Improving Data Aggregation in IoT Sensor Networks Using Self-Organizing Maps and Firefly Optimization Algorithm Article Swipe
Hassan Sh. Alshehri
,
Fuad Bajaber
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25237107
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25237107
Internet of Things (IoT) sensor networks comprise diminutive sensor units primarily designed for monitoring phenomena within a designated area. However, reaching the complete potential of this kind of network is extremely difficult due to several challenges, including the fact that the data transmitted by the sensor nodes contains a large amount of duplicates. Data aggregation can be employed to address this issue in routing packets from nodes that send data to the base station (BS). In this study, a novel, hybrid data aggregation framework for IoT sensor networks is proposed by integrating Self-Organizing Maps (SOMs) with the Firefly Optimization Algorithm (FOA). The core motivation for this integration is to address persistent challenges in IoT sensor networks, chiefly energy efficiency, network longevity, and the reliability of data transmission. By combining the adaptive, unsupervised clustering capabilities of SOMs with the robust, multi-objective optimization properties of the FOA, the method aims to achieve more intelligent, adaptive, and practical solutions for real-world IoT systems. This work presents an innovative framework that synergistically leverages the strengths of FOA and SOM, offering a new methodology that addresses key challenges in scalable and energy-efficient IoT sensor network clustering. The suggested algorithm’s validity has been verified using an experimental analysis performed in MATLAB. Experimental results show the proposed method extends network lifetime by 15% and reduces energy consumption by 10% compared to FOA, SOM, and LEACH benchmarks. A notable classification rate was attained after implementing and testing the proposed method using the Intel Berkeley Research Lab dataset.
Related Topics
Concepts
Wireless sensor network
Computer science
Data aggregator
Cluster analysis
Scalability
Firefly algorithm
Network packet
Energy consumption
Routing (electronic design automation)
Key (lock)
Base station
Reliability (semiconductor)
Distributed computing
Computer network
Efficient energy use
Optimization problem
Real-time computing
Data mining
Internet of Things
Key distribution in wireless sensor networks
Energy (signal processing)
Hot spot (computer programming)
Data transmission
Yarn
Data integration
Big data
Mobile wireless sensor network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25237107
- https://www.mdpi.com/1424-8220/25/23/7107/pdf?version=1763718344
- OA Status
- gold
- References
- 32
- OpenAlex ID
- https://openalex.org/W7106271213
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106271213Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s25237107Digital Object Identifier
- Title
-
Improving Data Aggregation in IoT Sensor Networks Using Self-Organizing Maps and Firefly Optimization AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-21Full publication date if available
- Authors
-
Hassan Sh. Alshehri, Fuad BajaberList of authors in order
- Landing page
-
https://doi.org/10.3390/s25237107Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/25/23/7107/pdf?version=1763718344Direct link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/23/7107/pdf?version=1763718344Direct OA link when available
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Wireless sensor network, Computer science, Data aggregator, Cluster analysis, Scalability, Firefly algorithm, Network packet, Energy consumption, Routing (electronic design automation), Key (lock), Base station, Reliability (semiconductor), Distributed computing, Computer network, Efficient energy use, Optimization problem, Real-time computing, Data mining, Internet of Things, Key distribution in wireless sensor networks, Energy (signal processing), Hot spot (computer programming), Data transmission, Yarn, Data integration, Big data, Mobile wireless sensor networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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32Number of works referenced by this work
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| abstract_inverted_index.By | 127 |
| abstract_inverted_index.In | 75 |
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| abstract_inverted_index.be | 56 |
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| abstract_inverted_index.in | 62, 112, 183, 203 |
| abstract_inverted_index.is | 29, 88, 107 |
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| abstract_inverted_index.10% | 221 |
| abstract_inverted_index.15% | 215 |
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| abstract_inverted_index.IoT | 85, 113, 158, 187 |
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| abstract_inverted_index.The | 101, 191 |
| abstract_inverted_index.and | 121, 153, 173, 185, 216, 226, 237 |
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| abstract_inverted_index.key | 181 |
