2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)最新文献_第2页

Optimal Content Caching Policy Considering Mode Selection and User Preference under Overlay D2D Communications 覆盖D2D通信下考虑模式选择和用户偏好的最优内容缓存策略

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00035

Yue Wang, Guangsheng Feng, Junyu Lin, Haibin Lv, Jiayu Sun, Huiqiang Wang, Zihan Gao

引用次数: 3

Configuration of the C-V2X Mode 4 Sidelink PC5 Interface for Vehicular Communication 车载通信C-V2X Mode 4副链路PC5接口的配置

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00014

Rafael Molina-Masegosa, J. Gozálvez, M. Sepulcre

引用次数: 64

Energy-Aware Service Composition of Configurable IoT Smart Things 可配置物联网智能事物的能源感知服务组成

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00013

Mengyu Sun, Zhangbing Zhou, Yucong Duan

{"title":"Energy-Aware Service Composition of Configurable IoT Smart Things","authors":"Mengyu Sun, Zhangbing Zhou, Yucong Duan","doi":"10.1109/MSN.2018.00013","DOIUrl":"https://doi.org/10.1109/MSN.2018.00013","url":null,"abstract":"This paper presents a three-tier framework to facilitate the composition of Internet-of-Things (IoT) services, where these IoT services represent functionalities provided by heterogenous smart things. Various IoT services are categorized into service classes through the categorization of their functionalities. A service network is constructed by considering the invocation relationship between service classes, and service class chains are generated using traditional Web service composition techniques to satisfy the requirement from the functional perspective only. Considering the factors, including spatial and temporal constraints, energy efficiency, and the functional configurability, IoT service composition can be reduced to a multi-objective optimization problem. Heuristic algorithms, such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), are adopted to search for optimal IoT service compositions. Experimental results show that PSO performs better than GA and ACO in searching for approximately optimal IoT service compositions and reducing the energy consumption of smart things in the network.","PeriodicalId":264541,"journal":{"name":"2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133318191","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

Smartphone-Assisted Over-Air Reprogramming Based on Visible Light Communication 基于可见光通信的智能手机辅助空中重编程

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00025

J. Qiu, Sai Li, Pan Zheng, Jing Fan

引用次数: 0

[Publisher's information] (发布者的信息)

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/msn.2018.00044

引用次数: 0

MSN 2018 Technical Program Committee MSN 2018技术计划委员会

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/msn.2018.00006

引用次数: 0

GTC Forest: An Ensemble Method for Network Structured Data Classification GTC森林:网络结构化数据分类的集成方法

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00020

Jinxi Wang, Bo Hu, Xiang Li, Zhen Yang

{"title":"GTC Forest: An Ensemble Method for Network Structured Data Classification","authors":"Jinxi Wang, Bo Hu, Xiang Li, Zhen Yang","doi":"10.1109/MSN.2018.00020","DOIUrl":"https://doi.org/10.1109/MSN.2018.00020","url":null,"abstract":"In recent years, deep neural networks have achieved great success in various applications, particularly in visual tasks such as image classification. However, deep neural networks cannot reach their full potential when dealing with classification problems in networks. Because the network-related dataset is usually in a structured format rather than an image format, and in some cases the data scale is small to train a deep model. Therefore, we aim at another choice, which can abandon the structure of deep neural networks, but remain the powerful representation learning ability. In this paper, we propose GTC Forest, a tree-based ensemble method for network structured data classification. GTC Forest consists of two parts: the first part Multi-Grained Traversing to do representation learning in network structured data; and the second part Cascade Forest to train on small-scale dataset, as well as reducing model complexity. Experiments are conducted on user broadband dataset, which is built to guarantee users a better Internet experience. And the results prove that our model is effective, and has higher accuracy than other machine learning methods in network structured data classification.","PeriodicalId":264541,"journal":{"name":"2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127147222","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

Influence Maximization with Trust Relationship in Social Networks 基于信任关系的社会网络影响最大化

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00017

Nan Wang, Jiansong Da, Jinbao Li, Yong Liu

引用次数: 3

A Deployment Model of Charging Pile Based on Random Forest for Shared Electric Vehicle in Smart Cities 基于随机森林的智慧城市共享电动汽车充电桩部署模型

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.00015

Tiantian Xu, Huazhi Sun, Guangjie Han, Chunmei Ma, Lifen Jiang

{"title":"A Deployment Model of Charging Pile Based on Random Forest for Shared Electric Vehicle in Smart Cities","authors":"Tiantian Xu, Huazhi Sun, Guangjie Han, Chunmei Ma, Lifen Jiang","doi":"10.1109/MSN.2018.00015","DOIUrl":"https://doi.org/10.1109/MSN.2018.00015","url":null,"abstract":"In the smart cities, sharing electric vehicles have many advantages such as environmental protection, low carbon emission, and high efficiency, which will greatly facilitate human life. However, whether the shared electric vehicle can be charged in time directly affects the users' experience. Based on this fact, we study that how to deploy the charging piles in the parking station of the shared electric vehicle, and propose a charging pile deployment model. First, the number of shared electric vehicles that check out from parking stations is predicted based on the random forest algorithm. Then, a mobile model of the shared electric vehicle is established. Based on the mobile model, the number of shared electric vehicles that check in within the target period is calculated and whether they reach the starvation state are determined. After that, the number of charging piles to be deployed is determined. Finally, the effects of hunger rate, battery capacity and the distance between parking stations on the number of charging piles were simulated by experiments. The experimental results verify the validity and feasibility of the proposed model. The prediction model proposed in this paper can provide a certain decision basis for the sharing of electric vehicle charging pile planning.","PeriodicalId":264541,"journal":{"name":"2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"41 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120838678","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

Reputation and Incentive Mechanism for SDN Applications SDN应用的声誉和激励机制

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) Pub Date : 2018-12-01 DOI: 10.1109/MSN.2018.000-3

Yufu Wang, Yuan Liu, Jinzong Hu, Mingwei Zhang, Xingwei Wang

引用次数: 1