2021 IEEE 46th Conference on Local Computer Networks (LCN)最新文献_第7页

Breach Path Detection Reliability in Energy Harvesting Wireless Sensor Networks 能量采集无线传感器网络缺口路径检测可靠性研究

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9525018

Salwa Abougamila, Mohammed Elmorsy, E. Elmallah

引用次数: 0

Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing 基于迁移学习的5G RAN切片加速深度强化学习

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524965

A. M. Nagib, H. Abou-zeid, H. Hassanein

引用次数: 17

Predictive Analytics Integrated Multi-level Optimization of Offshore Connectivity in Ocean Network 基于预测分析的海洋网络海上互联互通多级优化

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/lcn52139.2021.9525021

S. Surendran, M. Ramesh, A. Montresor

{"title":"Predictive Analytics Integrated Multi-level Optimization of Offshore Connectivity in Ocean Network","authors":"S. Surendran, M. Ramesh, A. Montresor","doi":"10.1109/lcn52139.2021.9525021","DOIUrl":"https://doi.org/10.1109/lcn52139.2021.9525021","url":null,"abstract":"One of the primary difficulties of fishermen engaged in deep-sea fishing is the lack of effective communication systems to the shore. The Offshore Communication Network(OCN) resolves this problem by providing Internet over the ocean through a fishing vessel network. OCN is a multi-layered architecture with heterogeneous connectivity ranges, directionality, resources, and mobility patterns. Connectivity maintenance is challenging due to the lack of infrastructure, expanded mobility, network sparsity, and sea-wave-induced movements. This paper discusses a framework to improve OCN connectivity with a multi-level optimization strategy. We propose a predictive model to generate real-time forecasts of link status. At the physical level, node position re-orientations to higher connectivity locations are suggested. The transmission queue management and prioritized scheduling in the link-layer minimize the queuing delay. A reinforcement routing strategy in the network layer determines the best next-hop for message dissemination. The proposed three-level optimization approach facilitates communication capability enhancement in OCN.","PeriodicalId":222740,"journal":{"name":"2021 IEEE 46th Conference on Local Computer Networks (LCN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114543102","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: 1

An Incentive Approach in Mobile Crowdsensing for Perceptual User 面向感知用户的移动众感激励方法

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524951

Lu Chen, De-gan Zhang, Jie Zhang, Ting Zhang, Jinyu Du, Hong-rui Fan

引用次数: 0

Python-Based TinyIPFIX in Wireless Sensor Networks 无线传感器网络中基于python的TinyIPFIX

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9525017

Eryk Schiller, Ramon Huber, B. Stiller

引用次数: 0

Provisioning Differentiated QoS for NVMe over Fabrics 通过fabric为NVMe提供差异化QoS

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524967

J. Biswas, Jit Gupta, K. Kant, Amitangshu Pal, D. Minturn

引用次数: 1

A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking 可靠区块链网络中对等连通性估计的机器学习方法

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9525012

Jinoh Kim, Makiya Nakashima, Wenjun Fan, Simeon Wuthier, Xiaobo Zhou, Ikkyun Kim, Sang-Yoon Chang

{"title":"A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking","authors":"Jinoh Kim, Makiya Nakashima, Wenjun Fan, Simeon Wuthier, Xiaobo Zhou, Ikkyun Kim, Sang-Yoon Chang","doi":"10.1109/LCN52139.2021.9525012","DOIUrl":"https://doi.org/10.1109/LCN52139.2021.9525012","url":null,"abstract":"Peer connectivity plays a significant role in a blockchain network since any poor connectivity may result in the nodes operating on outdated data (e.g., cryptocurrency transactions). Although connectivity information is maintained by individual nodes, such identifier-based information might be unreliable due to the possibility of bogus identifiers. This paper tackles the problem of peer connectivity estimation through data-driven analytics of blockchain traffic for reliable blockchain networking. We define a set of variables to represent traffic characteristics and estimate peer connectivity from the collected data using a machine learning methodology. We also investigate the feasibility of feature prioritization to minimize estimation complexities. Our experimental results show that the presented estimation mechanism makes accurate predictions, with less than 0.1 difference between the measurement and estimation for over 99.7% of predictions. The time complexity measured on a commodity machine shows a microsecond scale for completing a single prediction task, enabling real-time operations.","PeriodicalId":222740,"journal":{"name":"2021 IEEE 46th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130485545","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

Upper Bounds on Path Exposure in EH-WSNs with Variable Transmission Ranges 可变传输范围EH-WSNs的路径暴露上限

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524991

Abdulsalam Basabaa, E. Elmallah

引用次数: 1

Delay-Aware DNN Inference Throughput Maximization in Edge Computing via Jointly Exploring Partitioning and Parallelism 基于分区和并行性联合探索的边缘计算延迟感知DNN推理吞吐量最大化

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524928

Jing Li, W. Liang, Yuchen Li, Zichuan Xu, X. Jia

{"title":"Delay-Aware DNN Inference Throughput Maximization in Edge Computing via Jointly Exploring Partitioning and Parallelism","authors":"Jing Li, W. Liang, Yuchen Li, Zichuan Xu, X. Jia","doi":"10.1109/LCN52139.2021.9524928","DOIUrl":"https://doi.org/10.1109/LCN52139.2021.9524928","url":null,"abstract":"Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed on the local IoT device of the request; and another part is processed on a cloudlet (server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet in which the request is assigned.In this paper, we study a novel delay-aware DNN inference throughput maximization problem with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN model partitioning and multi-thread parallelism of DNN inference. To this end, we first show that the problem is NP-hard. We then devise a constant approximation algorithm for it. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising.","PeriodicalId":222740,"journal":{"name":"2021 IEEE 46th Conference on Local Computer Networks (LCN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115307341","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: 6

Anomaly Detection for Discovering Performance Degradation in Cellular IoT Services 用于发现蜂窝物联网服务性能下降的异常检测

2021 IEEE 46th Conference on Local Computer Networks (LCN) Pub Date : 2021-10-01 DOI: 10.1109/LCN52139.2021.9524931

Dimitar Minovski, C. Åhlund, Karan Mitra, Irina Cotanis

引用次数: 1