https://en.wikipedia.org/wiki/Wireless_sensor_network?action=history&feed=atom&title=Talk:Main_PageWireless sensor network - Revision history2024-03-29T02:18:18ZRevision history for this page on the wikiMediaWiki 1.42.0-wmf.24https://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1215225085&oldid=prevHeyElliott: Category:CS1 maint: location missing publisher, added info to refs, ce2024-03-23T21:28:54Z<p><a href="/wiki/Category:CS1_maint:_location_missing_publisher" title="Category:CS1 maint: location missing publisher">Category:CS1 maint: location missing publisher</a>, added info to refs, ce</p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Wireless sensor networks are composed of low-energy, small-size, and low-range unattended sensor nodes. Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.<ref>{{cite book|first1=A.|last1=Xenakis|first2=F.|last2=Foukalas|first3=G.|last3=Stamoulis|title=Proceedings of the 19th Panhellenic Conference on Informatics |chapter=Minimum weighted clustering algorithm for wireless sensor networks |doi=10.1145/2801948.2801999|date=October 2015|pages=255–260|isbn=978-1-4503-3551-5|s2cid=9188571}}</ref><ref>{{cite book|first1=T. A. H.|last1=Hassan|first2=G.|last2=Selim|first3=R.|last3=Sadek|title=2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) |chapter=A novel energy efficient vice Cluster Head routing protocol in Wireless Sensor Networks |location=Cairo|year=2015|pages=313–320|doi=10.1109/IntelCIS.2015.7397240|isbn=978-1-5090-1949-6|s2cid=10688614}}</ref> However, this duty cycling may result in high network latency, routing overhead, and neighbor discovery delays due to asynchronous sleep and wake-up scheduling. These limitations call for a countermeasure for duty-cycled wireless sensor networks which should minimize routing information, routing traffic load, and energy consumption. Researchers from Sungkyunkwan University have proposed a lightweight non-increasing delivery-latency interval routing referred as LNDIR. This scheme can discover minimum latency routes at each non-increasing delivery-latency interval instead of each time slot.{{clarify|date=September 2020}} Simulation experiments demonstrated the validity of this novel approach in minimizing routing information stored at each sensor. Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink. Performance improvements of up to 12-fold and 11-fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes.<ref name="LNDIR">{{cite journal |last1=K Shahzad |first1=Muhammad |last2=Nguyen |first2=Dang Tu |last3=Zalyubovskiy |first3=Vyacheslav |last4=Choo |first4=Hyunseung |date=2018 |title=LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks |journal= International Journal of Distributed Sensor Networks |volume=14 |issue=4 |page=1550147718767605 |doi=10.1177/1550147718767605|doi-access=free }} [[File:CC-BY icon.svg|50px]] Material was copied from this source, which is available under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Wireless sensor networks are composed of low-energy, small-size, and low-range unattended sensor nodes. Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.<ref>{{cite book|first1=A.|last1=Xenakis|first2=F.|last2=Foukalas|first3=G.|last3=Stamoulis|title=Proceedings of the 19th Panhellenic Conference on Informatics |chapter=Minimum weighted clustering algorithm for wireless sensor networks |doi=10.1145/2801948.2801999|date=October 2015|pages=255–260|isbn=978-1-4503-3551-5|s2cid=9188571}}</ref><ref>{{cite book|first1=T. A. H.|last1=Hassan|first2=G.|last2=Selim|first3=R.|last3=Sadek<ins style="font-weight: bold; text-decoration: none;"> |publisher=IEEE </ins>|title=2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) |chapter=A novel energy efficient vice Cluster Head routing protocol in Wireless Sensor Networks |location=Cairo|year=2015|pages=313–320|doi=10.1109/IntelCIS.2015.7397240|isbn=978-1-5090-1949-6|s2cid=10688614}}</ref> However, this duty cycling may result in high network latency, routing overhead, and neighbor discovery delays due to asynchronous sleep and wake-up scheduling. These limitations call for a countermeasure for duty-cycled wireless sensor networks which should minimize routing information, routing traffic load, and energy consumption. Researchers from Sungkyunkwan University have proposed a lightweight non-increasing delivery-latency interval routing referred as LNDIR. This scheme can discover minimum latency routes at each non-increasing delivery-latency interval instead of each time slot.{{clarify|date=September 2020}} Simulation experiments demonstrated the validity of this novel approach in minimizing routing information stored at each sensor. Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink. Performance improvements of up to 12-fold and 11-fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes.<ref name="LNDIR">{{cite journal |last1=K Shahzad |first1=Muhammad |last2=Nguyen |first2=Dang Tu |last3=Zalyubovskiy |first3=Vyacheslav |last4=Choo |first4=Hyunseung |date=2018 |title=LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks |journal= International Journal of Distributed Sensor Networks |volume=14 |issue=4 |page=1550147718767605 |doi=10.1177/1550147718767605|doi-access=free }} [[File:CC-BY icon.svg|50px]] Material was copied from this source, which is available under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].</ref></div></td>
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</table>HeyElliotthttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1215032498&oldid=prevSkoocda: /* Wireless */ Added link to LoRa page2024-03-22T18:48:45Z<p><span dir="auto"><span class="autocomment">Wireless: </span> Added link to LoRa page</span></p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>There are several wireless standards and solutions for sensor node connectivity. [[Thread (network protocol)|Thread]] and [[Zigbee]] can connect sensors operating at 2.4&nbsp;GHz with a data rate of 250kbit/s. Many use a lower frequency to increase radio range (typically 1&nbsp;km), for example [[Z-wave]] operates at 915&nbsp;MHz and in the EU 868&nbsp;MHz has been widely used but these have a lower data rate (typically 50 kbit/s). The IEEE 802.15.4 working group provides a standard for low power device connectivity and commonly sensors and smart meters use one of these standards for connectivity. With the emergence of [[Internet of Things]], many other proposals have been made to provide sensor connectivity. LoRa<ref>{{cite web|url=https://www.lora-alliance.org/technology|title=LoRa Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109080940/https://www.lora-alliance.org/technology|archive-date=2017-11-09}}</ref> is a form of [[LPWAN]] which provides long range low power wireless connectivity for devices, which has been used in smart meters and other long range sensor applications. Wi-SUN<ref>{{cite web|url=https://www.wi-sun.org/|title=Wi-Sun Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109134725/https://www.wi-sun.org/|archive-date=2017-11-09|date=2018-08-15}}</ref> connects devices at home. [[NarrowBand IOT]]<ref>{{cite web|url=https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|title=NB-IOT vs. LoRa vs. Sigfox, LINKLabs, Jan 2017.|url-status=live|archive-url=https://web.archive.org/web/20171110171333/https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|archive-date=2017-11-10}}</ref> and LTE-M<ref>{{cite web|url=https://www.link-labs.com/blog/what-is-lte-m|title=What is LTE-M?|url-status=live|archive-url=https://web.archive.org/web/20171109081312/https://www.link-labs.com/blog/what-is-lte-m|archive-date=2017-11-09}}</ref> can connect up to millions of sensors and devices using cellular technology.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>There are several wireless standards and solutions for sensor node connectivity. [[Thread (network protocol)|Thread]] and [[Zigbee]] can connect sensors operating at 2.4&nbsp;GHz with a data rate of 250kbit/s. Many use a lower frequency to increase radio range (typically 1&nbsp;km), for example [[Z-wave]] operates at 915&nbsp;MHz and in the EU 868&nbsp;MHz has been widely used but these have a lower data rate (typically 50 kbit/s). The IEEE 802.15.4 working group provides a standard for low power device connectivity and commonly sensors and smart meters use one of these standards for connectivity. With the emergence of [[Internet of Things]], many other proposals have been made to provide sensor connectivity. <ins style="font-weight: bold; text-decoration: none;">[[</ins>LoRa<ins style="font-weight: bold; text-decoration: none;">]]</ins><ref>{{cite web|url=https://www.lora-alliance.org/technology|title=LoRa Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109080940/https://www.lora-alliance.org/technology|archive-date=2017-11-09}}</ref> is a form of [[LPWAN]] which provides long range low power wireless connectivity for devices, which has been used in smart meters and other long range sensor applications. Wi-SUN<ref>{{cite web|url=https://www.wi-sun.org/|title=Wi-Sun Alliance|url-status=live|archive-url=https://web.archive.org/web/20171109134725/https://www.wi-sun.org/|archive-date=2017-11-09|date=2018-08-15}}</ref> connects devices at home. [[NarrowBand IOT]]<ref>{{cite web|url=https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|title=NB-IOT vs. LoRa vs. Sigfox, LINKLabs, Jan 2017.|url-status=live|archive-url=https://web.archive.org/web/20171110171333/https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox|archive-date=2017-11-10}}</ref> and LTE-M<ref>{{cite web|url=https://www.link-labs.com/blog/what-is-lte-m|title=What is LTE-M?|url-status=live|archive-url=https://web.archive.org/web/20171109081312/https://www.link-labs.com/blog/what-is-lte-m|archive-date=2017-11-09}}</ref> can connect up to millions of sensors and devices using cellular technology.</div></td>
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</table>Skoocdahttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1201596877&oldid=prevMeno25: /* See also */2024-02-01T00:28:13Z<p><span dir="auto"><span class="autocomment">See also</span></span></p>
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</table>Meno25https://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1193477771&oldid=prevOAbot: Open access bot: hdl updated in citation with #oabot.2024-01-04T01:06:26Z<p><a href="/wiki/Wikipedia:OABOT" title="Wikipedia:OABOT">Open access bot</a>: hdl updated in citation with #oabot.</p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>To reduce communication costs some algorithms remove or reduce nodes' redundant sensor information and avoid forwarding data that is of no use. This technique has been used, for instance, for distributed anomaly detection<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2015-12-01|title=Ensembles of incremental learners to detect anomalies in ad hoc sensor networks|journal=Ad Hoc Networks|series=Special Issue on Big Data Inspired Data Sensing, Processing and Networking Technologies|volume=35|pages=14–36|doi=10.1016/j.adhoc.2015.07.013|issn=1570-8705|hdl=11572/196409|hdl-access=free}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE International Conference on Systems, Man, and Cybernetics |chapter=Anomaly Detection in Sensor Systems Using Lightweight Machine Learning |date=October 2013|pages=7–13|doi=10.1109/SMC.2013.9|isbn=978-1-4799-0652-9|s2cid=6434158}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE 13th International Conference on Data Mining Workshops |chapter=Online Extreme Learning on Fixed-Point Sensor Networks |date=December 2013|pages=319–326|doi=10.1109/ICDMW.2013.74|isbn=978-1-4799-3142-2|s2cid=6460187}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Wörtche|first3=H. J.|last4=Liotta|first4=A.|title=2014 IEEE International Conference on Data Mining Workshop |chapter=Online Fusion of Incremental Learning for Wireless Sensor Networks |date=December 2014|pages=525–532|doi=10.1109/ICDMW.2014.79|isbn=978-1-4799-4274-9|hdl=10545/622629|s2cid=14029568|hdl-access=free}}</ref> or distributed optimization.<ref>{{Cite journal|last=Iacca|first=G.|title=Distributed optimization in wireless sensor networks: an island-model framework|journal=Soft Computing|language=en|volume=17|issue=12|pages=2257–2277|doi=10.1007/s00500-013-1091-x|issn=1433-7479|arxiv=1810.02679|year=2018|bibcode=2018arXiv181002679I|s2cid=33273544}}</ref> As nodes can inspect the data they forward, they can measure averages or directionality for example of readings from other nodes. For example, in sensing and monitoring applications, it is generally the case that neighboring sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires techniques for in-network data aggregation and mining. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes.<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2017-01-01|title=Spatial anomaly detection in sensor networks using neighborhood information|journal=Information Fusion|volume=33|pages=41–56|doi=10.1016/j.inffus.2016.04.007|issn=1566-2535|doi-access=free}}</ref><ref name="refESPDA">{{Cite book|last=Cam|first=H|author2=Ozdemir, S Nair, P Muthuavinashiappan, D|title=Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)|chapter=ESPDA: Energy-efficient and Secure Pattern-based Data Aggregation for wireless sensor networks|date=October 2003|volume=2|pages=732–736|doi=10.1109/icsens.2003.1279038|isbn=978-0-7803-8133-9|citeseerx=10.1.1.1.6961|s2cid=15686293}}</ref> Recently, it has been found that network gateways also play an important role in improving energy efficiency of sensor nodes by scheduling more resources for the nodes with more critical energy efficiency need and advanced energy efficient scheduling algorithms need to be implemented at network gateways for the improvement of the overall network energy efficiency.<ref name=Zander/><ref>{{cite journal|last=Rowayda|first=A. Sadek|title= Hybrid energy aware clustered protocol for IoT heterogeneous network |journal= Future Computing and Informatics Journal|volume=3|issue=2|pages=166–177|date=May 2018 |doi=10.1016/j.fcij.2018.02.003|doi-access=free}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>To reduce communication costs some algorithms remove or reduce nodes' redundant sensor information and avoid forwarding data that is of no use. This technique has been used, for instance, for distributed anomaly detection<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2015-12-01|title=Ensembles of incremental learners to detect anomalies in ad hoc sensor networks|journal=Ad Hoc Networks|series=Special Issue on Big Data Inspired Data Sensing, Processing and Networking Technologies|volume=35|pages=14–36|doi=10.1016/j.adhoc.2015.07.013|issn=1570-8705|hdl=11572/196409|hdl-access=free}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE International Conference on Systems, Man, and Cybernetics |chapter=Anomaly Detection in Sensor Systems Using Lightweight Machine Learning |date=October 2013|pages=7–13|doi=10.1109/SMC.2013.9|isbn=978-1-4799-0652-9|s2cid=6434158}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE 13th International Conference on Data Mining Workshops |chapter=Online Extreme Learning on Fixed-Point Sensor Networks |date=December 2013|pages=319–326|doi=10.1109/ICDMW.2013.74|isbn=978-1-4799-3142-2|s2cid=6460187}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Wörtche|first3=H. J.|last4=Liotta|first4=A.|title=2014 IEEE International Conference on Data Mining Workshop |chapter=Online Fusion of Incremental Learning for Wireless Sensor Networks |date=December 2014|pages=525–532|doi=10.1109/ICDMW.2014.79|isbn=978-1-4799-4274-9|hdl=10545/622629|s2cid=14029568|hdl-access=free}}</ref> or distributed optimization.<ref>{{Cite journal|last=Iacca|first=G.|title=Distributed optimization in wireless sensor networks: an island-model framework|journal=Soft Computing|language=en|volume=17|issue=12|pages=2257–2277|doi=10.1007/s00500-013-1091-x|issn=1433-7479|arxiv=1810.02679|year=2018|bibcode=2018arXiv181002679I|s2cid=33273544}}</ref> As nodes can inspect the data they forward, they can measure averages or directionality for example of readings from other nodes. For example, in sensing and monitoring applications, it is generally the case that neighboring sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires techniques for in-network data aggregation and mining. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes.<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2017-01-01|title=Spatial anomaly detection in sensor networks using neighborhood information|journal=Information Fusion|volume=33|pages=41–56|doi=10.1016/j.inffus.2016.04.007|issn=1566-2535|doi<ins style="font-weight: bold; text-decoration: none;">-access=free|hdl=11572/196405|hdl</ins>-access=free}}</ref><ref name="refESPDA">{{Cite book|last=Cam|first=H|author2=Ozdemir, S Nair, P Muthuavinashiappan, D|title=Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)|chapter=ESPDA: Energy-efficient and Secure Pattern-based Data Aggregation for wireless sensor networks|date=October 2003|volume=2|pages=732–736|doi=10.1109/icsens.2003.1279038|isbn=978-0-7803-8133-9|citeseerx=10.1.1.1.6961|s2cid=15686293}}</ref> Recently, it has been found that network gateways also play an important role in improving energy efficiency of sensor nodes by scheduling more resources for the nodes with more critical energy efficiency need and advanced energy efficient scheduling algorithms need to be implemented at network gateways for the improvement of the overall network energy efficiency.<ref name=Zander/><ref>{{cite journal|last=Rowayda|first=A. Sadek|title= Hybrid energy aware clustered protocol for IoT heterogeneous network |journal= Future Computing and Informatics Journal|volume=3|issue=2|pages=166–177|date=May 2018 |doi=10.1016/j.fcij.2018.02.003|doi-access=free}}</ref></div></td>
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</table>OAbothttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1186679954&oldid=prevMe, Myself, and I are Here: fixed dashes using a script2023-11-24T20:32:14Z<p>fixed <a href="/wiki/MOS:DASH" class="mw-redirect" title="MOS:DASH">dashes</a> using a <a href="/wiki/User:GregU/dashes.js" title="User:GregU/dashes.js">script</a></p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Power consumption constraints for nodes using batteries or [[energy harvesting]]. Examples of suppliers are ReVibe Energy<ref>{{cite web|url=https://revibeenergy.com|title=ReVibe Energy <del style="font-weight: bold; text-decoration: none;">-</del> Powering The Industrial IoT|website=revibeenergy.com|access-date=3 May 2018|url-status=live|archive-url=https://web.archive.org/web/20170922150745/http://revibeenergy.com/|archive-date=22 September 2017}}</ref> and Perpetuum<ref>{{cite web|url=https://perpetuum.com/|title=THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS|website=THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS|access-date=3 May 2018|url-status=live|archive-url=https://web.archive.org/web/20180413082857/https://perpetuum.com/|archive-date=13 April 2018}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Power consumption constraints for nodes using batteries or [[energy harvesting]]. Examples of suppliers are ReVibe Energy<ref>{{cite web|url=https://revibeenergy.com|title=ReVibe Energy <ins style="font-weight: bold; text-decoration: none;">–</ins> Powering The Industrial IoT|website=revibeenergy.com|access-date=3 May 2018|url-status=live|archive-url=https://web.archive.org/web/20170922150745/http://revibeenergy.com/|archive-date=22 September 2017}}</ref> and Perpetuum<ref>{{cite web|url=https://perpetuum.com/|title=THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS|website=THE WORLD LEADER IN VIBRATION HARVESTER POWERED WIRELESS SENSING SYSTEMS|access-date=3 May 2018|url-status=live|archive-url=https://web.archive.org/web/20180413082857/https://perpetuum.com/|archive-date=13 April 2018}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>PreonVM<ref>{{cite web|url=https://www.virtenio.com/en/preonvm-virtual-maschine.html|title=PreonVM <del style="font-weight: bold; text-decoration: none;">-</del> Virtual maschine for wireless sensor devices|archive-url=https://web.archive.org/web/20171111094628/https://www.virtenio.com/en/preonvm-virtual-maschine.html|archive-date=2017-11-11 }}</ref> is an OS for wireless sensor networks, which provides [[6LoWPAN]] based on [[Contiki]] and support for the [[Java (programming language)|Java]] programming language.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>PreonVM<ref>{{cite web|url=https://www.virtenio.com/en/preonvm-virtual-maschine.html|title=PreonVM <ins style="font-weight: bold; text-decoration: none;">–</ins> Virtual maschine for wireless sensor devices|archive-url=https://web.archive.org/web/20171111094628/https://www.virtenio.com/en/preonvm-virtual-maschine.html|archive-date=2017-11-11 }}</ref> is an OS for wireless sensor networks, which provides [[6LoWPAN]] based on [[Contiki]] and support for the [[Java (programming language)|Java]] programming language.</div></td>
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</table>Me, Myself, and I are Herehttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1185636998&oldid=prevYkhwong: Add {{Redirect|WSN}}2023-11-18T01:33:37Z<p>Add {{Redirect|WSN}}</p>
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</table>Ykhwonghttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1185295573&oldid=prevCitation bot: Add: bibcode. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 596/11092023-11-15T20:57:59Z<p>Add: bibcode. | <a href="/wiki/Wikipedia:UCB" class="mw-redirect" title="Wikipedia:UCB">Use this bot</a>. <a href="/wiki/Wikipedia:DBUG" class="mw-redirect" title="Wikipedia:DBUG">Report bugs</a>. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 596/1109</p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>There are studies that show that using sensors for incident monitoring improve in a great way the response of firefighters and police to an unexpected situation.<ref>{{Cite journal|last=Aguilar|first=Mónica|title=INRISCO: INcident monitoRing in Smart COmmunities|journal=IEEE Access|year=2020|volume=8|pages=72435–72460|url=https://ieeexplore.ieee.org/document/9064504|doi=10.1109/ACCESS.2020.2987483|s2cid=218468946|hdl=2117/328871|hdl-access=free}}</ref> For an early detection of incidents we can use acoustic sensors to detect a spike in the noise of the city because of a possible accident,<ref>{{Cite journal|last=Pastor|first=Adolfo|title=Psychoacoustic Annoyance Implementation With Wireless Acoustic Sensor Networks for Monitoring in Smart Cities|journal=IEEE Internet of Things Journal|year=2020|volume=7|pages=128–136|url=https://ieeexplore.ieee.org/document/8865089|doi=10.1109/JIOT.2019.2946971|s2cid=208111073}}</ref> or use termic sensors to detect a possible fire.<ref>{{Cite journal|last=Lloret|first=Jaime|title=A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification|journal= Sensors|year=2009|volume=9|issue=11|pages=8722–8747|doi=10.3390/s91108722|pmid=22291533|bibcode=2009Senso...9.8722L|s2cid=13104461|pmc=3260610|doi-access=free}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>There are studies that show that using sensors for incident monitoring improve in a great way the response of firefighters and police to an unexpected situation.<ref>{{Cite journal|last=Aguilar|first=Mónica|title=INRISCO: INcident monitoRing in Smart COmmunities|journal=IEEE Access|year=2020|volume=8|pages=72435–72460|url=https://ieeexplore.ieee.org/document/9064504|doi=10.1109/ACCESS.2020.2987483<ins style="font-weight: bold; text-decoration: none;">|bibcode=2020IEEEA...872435I </ins>|s2cid=218468946|hdl=2117/328871|hdl-access=free}}</ref> For an early detection of incidents we can use acoustic sensors to detect a spike in the noise of the city because of a possible accident,<ref>{{Cite journal|last=Pastor|first=Adolfo|title=Psychoacoustic Annoyance Implementation With Wireless Acoustic Sensor Networks for Monitoring in Smart Cities|journal=IEEE Internet of Things Journal|year=2020|volume=7|pages=128–136|url=https://ieeexplore.ieee.org/document/8865089|doi=10.1109/JIOT.2019.2946971|s2cid=208111073}}</ref> or use termic sensors to detect a possible fire.<ref>{{Cite journal|last=Lloret|first=Jaime|title=A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification|journal= Sensors|year=2009|volume=9|issue=11|pages=8722–8747|doi=10.3390/s91108722|pmid=22291533|bibcode=2009Senso...9.8722L|s2cid=13104461|pmc=3260610|doi-access=free}}</ref></div></td>
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</table>Citation bothttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1177990628&oldid=prevAnomieBOT: Substing templates: {{Format ISBN}}. See User:AnomieBOT/docs/TemplateSubster for info.2023-09-30T23:18:34Z<p><a href="/wiki/User:AnomieBOT/docs/TemplateSubster" title="User:AnomieBOT/docs/TemplateSubster">Substing templates</a>: {{Format ISBN}}. See <a href="/wiki/User:AnomieBOT/docs/TemplateSubster" title="User:AnomieBOT/docs/TemplateSubster">User:AnomieBOT/docs/TemplateSubster</a> for info.</p>
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<a href="//en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1177987105&oldid=1172204965">Show changes</a>Trappist the monkhttps://en.wikipedia.org/w/index.php?title=Wireless_sensor_network&diff=1172204965&oldid=prevCitation bot: Alter: title, template type. Add: chapter. Removed parameters. | Use this bot. Report bugs. | #UCB_CommandLine2023-08-25T16:39:33Z<p>Alter: title, template type. Add: chapter. Removed parameters. | <a href="/wiki/Wikipedia:UCB" class="mw-redirect" title="Wikipedia:UCB">Use this bot</a>. <a href="/wiki/Wikipedia:DBUG" class="mw-redirect" title="Wikipedia:DBUG">Report bugs</a>. | #UCB_CommandLine</p>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Air quality monitoring====</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Experiments have shown that personal exposure to [[air pollution]] in cities can vary a lot.<ref>{{cite journal |last1=Apte |first1=J.S. |last2=Messier |first2=K.P. |last3=Gani |first3=S. |last4=Brauer |first4=M. |last5=Kirchstetter |first5=T.W. |last6=Lunden |first6=M.M. |last7=Marshall |first7=J.D. |last8=Portier |first8=C.J. |last9=Vermeulen |first9=R.C.H. |last10=Hamburg |first10=S.P. |date=2017 |title=High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data |journal=Environmental Science and Technology |volume=51 |issue=12 |pages=6999–7008 |doi=10.1021/acs.est.7b00891|pmid=28578585 |bibcode=2017EnST...51.6999A |doi-access=free }}</ref> Therefore, it is of interest to have higher temporal and spatial resolution of [[pollutants]] and [[particulates]]. For research purposes, wireless sensor networks have been deployed to monitor the concentration of [[air pollution|dangerous gases for citizens]] (e.g., in [[London]]).<ref>{{cite web |url=https://www.breathelondon.org/ |title=Breathe London |author=<!--Not stated--> |website=Breathe London |access-date=27 April 2021}}</ref> However, sensors for gases and particulate matter suffer from high unit-to-unit variability, cross-sensitivities, and (concept) drift.<ref>{{cite journal |last1=Feinberg |first1=S. |last2=Williams |first2=R. |last3=Hagler |first3=G.S.W. |last4=Rickard |first4=J. |last5=Garver |first5=D. |last6=Harshfield |first6=G. |last7=Stauffer |first7=P. |last8=Mattson |first8=E. |last9=Judge |first9=R. |last10=Garvey |first10=S. |date=2018 |title=Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado |journal=Atmospheric Measurement Techniques |volume=11 |issue=8 |pages=4605–4615 |doi=10.5194/amt-11-4605-2018|pmid=31595175 |pmc=6781239 |bibcode=2018AMT....11.4605F |doi-access=free }}</ref> Moreover, the quality of data is currently insufficient for trustworthy decision-making, as field calibration leads to unreliable measurement results, and frequent recalibration might be required. A possible solution could be blind calibration or the usage of mobile references.<ref>{{cite <del style="font-weight: bold; text-decoration: none;">journal</del> |last1=Balzano |first1=L. |last2=Nowak |first2=R. |<del style="font-weight: bold; text-decoration: none;">date</del>=<del style="font-weight: bold; text-decoration: none;">2008</del> |<del style="font-weight: bold; text-decoration: none;">title</del>=Blind Calibration of Networks of Sensors: Theory and Algorithms |<del style="font-weight: bold; text-decoration: none;">journal</del>=<del style="font-weight: bold; text-decoration: none;">Networked Sensing Information and Control</del> |pages=9–37 | doi=10.1007/978-0-387-68845-9_1|isbn=978-0-387-68843-5 }}</ref><ref>{{cite book |last1=Sauce |first1=O. |last2=Hasenfratz |first2=D. |last3=Thiele|first3=L. |title=Proceedings of the 14th International Conference on Information Processing in Sensor Networks |chapter=Reducing multi-hop calibration errors in large-scale mobile sensor networks |date=2015 |pages=274–285 | doi=10.1145/2737095.2737113|isbn=9781450334754 |s2cid=15171166 }}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Experiments have shown that personal exposure to [[air pollution]] in cities can vary a lot.<ref>{{cite journal |last1=Apte |first1=J.S. |last2=Messier |first2=K.P. |last3=Gani |first3=S. |last4=Brauer |first4=M. |last5=Kirchstetter |first5=T.W. |last6=Lunden |first6=M.M. |last7=Marshall |first7=J.D. |last8=Portier |first8=C.J. |last9=Vermeulen |first9=R.C.H. |last10=Hamburg |first10=S.P. |date=2017 |title=High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data |journal=Environmental Science and Technology |volume=51 |issue=12 |pages=6999–7008 |doi=10.1021/acs.est.7b00891|pmid=28578585 |bibcode=2017EnST...51.6999A |doi-access=free }}</ref> Therefore, it is of interest to have higher temporal and spatial resolution of [[pollutants]] and [[particulates]]. For research purposes, wireless sensor networks have been deployed to monitor the concentration of [[air pollution|dangerous gases for citizens]] (e.g., in [[London]]).<ref>{{cite web |url=https://www.breathelondon.org/ |title=Breathe London |author=<!--Not stated--> |website=Breathe London |access-date=27 April 2021}}</ref> However, sensors for gases and particulate matter suffer from high unit-to-unit variability, cross-sensitivities, and (concept) drift.<ref>{{cite journal |last1=Feinberg |first1=S. |last2=Williams |first2=R. |last3=Hagler |first3=G.S.W. |last4=Rickard |first4=J. |last5=Garver |first5=D. |last6=Harshfield |first6=G. |last7=Stauffer |first7=P. |last8=Mattson |first8=E. |last9=Judge |first9=R. |last10=Garvey |first10=S. |date=2018 |title=Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado |journal=Atmospheric Measurement Techniques |volume=11 |issue=8 |pages=4605–4615 |doi=10.5194/amt-11-4605-2018|pmid=31595175 |pmc=6781239 |bibcode=2018AMT....11.4605F |doi-access=free }}</ref> Moreover, the quality of data is currently insufficient for trustworthy decision-making, as field calibration leads to unreliable measurement results, and frequent recalibration might be required. A possible solution could be blind calibration or the usage of mobile references.<ref>{{cite <ins style="font-weight: bold; text-decoration: none;">book</ins> |last1=Balzano |first1=L. |last2=Nowak |first2=R. |<ins style="font-weight: bold; text-decoration: none;">title</ins>=<ins style="font-weight: bold; text-decoration: none;">Networked Sensing Information and Control</ins> |<ins style="font-weight: bold; text-decoration: none;">chapter</ins>=Blind Calibration of Networks of Sensors: Theory and Algorithms |<ins style="font-weight: bold; text-decoration: none;">date</ins>=<ins style="font-weight: bold; text-decoration: none;">2008</ins> |pages=9–37 | doi=10.1007/978-0-387-68845-9_1|isbn=978-0-387-68843-5 }}</ref><ref>{{cite book |last1=Sauce |first1=O. |last2=Hasenfratz |first2=D. |last3=Thiele|first3=L. |title=Proceedings of the 14th International Conference on Information Processing in Sensor Networks |chapter=Reducing multi-hop calibration errors in large-scale mobile sensor networks |date=2015 |pages=274–285 | doi=10.1145/2737095.2737113|isbn=9781450334754 |s2cid=15171166 }}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>This is a form of in-network processing where [[sensor node]]s are assumed to be unsecured with limited available energy, while the base station is assumed to be secure with unlimited available energy. Aggregation complicates the already existing security challenges for wireless sensor networks<ref name="refSAWN">{{cite journal|last=Hu|first=Lingxuan|author2=David Evans |title=Secure aggregation for wireless networks|journal=Workshop on Security and Assurance in Ad Hoc Networks|date=January 2003}}</ref> and requires new security techniques tailored specifically for these scenarios. Providing security to aggregate data in wireless sensor networks is known as ''secure data aggregation in WSN''.<ref name="refESPDA" /><ref name="refSAWN" /><ref name="refSIA">{{cite book|last=Przydatek|first=Bartosz|author2=Dawn Song |author3=Adrian Perrig |title=SIA: <del style="font-weight: bold; text-decoration: none;">secure</del> information aggregation in sensor networks|journal=SenSys|year=2003|pages=255–265|doi=10.1145/958491.958521|isbn=978-1581137071|s2cid=239370}}</ref> were the first few works discussing techniques for secure data aggregation in wireless sensor networks.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>This is a form of in-network processing where [[sensor node]]s are assumed to be unsecured with limited available energy, while the base station is assumed to be secure with unlimited available energy. Aggregation complicates the already existing security challenges for wireless sensor networks<ref name="refSAWN">{{cite journal|last=Hu|first=Lingxuan|author2=David Evans |title=Secure aggregation for wireless networks|journal=Workshop on Security and Assurance in Ad Hoc Networks|date=January 2003}}</ref> and requires new security techniques tailored specifically for these scenarios. Providing security to aggregate data in wireless sensor networks is known as ''secure data aggregation in WSN''.<ref name="refESPDA" /><ref name="refSAWN" /><ref name="refSIA">{{cite book|last=Przydatek|first=Bartosz|author2=Dawn Song |author3=Adrian Perrig |title<ins style="font-weight: bold; text-decoration: none;">=Proceedings of the 1st international conference on Embedded networked sensor systems |chapter</ins>=SIA: <ins style="font-weight: bold; text-decoration: none;">Secure</ins> information aggregation in sensor networks<ins style="font-weight: bold; text-decoration: none;"> </ins>|journal=SenSys|year=2003|pages=255–265|doi=10.1145/958491.958521|isbn=978-1581137071|s2cid=239370}}</ref> were the first few works discussing techniques for secure data aggregation in wireless sensor networks.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Two main security challenges in secure data aggregation are confidentiality and integrity of data. While [[encryption]] is traditionally used to provide end to end confidentiality in wireless sensor network, the aggregators in a secure data aggregation scenario need to decrypt the encrypted data to perform aggregation. This exposes the plaintext at the aggregators, making the data vulnerable to attacks from an adversary. Similarly an aggregator can inject false data into the aggregate and make the base station accept false data. Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges.<ref name=refSDA>{{cite book|last=Kumar|first=Vimal|author2=Sanjay K. Madria |title=2012 IEEE 13th International Conference on Mobile Data Management|chapter=Secure Hierarchical Data Aggregation in Wireless Sensor Networks: Performance Evaluation and Analysis|date=August 2012 |pages=196–201|doi=10.1109/MDM.2012.49|isbn=978-1-4673-1796-2|s2cid=2990744}}</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Two main security challenges in secure data aggregation are confidentiality and integrity of data. While [[encryption]] is traditionally used to provide end to end confidentiality in wireless sensor network, the aggregators in a secure data aggregation scenario need to decrypt the encrypted data to perform aggregation. This exposes the plaintext at the aggregators, making the data vulnerable to attacks from an adversary. Similarly an aggregator can inject false data into the aggregate and make the base station accept false data. Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges.<ref name=refSDA>{{cite book|last=Kumar|first=Vimal|author2=Sanjay K. Madria |title=2012 IEEE 13th International Conference on Mobile Data Management|chapter=Secure Hierarchical Data Aggregation in Wireless Sensor Networks: Performance Evaluation and Analysis|date=August 2012 |pages=196–201|doi=10.1109/MDM.2012.49|isbn=978-1-4673-1796-2|s2cid=2990744}}</ref></div></td>
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