experienced and Novice) to participate in the experiments. All of the drivers are driving on the right-hand side of the road, and the steering wheel is on the left side of the vehicle. The aimed data were collected by a set of glasses type eye-tracker and be further classified into four driving vision-based AOI (Areas of Interest). The results of Mann-Whitney U-tests showed that Novice drivers have a disordered line of sight and tend to spend more attention on their right view and switch their line of sight back and forth between the AOIs. Moreover, Experienced drivers more tend to keep their view directly in front of their heads instead of using the peripheral vision. The results of this study may provide guidelines to prevent accidents in Advanced Driver Assistance Systems (ADAS) and offers useful insights for the training of new drivers.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115311647","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: 2

Detection of malicious nodes in drone ad-hoc network based on supervised learning and clustering algorithms 基于监督学习和聚类算法的无人机自组网恶意节点检测

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00037

Shanshan Sun, Zuchao Ma, Liang Liu, Hang Gao, Jianfei Peng

{"title":"Detection of malicious nodes in drone ad-hoc network based on supervised learning and clustering algorithms","authors":"Shanshan Sun, Zuchao Ma, Liang Liu, Hang Gao, Jianfei Peng","doi":"10.1109/MSN50589.2020.00037","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00037","url":null,"abstract":"Multi-drone swarm has been widely used in disaster monitoring, mapping and remote sensing, national defense military and other fields, and has become a research hotspot in recent years. Due to the openness of its operating environment, attackers can invade the control system to capture drone, and then carry out data attacks such as tamper attack, drop attack and replay attack in drone ad-hoc network, which causes a great threat to the security of drone network. Existing malicious nodes detection algorithms are not efficient when applied to drone ad-hoc network, for the following reasons: (1) The malicious node detection algorithms based on reputation usually adopt a static threshold to determine whether a node is malicious, which is inefficient in dynamic drone network. (2) Mutual cooperation based malicious node detection algorithms rely on the high meeting probability of nodes. In order to solve the above problems, we propose a Malicious Drones Detection Algorithm(MDA) based on supervised learning and clustering algorithms. The ground station calculates the reputation value of each routing path according to the received packets from different source nodes, and then evaluates the reputation value of drones with linear regression algorithm. Finally, gaussian clustering algorithm is used to cluster drones and find out malicious drones. Experiments were conducted in indoor and outdoor drone network. The experimental results indicate that the accuracy of MDA outperforms the existing methods by 10% 20%. And in the case of fewer malicious nodes, the accuracy can reach more than 90%, and the error rate is less than 10%.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123253566","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: 7