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| abstract_inverted_index.was | 233 |
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| abstract_inverted_index.FOA, | 144, 224 |
| abstract_inverted_index.Maps | 93 |
| abstract_inverted_index.SOM, | 174, 225 |
| abstract_inverted_index.SOMs | 135 |
| abstract_inverted_index.This | 160 |
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| abstract_inverted_index.base | 72 |
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| abstract_inverted_index.core | 102 |
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| abstract_inverted_index.show | 207 |
| abstract_inverted_index.that | 39, 67, 166, 179 |
| abstract_inverted_index.this | 25, 60, 76, 105 |
| abstract_inverted_index.with | 95, 136 |
| abstract_inverted_index.work | 161 |
| abstract_inverted_index.(BS). | 74 |
| abstract_inverted_index.(IoT) | 3 |
| abstract_inverted_index.Intel | 244 |
| abstract_inverted_index.LEACH | 227 |
| abstract_inverted_index.after | 235 |
| abstract_inverted_index.area. | 18 |
| abstract_inverted_index.issue | 61 |
| abstract_inverted_index.large | 49 |
| abstract_inverted_index.nodes | 46, 66 |
| abstract_inverted_index.units | 9 |
| abstract_inverted_index.using | 198, 242 |
| abstract_inverted_index.(FOA). | 100 |
| abstract_inverted_index.(SOMs) | 94 |
| abstract_inverted_index.Things | 2 |
| abstract_inverted_index.amount | 50 |
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| abstract_inverted_index.hybrid | 80 |
| abstract_inverted_index.method | 146, 210, 241 |
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| abstract_inverted_index.Firefly | 97 |
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| abstract_inverted_index.address | 59, 109 |
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| abstract_inverted_index.network | 28, 119, 189, 212 |
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| abstract_inverted_index.results | 206 |
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| abstract_inverted_index.testing | 238 |
| abstract_inverted_index.Berkeley | 245 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.Internet | 0 |
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| abstract_inverted_index.compared | 222 |
| abstract_inverted_index.complete | 22 |
| abstract_inverted_index.comprise | 6 |
| abstract_inverted_index.contains | 47 |
| abstract_inverted_index.dataset. | 248 |
| abstract_inverted_index.designed | 11 |
| abstract_inverted_index.employed | 57 |
| abstract_inverted_index.lifetime | 213 |
| abstract_inverted_index.networks | 5, 87 |
| abstract_inverted_index.offering | 175 |
| abstract_inverted_index.presents | 162 |
| abstract_inverted_index.proposed | 89, 209, 240 |
| abstract_inverted_index.reaching | 20 |
| abstract_inverted_index.scalable | 184 |
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| abstract_inverted_index.verified | 197 |
| abstract_inverted_index.Algorithm | 99 |
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| abstract_inverted_index.addresses | 180 |
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| abstract_inverted_index.including | 36 |
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| abstract_inverted_index.networks, | 115 |
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| abstract_inverted_index.potential | 23 |
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| abstract_inverted_index.strengths | 170 |
| abstract_inverted_index.suggested | 192 |
| abstract_inverted_index.challenges | 111, 182 |
| abstract_inverted_index.clustering | 132 |
| abstract_inverted_index.designated | 17 |
| abstract_inverted_index.diminutive | 7 |
| abstract_inverted_index.innovative | 164 |
| abstract_inverted_index.longevity, | 120 |
| abstract_inverted_index.monitoring | 13 |
| abstract_inverted_index.motivation | 103 |
| abstract_inverted_index.persistent | 110 |
| abstract_inverted_index.properties | 141 |
| abstract_inverted_index.real-world | 157 |
| abstract_inverted_index.aggregation | 54, 82 |
| abstract_inverted_index.benchmarks. | 228 |
| abstract_inverted_index.challenges, | 35 |
| abstract_inverted_index.clustering. | 190 |
| abstract_inverted_index.consumption | 219 |
| abstract_inverted_index.duplicates. | 52 |
| abstract_inverted_index.efficiency, | 118 |
| abstract_inverted_index.integrating | 91 |
| abstract_inverted_index.integration | 106 |
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| abstract_inverted_index.Optimization | 98 |
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| abstract_inverted_index.experimental | 200 |
| abstract_inverted_index.implementing | 236 |
| abstract_inverted_index.intelligent, | 151 |
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| abstract_inverted_index.algorithm’s | 193 |
| abstract_inverted_index.transmission. | 126 |
| abstract_inverted_index.classification | 231 |
| abstract_inverted_index.Self-Organizing | 92 |
| abstract_inverted_index.multi-objective | 139 |
| abstract_inverted_index.synergistically | 167 |
| abstract_inverted_index.energy-efficient | 186 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.84432753 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |