Adaptive aggregation based IoT traffic patterns for optimizing smart city network performance

Amin S. Ibrahim

doi:10.1016/j.aej.2022.03.037

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Adaptive aggregation based IoT traffic patterns for optimizing smart city network performance

2022-12-01

DOI：10.1016/j.aej.2022.03.037

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Abstract

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/).

Keywords:

IoT

Adaptive aggregation

IoT Traffic characteristics

Smart city

Traffic aggregation

Performance evaluation

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