AROA: Adam Remora Optimization Algorithm and Deep Q network for energy harvesting in Fog-IoV network Article Swipe
Savita Lohat
,
Sheilza Jain
,
Rajender Kumar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.asoc.2023.110072
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.asoc.2023.110072
Electric Vehicles (EV) has gained immense popularity due to the increasing awareness amongst people regarding low carbon emission. Smart vehicles have become a central part of the Internet of vehicles (IoV) infrastructure. Whenever several vehicles distribute its tasks, then the classical centralized model meet various issues, such as security and delay in communication. This paper devises a technique for energy harvesting in the Fog-IoV network. The Fog-IoV network simulation is done for enhanced processing. The three layers, such as fog layer, cloud layer, and IoV layer are adapted for electricity trading. The power prediction is performed with a deep reinforcement learning technique, namely Deep Q network (DQN). The optimal electricity trading is done with the proposed Adam Remora Optimization Algorithm (AROA). The AROA is obtained by the amalgamation of Remora Optimization Algorithm (ROA) and Adam optimization algorithm. The EVs represents buyer of electricity that demands electricity in such a way that Road side unit (RSU) perform bidding. The fitness function is newly modelled using predicted power, price, and distance. The experimentation of the technique is done in terms of fitness, power, and pricing. The proposed AROA-based DQN offered enhanced performance with the highest power of 11.920 and the smallest pricing of 16.949%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.asoc.2023.110072
- OA Status
- hybrid
- Cited By
- 8
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319160660
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319160660Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.asoc.2023.110072Digital Object Identifier
- Title
-
AROA: Adam Remora Optimization Algorithm and Deep Q network for energy harvesting in Fog-IoV networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-03Full publication date if available
- Authors
-
Savita Lohat, Sheilza Jain, Rajender KumarList of authors in order
- Landing page
-
https://doi.org/10.1016/j.asoc.2023.110072Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.asoc.2023.110072Direct OA link when available
- Concepts
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Computer science, Bidding, Electricity, Aroa, Cloud computing, Global optimization, Algorithm, Real-time computing, Simulation, Electrical engineering, Escherichia coli, Enterobacteriaceae, Business, Marketing, Biochemistry, Gene, Chemistry, Operating system, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fog | 79 |
| abstract_inverted_index.for | 58, 71, 88 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.its | 36 |
| abstract_inverted_index.low | 15 |
| abstract_inverted_index.the | 9, 26, 39, 62, 114, 126, 172, 191, 197 |
| abstract_inverted_index.way | 149 |
| abstract_inverted_index.(EV) | 2 |
| abstract_inverted_index.AROA | 122 |
| abstract_inverted_index.Adam | 116, 134 |
| abstract_inverted_index.Deep | 103 |
| abstract_inverted_index.Road | 151 |
| abstract_inverted_index.This | 53 |
| abstract_inverted_index.deep | 98 |
| abstract_inverted_index.done | 70, 112, 175 |
| abstract_inverted_index.have | 20 |
| abstract_inverted_index.meet | 43 |
| abstract_inverted_index.part | 24 |
| abstract_inverted_index.side | 152 |
| abstract_inverted_index.such | 46, 77, 147 |
| abstract_inverted_index.that | 143, 150 |
| abstract_inverted_index.then | 38 |
| abstract_inverted_index.unit | 153 |
| abstract_inverted_index.with | 96, 113, 190 |
| abstract_inverted_index.(IoV) | 30 |
| abstract_inverted_index.(ROA) | 132 |
| abstract_inverted_index.(RSU) | 154 |
| abstract_inverted_index.Smart | 18 |
| abstract_inverted_index.buyer | 140 |
| abstract_inverted_index.cloud | 81 |
| abstract_inverted_index.delay | 50 |
| abstract_inverted_index.layer | 85 |
