2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)最新文献

Summarizing the Slices: Sample-Based Core-Periphery Classification on Complex Networks 切片总结:基于样本的复杂网络核心-外围分类

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00049

Bo Yan, Wenli Tang, J. Liu, Yiping Liu, Fanku Meng, H. Su

引用次数: 0

A Lightweight Hash Function Based on Cellular Automata for Mobile Network 移动网络中基于元胞自动机的轻量级哈希函数

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00055

Xing Zhang, Qinbao Xu, Xiao Wei Li, Changda Wang

引用次数: 4

Encoding Space to Count Multi-Targets with Multiplexed Binary Infrared Sensors 多路二进制红外传感器多目标计数的编码空间

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00080

Longxiang Luo, Yang Xiao, W. Liang

引用次数: 1

Mobility Pattern-Aware Task Recommendation for Taxi Crowdsourcing Delivery 基于出行模式感知的出租车众包配送任务推荐

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00043

Pengfei Wang, Ruiyun Yu

引用次数: 1

Addressing the Conflict of Negative Feedback and Sampling for Online Ad Recommendation in Mobile Social Networks 移动社交网络中在线广告推荐负反馈与抽样的冲突解决

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00039

Yu Tao, Yuanxing Zhang, Jianing Lin, Kaigui Bian

{"title":"Addressing the Conflict of Negative Feedback and Sampling for Online Ad Recommendation in Mobile Social Networks","authors":"Yu Tao, Yuanxing Zhang, Jianing Lin, Kaigui Bian","doi":"10.1109/MSN48538.2019.00039","DOIUrl":"https://doi.org/10.1109/MSN48538.2019.00039","url":null,"abstract":"Online advertisement (ad) recommendation in the mobile social network (MSN) is an uprising interest of research. Compared to traditional recommendation systems, one of its major difference is the presence of explicit negative feedback from users (e.g., a user does not click an ad, or she/he does not like it). On the other hand, most methods utilize negative sampling (e.g., randomly sampling an item that a user never interacts with to avoid overfitting, that is, she/he is assumed to dislike it) while training conventional recommendation systems. This may lead to a conflict between negative feedback and sampling, as they should be treated differently, but they are considered as the same if traditional methods are directly applied for online ad recommendation. In this paper, we present AdRec, a novel framework of online ad recommendation in MSN to address this conflict. We introduce an auxiliary output and modify the loss function to assign different weights to negative samples and feedbacks. A theoretical analysis is applied to show the efficiency of our design, and experiments on real world datasets demonstrate that our proposed method outperforms several state-of-the-art approaches.","PeriodicalId":368318,"journal":{"name":"2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126555962","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

RF Aerially Charging Scheduling for UAV Fleet : A Q-Learning Approach 无人机机群射频空中充电调度:q -学习方法

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00046

Jinwei Xu, K. Zhu, Ran Wang

{"title":"RF Aerially Charging Scheduling for UAV Fleet : A Q-Learning Approach","authors":"Jinwei Xu, K. Zhu, Ran Wang","doi":"10.1109/MSN48538.2019.00046","DOIUrl":"https://doi.org/10.1109/MSN48538.2019.00046","url":null,"abstract":"In recent years, unmanned aerial vehicles (UAVs) have attracted extensive interests from both academia and industry due to the potential wide applications with universal applicable nature of the deployment. However, currently the bottleneck for UAVs is the limited carried energy resources (e.g. oil box, battery), especially for electric-driven UAVs. For a system consisting of multiple UAVs using batteries, its stability depends on each UAV. Therefore, the lifetime of each UAV is expected to be extended. In this paper, we propose the concept of RF charging aerially for the UAV fleet. Specifically, in order to ensure the stability of the system, wireless charging is considered for enhancing the lifetime of each UAV. However, it may be unbalanced. Accordingly, the issue of charging scheduling arises. The problem is formulated as a Q-Learning problem in this paper. Agent constantly explores and optimizes its scheduling policy. Finally, it can adapt to different UAV distribution situations. We take the energy levels of UAVs as input, which is easy for implementation. We have compared with two other algorithms (RSA and LESA) and compared with the case of no-charging. The results show that comparing with no-charging, the stability of the system can be improved by up to 78%. Compared with RSA and LESA, system stability is increased by up to 30%-40%. In addition, our method is more flexible and applicable to fleet than other ways (such as return to base station, landing to power line, ground laser, etc) to supplement energy.","PeriodicalId":368318,"journal":{"name":"2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134547539","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

AMRnet: A Real-Time Automatic Modulation Recognition Network for Wireless Communication System 无线通信系统的实时自动调制识别网络

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00031

Xinyu Li, Pengrui Duan

{"title":"AMRnet: A Real-Time Automatic Modulation Recognition Network for Wireless Communication System","authors":"Xinyu Li, Pengrui Duan","doi":"10.1109/MSN48538.2019.00031","DOIUrl":"https://doi.org/10.1109/MSN48538.2019.00031","url":null,"abstract":"In recent years, deep learning has brought new opportunities and challenges to the wireless communication field, owing to its expressive capacity and convenient optimization capability. In this paper, we propose a real-time Automatic Modulation Recognition Network (AMRnet) for signal transmission process, which can be easily applied to mobile and embedded wireless communication applications (e.g., The GNU Radio system based on USRP platform). Our proposed AMRnet consists of a series of convolutional neural networks based on the design theory of lightweight networks. More specifically, depthwise separable convolution structure is used to reduce computational complexity of the whole network. Residual block is included to improve the robustness for the simple backbone network. In addition, channel attention mechanism is added to lay more emphasis on the channel-wise information due to the unique data futures of the I/Q signal. We present numerous experiments on resource and accuracy tradeoffs and show strong performance compared to a number of state-of-the-art methods. This work will have a great impact on the future communication systems.","PeriodicalId":368318,"journal":{"name":"2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134346042","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

Multi-carrier Measurement Study of Mobile Network Latency: The Tale of Hong Kong and Helsinki 移动网络延迟的多载波测量研究:香港和赫尔辛基的故事

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00015

Tristan Braud, Teemu Kämäräinen, M. Siekkinen, P. Hui

引用次数: 8

Exploratory Community Detection: Finding Communities in Unknown Networks 探索性社区检测:在未知网络中发现社区

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00048

Bo Yan, Fanku Meng, J. Liu, Yiping Liu, H. Su

引用次数: 0

An Optimization Deployment Scheme for Static Charging Piles Based on Dynamic of Shared E-Bikes 基于动态的共享电动自行车静态充电桩优化配置方案

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2019-12-01 DOI: 10.1109/MSN48538.2019.00071

Ping Zhong, Aikun Xu, Yuanming Chen, Feng Gao, Guihua Duan

引用次数: 2