An Optimization Framework for Data Collection in Software Defined Vehicular Networks
Date
Authors
Wijesekara, Patikiri Arachchige Don Shehan Nilmantha
Sudheera, Kalupahana Liyanage Kushan
Sandamali, Gammana Guruge Nadeesha
Chong, Peter Han Joo
Supervisor
Item type
Journal Article
Degree name
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Volume Title
Publisher
MDPI AG
Abstract
A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.Description
Keywords
SDVN, data collection, optimization, vehicular network, 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering
Source
Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 23(3), 1600-. doi: 10.3390/s23031600
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).