| abstract_inverted_index.model | 42 |
| abstract_inverted_index.newly | 161 |
| abstract_inverted_index.paper | 54 |
| abstract_inverted_index.power | 92, 193 |
| abstract_inverted_index.terms | 177 |
| abstract_inverted_index.three | 75 |
| abstract_inverted_index.using | 163 |
| abstract_inverted_index.(DQN). | 106 |
| abstract_inverted_index.11.920 | 195 |
| abstract_inverted_index.Remora | 117, 129 |
| abstract_inverted_index.become | 21 |
| abstract_inverted_index.carbon | 16 |
| abstract_inverted_index.energy | 59 |
| abstract_inverted_index.gained | 4 |
| abstract_inverted_index.layer, | 80, 82 |
| abstract_inverted_index.namely | 102 |
| abstract_inverted_index.people | 13 |
| abstract_inverted_index.power, | 165, 180 |
| abstract_inverted_index.price, | 166 |
| abstract_inverted_index.tasks, | 37 |
| abstract_inverted_index.(AROA). | 120 |
| abstract_inverted_index.Fog-IoV | 63, 66 |
| abstract_inverted_index.adapted | 87 |
| abstract_inverted_index.amongst | 12 |
| abstract_inverted_index.central | 23 |
| abstract_inverted_index.demands | 144 |
| abstract_inverted_index.devises | 55 |
| abstract_inverted_index.fitness | 158 |
| abstract_inverted_index.highest | 192 |
| abstract_inverted_index.immense | 5 |
| abstract_inverted_index.issues, | 45 |
| abstract_inverted_index.layers, | 76 |
| abstract_inverted_index.network | 67, 105 |
| abstract_inverted_index.offered | 187 |
| abstract_inverted_index.optimal | 108 |
| abstract_inverted_index.perform | 155 |
| abstract_inverted_index.pricing | 199 |
| abstract_inverted_index.several | 33 |
| abstract_inverted_index.trading | 110 |
| abstract_inverted_index.various | 44 |
| abstract_inverted_index.16.949%. | 201 |
| abstract_inverted_index.Electric | 0 |
| abstract_inverted_index.Internet | 27 |
| abstract_inverted_index.Vehicles | 1 |
| abstract_inverted_index.Whenever | 32 |
| abstract_inverted_index.bidding. | 156 |
| abstract_inverted_index.enhanced | 72, 188 |
| abstract_inverted_index.fitness, | 179 |
| abstract_inverted_index.function | 159 |
| abstract_inverted_index.learning | 100 |
| abstract_inverted_index.modelled | 162 |
| abstract_inverted_index.network. | 64 |
| abstract_inverted_index.obtained | 124 |
| abstract_inverted_index.pricing. | 182 |
| abstract_inverted_index.proposed | 115, 184 |
| abstract_inverted_index.security | 48 |
| abstract_inverted_index.smallest | 198 |
| abstract_inverted_index.trading. | 90 |
| abstract_inverted_index.vehicles | 19, 29, 34 |
| abstract_inverted_index.Algorithm | 119, 131 |
| abstract_inverted_index.awareness | 11 |
| abstract_inverted_index.classical | 40 |
| abstract_inverted_index.distance. | 168 |
| abstract_inverted_index.emission. | 17 |
| abstract_inverted_index.performed | 95 |
| abstract_inverted_index.predicted | 164 |
| abstract_inverted_index.regarding | 14 |
| abstract_inverted_index.technique | 57, 173 |
| abstract_inverted_index.AROA-based | 185 |
| abstract_inverted_index.algorithm. | 136 |
| abstract_inverted_index.distribute | 35 |
| abstract_inverted_index.harvesting | 60 |
| abstract_inverted_index.increasing | 10 |
| abstract_inverted_index.popularity | 6 |
| abstract_inverted_index.prediction | 93 |
| abstract_inverted_index.represents | 139 |
| abstract_inverted_index.simulation | 68 |
| abstract_inverted_index.technique, | 101 |
| abstract_inverted_index.centralized | 41 |
| abstract_inverted_index.electricity | 89, 109, 142, 145 |
| abstract_inverted_index.performance | 189 |
| abstract_inverted_index.processing. | 73 |
| abstract_inverted_index.Optimization | 118, 130 |
| abstract_inverted_index.amalgamation | 127 |
| abstract_inverted_index.optimization | 135 |
| abstract_inverted_index.reinforcement | 99 |
| abstract_inverted_index.communication. | 52 |
| abstract_inverted_index.experimentation | 170 |
| abstract_inverted_index.infrastructure. | 31 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5086659102 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I55124831 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.78670682 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |