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Published VersionCC BY V4.0, Open Access
Journal article
Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management
Sensors , Vol.23(5), pp.1-26
2023
PMID: 36905062
Abstract
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.
Details
- Title
- Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management
- Authors
- Gerald K. Ijemaru (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringLi-Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKah Phooi Seng (Author) - Xi’an Jiaotong-Liverpool University
- Publication details
- Sensors , Vol.23(5), pp.1-26
- Publisher
- MDPI AG
- Date published
- 2023
- DOI
- 10.3390/s23052860
- ISSN
- 1424-8220
- PMID
- 36905062
- Copyright note
- © 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/).
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99711688402621
- Output Type
- Journal article
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