Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study
Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this...
Gespeichert in:
| Veröffentlicht in: | Electronics (Basel) 2023-10, Vol.12 (20), p.4304 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 20 |
| container_start_page | 4304 |
| container_title | Electronics (Basel) |
| container_volume | 12 |
| creator | Yuan, Zhenbo Ge, Yongqi Wei, Jiayuan Yuan, Shuhua Liu, Rui Mo, Xian |
| description | Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field. |
| doi_str_mv | 10.3390/electronics12204304 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2882561949</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A772062551</galeid><sourcerecordid>A772062551</sourcerecordid><originalsourceid>FETCH-LOGICAL-c361t-25b41ab64d09fb05a1f2a7ab4224385ce9eb9db45d4518061c6d91bc1da127e33</originalsourceid><addsrcrecordid>eNptUE1LAzEQDaJgqf0FXgKet-Zzd-OtlGqFisIqHpdski0pbbImqbD_3pR68ODMYYbHe_OGB8AtRnNKBbo3e6NS8M6qiAlBjCJ2ASYEVaIQRJDLP_s1mMW4Q7kEpjVFE9CsnAnbEb4Fo61K1jvY-wDPaLGW4dvEZN0WftqQjWKEjXHRhwe4gM0YkznIZBV8kcNwYjXpqMcbcNXLfTSz3zkFH4-r9-W62Lw-PS8Xm0LREqeC8I5h2ZVMI9F3iEvcE1nJjhHCaM2VEaYTumNcM45rVGJVaoE7hbXEpDKUTsHd-e4Q_Ncx_9nu_DG4bNmSuia8xIKJzJqfWVu5N611vU9BqtzaHKzyzvQ244uqIqgknOMsoGeBCj7GYPp2CPYgw9hi1J4Sb_9JnP4A2292xQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2882561949</pqid></control><display><type>article</type><title>Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Free E-Journal (出版社公開部分のみ）</source><creator>Yuan, Zhenbo ; Ge, Yongqi ; Wei, Jiayuan ; Yuan, Shuhua ; Liu, Rui ; Mo, Xian</creator><creatorcontrib>Yuan, Zhenbo ; Ge, Yongqi ; Wei, Jiayuan ; Yuan, Shuhua ; Liu, Rui ; Mo, Xian</creatorcontrib><description>Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12204304</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Classification ; Classification schemes ; Data analysis ; Data collection ; Data transmission ; Energy consumption ; Energy harvesting ; Energy management ; Energy use ; Literature reviews ; Mapping ; Methods ; Prediction theory ; Researchers ; Sensors ; Simulation ; Software ; Systematic review ; Trends ; Wireless sensor networks</subject><ispartof>Electronics (Basel), 2023-10, Vol.12 (20), p.4304</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-25b41ab64d09fb05a1f2a7ab4224385ce9eb9db45d4518061c6d91bc1da127e33</citedby><cites>FETCH-LOGICAL-c361t-25b41ab64d09fb05a1f2a7ab4224385ce9eb9db45d4518061c6d91bc1da127e33</cites><orcidid>0000-0002-1249-9190</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Yuan, Zhenbo</creatorcontrib><creatorcontrib>Ge, Yongqi</creatorcontrib><creatorcontrib>Wei, Jiayuan</creatorcontrib><creatorcontrib>Yuan, Shuhua</creatorcontrib><creatorcontrib>Liu, Rui</creatorcontrib><creatorcontrib>Mo, Xian</creatorcontrib><title>Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study</title><title>Electronics (Basel)</title><description>Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Classification schemes</subject><subject>Data analysis</subject><subject>Data collection</subject><subject>Data transmission</subject><subject>Energy consumption</subject><subject>Energy harvesting</subject><subject>Energy management</subject><subject>Energy use</subject><subject>Literature reviews</subject><subject>Mapping</subject><subject>Methods</subject><subject>Prediction theory</subject><subject>Researchers</subject><subject>Sensors</subject><subject>Simulation</subject><subject>Software</subject><subject>Systematic review</subject><subject>Trends</subject><subject>Wireless sensor networks</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUE1LAzEQDaJgqf0FXgKet-Zzd-OtlGqFisIqHpdski0pbbImqbD_3pR68ODMYYbHe_OGB8AtRnNKBbo3e6NS8M6qiAlBjCJ2ASYEVaIQRJDLP_s1mMW4Q7kEpjVFE9CsnAnbEb4Fo61K1jvY-wDPaLGW4dvEZN0WftqQjWKEjXHRhwe4gM0YkznIZBV8kcNwYjXpqMcbcNXLfTSz3zkFH4-r9-W62Lw-PS8Xm0LREqeC8I5h2ZVMI9F3iEvcE1nJjhHCaM2VEaYTumNcM45rVGJVaoE7hbXEpDKUTsHd-e4Q_Ncx_9nu_DG4bNmSuia8xIKJzJqfWVu5N611vU9BqtzaHKzyzvQ244uqIqgknOMsoGeBCj7GYPp2CPYgw9hi1J4Sb_9JnP4A2292xQ</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Yuan, Zhenbo</creator><creator>Ge, Yongqi</creator><creator>Wei, Jiayuan</creator><creator>Yuan, Shuhua</creator><creator>Liu, Rui</creator><creator>Mo, Xian</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-1249-9190</orcidid></search><sort><creationdate>20231001</creationdate><title>Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study</title><author>Yuan, Zhenbo ; Ge, Yongqi ; Wei, Jiayuan ; Yuan, Shuhua ; Liu, Rui ; Mo, Xian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-25b41ab64d09fb05a1f2a7ab4224385ce9eb9db45d4518061c6d91bc1da127e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Classification</topic><topic>Classification schemes</topic><topic>Data analysis</topic><topic>Data collection</topic><topic>Data transmission</topic><topic>Energy consumption</topic><topic>Energy harvesting</topic><topic>Energy management</topic><topic>Energy use</topic><topic>Literature reviews</topic><topic>Mapping</topic><topic>Methods</topic><topic>Prediction theory</topic><topic>Researchers</topic><topic>Sensors</topic><topic>Simulation</topic><topic>Software</topic><topic>Systematic review</topic><topic>Trends</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Zhenbo</creatorcontrib><creatorcontrib>Ge, Yongqi</creatorcontrib><creatorcontrib>Wei, Jiayuan</creatorcontrib><creatorcontrib>Yuan, Shuhua</creatorcontrib><creatorcontrib>Liu, Rui</creatorcontrib><creatorcontrib>Mo, Xian</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Zhenbo</au><au>Ge, Yongqi</au><au>Wei, Jiayuan</au><au>Yuan, Shuhua</au><au>Liu, Rui</au><au>Mo, Xian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-10-01</date><risdate>2023</risdate><volume>12</volume><issue>20</issue><spage>4304</spage><pages>4304-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12204304</doi><orcidid>https://orcid.org/0000-0002-1249-9190</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2079-9292 |
| ispartof | Electronics (Basel), 2023-10, Vol.12 (20), p.4304 |
| issn | 2079-9292 2079-9292 |
| language | eng |
| recordid | cdi_proquest_journals_2882561949 |
| source | MDPI - Multidisciplinary Digital Publishing Institute; Free E-Journal (出版社公開部分のみ） |
| subjects | Algorithms Classification Classification schemes Data analysis Data collection Data transmission Energy consumption Energy harvesting Energy management Energy use Literature reviews Mapping Methods Prediction theory Researchers Sensors Simulation Software Systematic review Trends Wireless sensor networks |
| title | Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T07%3A04%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Energy%20Prediction%20for%20Energy-Harvesting%20Wireless%20Sensor:%20A%20Systematic%20Mapping%20Study&rft.jtitle=Electronics%20(Basel)&rft.au=Yuan,%20Zhenbo&rft.date=2023-10-01&rft.volume=12&rft.issue=20&rft.spage=4304&rft.pages=4304-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics12204304&rft_dat=%3Cgale_proqu%3EA772062551%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2882561949&rft_id=info:pmid/&rft_galeid=A772062551&rfr_iscdi=true |