.png){kind=icon}
返回
Adaptive aggregation based IoT traffic patterns for optimizing smart city network performance
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2022.03.037.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Internet of Things (IoT) technology drives lifestyle changes in smart city infrastructure due to rapid growth of services and activities that develop well-being. One of the main challenges that resulted from the exponential spread of IoT is the dense number of nodes with the huge amount of data over different networks that influence the collision probability, and network congestion. Existing aggregation techniques worked to solve these challenges with overlooking the IoT traffic characteristics and their types. In this paper, adaptive aggregation techniques are proposed based on IoT traffic types to overcome IoT network issues. These techniques can abstract data, reduce number of packets sent with low traffic congestion, and reduce the recurring packet headers. The proposed adaptive aggregation techniques are accomplished over the IoT smart city networks that are architectured and practically tested to examine the simulation results. It is anticipated that the adaptive aggregation results could optimize the operational efficiency of IoT smart city networks in most key performance metrics, compared to the existing aggregation techniques.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword:
IoT
Adaptive aggregation
IoT Traffic characteristics
Smart city
Traffic aggregation
Performance evaluation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
6.3K
被引数:
2.6W
机构
引用论文
Determination of Cr(III) and Cr(VI) species by using dual mini columns and Inductively Coupled Plasma-AtomicEmission Spectrometry (ICP-AES).png)
Talanta
TalantaIF0
PREAI
分享收藏
分享收藏
查看更多内容