{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T17:23:42Z","timestamp":1780680222982,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,5]],"date-time":"2017-11-05T00:00:00Z","timestamp":1509840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"India-Canada Centre for Innovative Multidisciplinary Partnership to Accelerate Community Transformation and Sustainability","award":["11R18083"],"award-info":[{"award-number":["11R18083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.<\/jats:p>","DOI":"10.3390\/s17112551","type":"journal-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T11:39:38Z","timestamp":1509968378000},"page":"2551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring"],"prefix":"10.3390","volume":"17","author":[{"given":"Tongxin","family":"Shu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clarence","family":"De Silva","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,5]]},"reference":[{"key":"ref_1","first-page":"18","article-title":"Remote water quality monitoring system based on WSN and GPRS","volume":"1","author":"Wang","year":"2010","journal-title":"Instrum. Tech. Sens."},{"key":"ref_2","first-page":"4","article-title":"Water Quality Monitoring Using Wireless Sensor Networks: Current Trends and Future Research Directions","volume":"13","author":"Tapparello","year":"2017","journal-title":"ACM Trans. Sens. Netw. (TOSN)"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, T., Xia, M., Chen, J., and de Silva, C. (2017). Automated Water Quality Survey and Evaluation Using an IoT Platform with Mobile Sensor Nodes. Sensors, 17.","DOI":"10.3390\/s17081735"},{"key":"ref_4","unstructured":"Shu, T. (2016). Power Management in a Sensor Network for Automated Water Quality Monitoring. [Master Dissertation, University of British Columbia]."},{"key":"ref_5","unstructured":"Lee, C., and Lee, J. (2017, January 11\u201313). Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. Proceedings of the 2017 International Conference on Information Networking (ICOIN), Da Nang, Vietnam."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1007\/s00542-017-3339-3","article-title":"Performance evaluation of tree based data aggregation for real time indoor environment monitoring using wireless sensor network","volume":"23","author":"Ray","year":"2017","journal-title":"Microsyst. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, X., Cao, J., Lai, S., Yang, C., Wu, H., and Xu, Y.L. (2011, January 10\u201315). Energy efficient clustering for WSN-based structural health monitoring. Proceedings of the 2011 IEEE INFOCOM, Shanghai, China.","DOI":"10.1109\/INFCOM.2011.5935109"},{"key":"ref_8","unstructured":"Boselin, P., Pradeep, M., and Gajendran, E. (2017). Monitoring Climatic Conditions Using Wireless Sensor Networks. A Multidiscip. J. Sci. Res. Educ., 3, Available online: https:\/\/ssrn.com\/abstract=2905669."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vo, M.-T., Thanh Nghi, T.T., Tran, V.-S., Mai, L., and Le, C.-T. (2014, January 16\u201318). Wireless sensor network for real time healthcare monitoring: Network design and performance evaluation simulation. Proceedings of the 5th International Conference on Biomedical Engineering in Vietnam, Ho Chi Minh City, Vietnam.","DOI":"10.1007\/978-3-319-11776-8_22"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TIM.2009.2023818","article-title":"An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks with Energy-Hungry Sensors","volume":"59","author":"Alippi","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.comnet.2014.03.027","article-title":"Energy efficiency in wireless sensor networks: A top-down survey","volume":"67","author":"Rault","year":"2014","journal-title":"Comput. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lin, S., Miao, F., Zhang, J., Zhou, G., Gu, L., He, T., Stankovic, J.A., Son, S., and Pappas, G.J. (2016). ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks. ACM Trans. Sen. Netw., 12.","DOI":"10.1145\/2746342"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.adhoc.2008.06.003","article-title":"Energy conservation in wireless sensor networks: A survey","volume":"7","author":"Anastasi","year":"2009","journal-title":"Ad Hoc Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jcc.2017.55001","article-title":"Survey on Single Path and Multipath Energy Efficient Routing Protocols for Wireless Sensor Networks","volume":"5","author":"Saeed","year":"2017","journal-title":"J. Comput. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kandukuri, S., Lebreton, J., Lorion, R., Murad, N., and Lan-Sun-Luk, J.D. (2016, January 18\u201320). Energy-efficient data aggregation techniques for exploiting spatio-temporal correlations in wireless sensor networks. Proceedings of the Wireless Telecommunications Symposium (WTS), London, UK.","DOI":"10.1109\/WTS.2016.7482055"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TITS.2015.2464792","article-title":"Development and testing of a 3g\/lte adaptive data collection system in vehicular networks","volume":"17","author":"Drira","year":"2016","journal-title":"IEEE Trans. Intel. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6006","DOI":"10.1109\/JSEN.2017.2730225","article-title":"Energy Efficient Data Acquisition Techniques using Context Aware Sensing for Landslide Monitoring Systems","volume":"17","author":"Prabha","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MCOM.2006.1632657","article-title":"Emerging techniques for long lived wireless sensor networks","volume":"44","author":"Raghunathan","year":"2006","journal-title":"IEEE Commun. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2822","DOI":"10.3390\/s140202822","article-title":"Energy-efficient sensing in wireless sensor networks using compressed sensing","volume":"14","author":"Razzaque","year":"2014","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Srbinovski, B., Magno, M., Edwards-Murphy, F., Pakrashi, V., and Popovici, E. (2016). An energy aware adaptive sampling algorithm for energy harvesting WSN with energy hungry sensors. Sensors, 16.","DOI":"10.3390\/s16040448"},{"key":"ref_21","unstructured":"GE (2017, September 14). Datasheet of GE\/Telaire 6004. Available online: http:\/\/www.veronics.com\/products\/infrared_gas_sensing\/6004_CO2_Module.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/JSEN.2012.2215733","article-title":"Context-adaptive multimodal wireless sensor network for energy-efficient gas monitoring","volume":"13","author":"Jelicic","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.adhoc.2015.08.009","article-title":"Residual energy-based adaptive data collection approach for periodic sensor networks","volume":"35","author":"Makhoul","year":"2015","journal-title":"Ad Hoc Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Laiymani, D., and Makhoul, A. (2013, January 1\u20135). Adaptive data collection approach for periodic sensor networks. Proceedings of the 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy.","DOI":"10.1109\/IWCMC.2013.6583769"},{"key":"ref_25","unstructured":"(2017, September 14). NOAA Real-Time Buoy Data, Available online: https:\/\/buoybay.noaa.gov\/locations\/jamestown."},{"key":"ref_26","unstructured":"(2017, September 14). Intel Lab Data. Available online: http:\/\/db.csail.mit.edu\/labdata\/labdata.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:13Z","timestamp":1760208493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,5]]},"references-count":26,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["s17112551"],"URL":"https:\/\/doi.org\/10.3390\/s17112551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,5]]}}}