ICC 2019 - 2019 IEEE International Conference on Communications (ICC)最新文献_第2页

Sliced-RAN: Joint Slicing and Functional Split in Future 5G Radio Access Networks 切片ran:未来5G无线接入网络中的联合切片和功能分裂

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761081

Behnam Ojaghi, F. Adelantado, E. Kartsakli, A. Antonopoulos, C. Verikoukis

引用次数: 26

Context-Aware Adaptive Authentication and Authorization in Internet of Things 物联网环境感知自适应认证与授权

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761830

A. Arfaoui, S. Cherkaoui, A. Kribèche, S. Senouci, Mohamed Hamdi

{"title":"Context-Aware Adaptive Authentication and Authorization in Internet of Things","authors":"A. Arfaoui, S. Cherkaoui, A. Kribèche, S. Senouci, Mohamed Hamdi","doi":"10.1109/ICC.2019.8761830","DOIUrl":"https://doi.org/10.1109/ICC.2019.8761830","url":null,"abstract":"The rapid technological advancements in wireless communications, ubiquitous sensing and mobile networking have paved the way for the emergence of the Internet of Things (IoT) era, where “anything” can be connected “anywhere” at “anytime”. However, the flourish of IoT still faces various security and privacy preserving challenges that need to be addressed. In such pervasive and heterogeneous environment where the context conditions dynamically and frequently change, efficient and context-aware mechanisms are required to meet the users' changing needs. Therefore, it seems crucial to design an adaptive access control scheme in order to remotely control smart things while considering the dynamic context changes. In this paper, we propose a Context-Aware Attribute-Based Access Control (CAABAC) approach that incorporates the contextual information with the Ciphertext-Policy Attribute-based Encryption (CP-ABE) to ensure data security and provide an adaptive contextual privacy. From a security perspective, the proposed scheme satisfies the security requirements such as confidentiality, context-aware privacy, and resilience against key escrow problem. Performance analysis proves the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of storage, communication and computational cost.","PeriodicalId":402732,"journal":{"name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129494568","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: 14

Energy-Aware Task Allocation for Mobile IoT by Online Reinforcement Learning 基于在线强化学习的移动物联网能量感知任务分配

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761509

Jingjing Yao, N. Ansari

{"title":"Energy-Aware Task Allocation for Mobile IoT by Online Reinforcement Learning","authors":"Jingjing Yao, N. Ansari","doi":"10.1109/ICC.2019.8761509","DOIUrl":"https://doi.org/10.1109/ICC.2019.8761509","url":null,"abstract":"Fog-aided Internet of Things (IoT) networks provide low latency IoT services by offloading computational intensive and delay sensitive tasks to the fog nodes, which are deployed close to the IoT devices. Mobile IoT relies on battery limited mobile IoT devices (e.g., wearable devices and smartphones) to provision networks with enhanced flexibility. Mobile IoT faces the challenges of varying wireless channel conditions and hence may degrade the quality of service (QoS). We investigate the task allocation, which intelligently distributes tasks to different fog nodes and adapts to IoT varying mobile environment, such that the average task completion latency, constrained by QoS requirements and mobile IoT device battery capacity, is minimized. An integer linear programming (ILP) problem is then formulated to solve this problem. However, it is difficult to obtain the user mobility patterns (i.e., future locations where tasks are offloaded) and user side information (i.e., task length and computing intensity). Therefore, we propose an online learning algorithm to engineer task allocation decisions and then demonstrate its performances by extensive simulations.","PeriodicalId":402732,"journal":{"name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129895396","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: 11

An Architecture for Software Defined Drone Networks 软件定义无人机网络的体系结构

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761968

Mohannad A. Alharthi, A. Taha, H. Hassanein

引用次数: 17

Learning Multiple Primary Transmit Power Levels for Smart Spectrum Sharing 基于智能频谱共享的多主发射功率学习

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761201

Rui Zhang, Peng Cheng, Zhuo Chen, Yonghui Li, B. Vucetic

引用次数: 0

A Zero Site-Survey Overhead Indoor Tracking System using Particle Filter 基于粒子滤波的零站点测量架空室内跟踪系统

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761621

Feiyu Jin, Kai Liu, Hao Zhang, Weiwei Wu, Jingjing Cao, X. Zhai

{"title":"A Zero Site-Survey Overhead Indoor Tracking System using Particle Filter","authors":"Feiyu Jin, Kai Liu, Hao Zhang, Weiwei Wu, Jingjing Cao, X. Zhai","doi":"10.1109/ICC.2019.8761621","DOIUrl":"https://doi.org/10.1109/ICC.2019.8761621","url":null,"abstract":"With rapid development of Internet of Things (IoT) and pervasive computing, indoor localization and tracking has attracted considerable attentions. This work aims at designing an effective and scalable indoor tracking system based on smart phones embedded with Wi-Fi interfaces and inertial sensors. Specifically, we first propose a zero site-survey overhead algorithm (ZSSO), which includes a step detection mechanism, a map constraint construction method and a customized particle filter. The step detection mechanism is used to count user steps based on raw data extracted from inertial sensors. The map constraint construction method is adopted to generate obstacle constraints of the indoor environment based on a two-step conversion method designed for indoor map. Finally, a customized particle filter is proposed to track user's positions continuously. Further, we propose an enhanced version of ZSSO (i.e., E-ZSSO) to improve tracking performance by incorporating with Wi-Fi fingerprint based localization technique. First, an automatic Wi-Fi fingerprint collection mechanism is developed for building the fingerprint database without extra site-survey overhead. Then, the Wi-Fi fingerprint based localization results are further adopted to speed up the convergence of the particle filter as well as to better calibrate the localization results. We have implemented the indoor tracking system in real-world environments and conducted comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of our proposed algorithms.","PeriodicalId":402732,"journal":{"name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116354229","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: 5

Missing Value Imputations by Rule-Based Incomplete Data Fuzzy Modeling 基于规则的不完全数据模糊建模缺失值估算

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761052

Xiaochen Lai, Xin Liu, Liyong Zhang, Chi Lin, M. Obaidat, K. Hsiao

引用次数: 2

Optimum Priority Class Selection Under Wi-Fi/LTE Coexistence Wi-Fi/LTE共存下的最优优先级选择

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8762024

I. Samy, Loukas Lazos

引用次数: 4

Cross-Layer Analysis of RFID Systems with Correlated Shadowing and Random Radiation Efficiency 具有相关阴影和随机辐射效率的RFID系统的跨层分析

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761060

R. Valentini, R. Alesii, M. Levorato, F. Santucci

引用次数: 4

Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection 基于大感知数据的入侵检测强化学习

ICC 2019 - 2019 IEEE International Conference on Communications (ICC) Pub Date : 2019-05-20 DOI: 10.1109/ICC.2019.8761575

S. Otoum, B. Kantarci, H. Mouftah

{"title":"Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection","authors":"S. Otoum, B. Kantarci, H. Mouftah","doi":"10.1109/ICC.2019.8761575","DOIUrl":"https://doi.org/10.1109/ICC.2019.8761575","url":null,"abstract":"Wireless sensor and actuator networks are widely adopted in various applications such as critical infrastructure monitoring where sensory data in big volumes and velocity are prone to security vulnerabilities for the network and the monitored infrastructure. Despite the vulnerabilities of the big data phenomenon, intelligent data analytics technique can enable the analysis of huge amount of data and identification of intrusive behavior in real time. The main performance targets for any Intrusion Detection System (IDS) involve accuracy, detection, precision, F<sub>1</sub> score and Receiver Operating Characteristics. Pursuant to these, this paper proposes a big data-driven IDS approach in Wireless Sensor Networks by harnessing reinforcement learning techniques on a hybrid IDS framework. We study the performance of RL-IDS and compare it to the previously proposed Adaptive Machine Learning-based IDS (AML-IDS) namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS). The experimental results show that RL-IDS can achieve  100% success in detection, accuracy and precision-recall rates whereas its predecessor ASCH-IDS performs with an accuracy level that is slightly above 99%.","PeriodicalId":402732,"journal":{"name":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127777461","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: 61