{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:15:30Z","timestamp":1765041330447,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["787128"],"award-info":[{"award-number":["787128"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes the acquisition and installation of electronic components such as sensors, batteries, and the devices on which these components are installed. The problem of sensor coverage in the sewer network or a part of it is presented in the form of a mixed-integer programming model. This method guarantees that we obtain an optimal solution to this problem. A model was proposed that can take into account either only partial or complete coverage of the considered sewer network. The CPLEX solver was used to solve this problem. The study was carried out for a practically relevant network under selected scenarios determined by artificial and realistic datasets.<\/jats:p>","DOI":"10.3390\/s22186854","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"6854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Cost-Efficient Coverage of Wastewater Networks by IoT Monitoring Devices"],"prefix":"10.3390","volume":"22","author":[{"given":"Arkadiusz","family":"Sikorski","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7411-0948","authenticated-orcid":false,"given":"Fernando","family":"Solano Donado","sequence":"additional","affiliation":[{"name":"Institute of Telecommunications, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6647-5189","authenticated-orcid":false,"given":"Stanis\u0142aw","family":"Kozdrowski","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1002\/dta.2394","article-title":"Characterisation of aqueous waste produced during the clandestine production of amphetamine following the Leuckart route utilising solid-phase extraction gas chromatography-mass spectrometry and capillary electrophoresis with contactless conductivity detection","volume":"10","author":"Hauser","year":"2018","journal-title":"Drug Test. Anal."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cotell, C.M., Sprague, J.A., and Smidt, F.A. (1994). Alkaline Cleaning. Surface Engineering, ASM International.","DOI":"10.31399\/asm.hb.v05.9781627081702"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5179","DOI":"10.1080\/19443994.2013.768420","article-title":"Treatment of ion-exchange resins regeneration wastewater using reverse osmosis method for reuse","volume":"51","author":"Ghasemipanah","year":"2013","journal-title":"Desalin. Water Treat."},{"key":"ref_4","unstructured":"Consortium, H.M. (2019, October 17). Micromole\u2014Sewage Monitoring System for Tracking Synthetic Drug Laboratories. Available online: http:\/\/www.micromole.eu."},{"key":"ref_5","unstructured":"Consortium, H. (2022, September 06). H2020 SYSTEM Consortium\u2014Synergy of Integrated Sensors and Technologies for Urban Secured Environment. Fact Sheet Available at EC Website. Available online: https:\/\/cordis.europa.eu\/project\/rcn\/220304\/factsheet\/en."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"And\u00f2, B., Baldini, F., Di Natale, C., Marrazza, G., and Siciliano, P. (2018). A Distributed Sensor Network for Waste Water Management Plant Protection. Convegno Nazionale Sensori, Springer International Publishing.","DOI":"10.1007\/978-3-319-55077-0"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.watres.2017.06.030","article-title":"Detection and quantification of lateral, illicit connections and infiltration in sewers with Infra-Red camera: Conclusions after a wide experimental plan","volume":"122","author":"Lepot","year":"2017","journal-title":"Water Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tan, F.H.S., Park, J.R., Jung, K., Lee, J.S., and Kang, D.K. (2020). Cascade of One Class Classifiers for Water Level Anomaly Detection. Electronics, 9.","DOI":"10.3390\/electronics9061012"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tashman, Z., Gorder, C., Parthasarathy, S., Nasr-Azadani, M.M., and Webre, R. (2020). Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference. Sustainability, 12.","DOI":"10.3390\/su12072897"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, D., Heery, B., O\u2019Neil, M., Little, S., O\u2019Connor, N.E., and Regan, F. (2019). A Low-Cost Smart Sensor Network for Catchment Monitoring. Sensors, 19.","DOI":"10.3390\/s19102278"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Perfido, D., Messervey, T., Zanotti, C., Raciti, M., and Costa, A. (2016). Automated Leak Detection System for the Improvement of Water Network Management. Proceedings, 1.","DOI":"10.3390\/ecsa-3-S5002"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rojek, I., and Studzinski, J. (2019). Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities. Sustainability, 11.","DOI":"10.3390\/su11020518"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ji, H., Yoo, S., Lee, B.J., Koo, D., and Kang, J.H. (2020). Measurement of Wastewater Discharge in Sewer Pipes Using Image Analysis. Water, 12.","DOI":"10.3390\/w12061771"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kuchmenko, T.A., and Lvova, L.B. (2019). A Perspective on Recent Advances in Piezoelectric Chemical Sensors for Environmental Monitoring and Foodstuffs Analysis. Chemosensors, 7.","DOI":"10.3390\/chemosensors7030039"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pisa, I., Sant\u00edn, I., Vicario, J., Morell, A., and Vilanova, R. (2019). ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants. Sensors, 19.","DOI":"10.3390\/s19061280"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Drenoyanis, A., Raad, R., Wady, I., and Krogh, C. (2019). Implementation of an IoT Based Radar Sensor Network for Wastewater Management. Sensors, 19.","DOI":"10.3390\/s19020254"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, J., Meng, F., Zhou, Y., Wang, Y., and Shi, P. (2018). Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18020606"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Desmet, C., Degiuli, A., Ferrari, C., Romolo, F., Blum, L., and Marquette, C. (2017). Electrochemical Sensor for Explosives Precursors\u2019 Detection in Water. Challenges, 8.","DOI":"10.3390\/challe8010010"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4666","DOI":"10.1109\/ACCESS.2022.3140391","article-title":"An Internet-of-Things Enabled Smart System for Wastewater Monitoring","volume":"10","author":"Solano","year":"2022","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Consortium, S. (2021, March 15). SIMONA\u2014Sistema Integrato di Competenze per il MONitoraggio, la Protezione e il Controllo Delle Infrastrutture Idriche, Fognarie e Ambientali. Available online: http:\/\/www.progettosimona.it."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.proeng.2015.08.956","article-title":"Optimal Placement of Water Quality Monitoring Stations in Sewer Systems: An Information Theory Approach","volume":"119","author":"Banik","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Banik, B., Alfonso, L., Di Cristo, C., and Leopardi, A. (2017). Greedy Algorithms for Sensor Location in Sewer Systems. Water, 9.","DOI":"10.3390\/w9110856"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Banik, B.K., Alfonso, L., Cristo, C.D., Leopardi, A., and Mynett, A. (2017). Evaluation of Different Formulations to Optimally Locate Sensors in Sewer Systems. J. Water Resour. Plan. Manag., 143.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000778"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ali, H., Osmani, S., and Banik, B. (2019). Integrating fuzzy logic with Pearson correlation to optimize contaminant detection in water distribution system with uncertainty analyses. Environ. Monit. Assess., 191.","DOI":"10.1007\/s10661-019-7533-x"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"211","DOI":"10.2166\/hydro.2012.066","article-title":"Information theory applied to evaluate the discharge monitoring network of the Magdalena River","volume":"15","author":"Alfonso","year":"2013","journal-title":"J. Hydroinformatics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alfonso, L., Lobbrecht, A., and Price, R. (2010). Information theory\u2013based approach for location of monitoring water level gauges in polders. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008101"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Alfonso, L., Lobbrecht, A., and Price, R. (2010). Optimization of water level monitoring network in polder systems using information theory. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008953"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sikorski, A., Kozdrowski, S., and Donado, F.S. (2021, January 23\u201325). IoT Device Deployment for Optimal Wastewater Network Coverage. Proceedings of the 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Hvar, Croatia.","DOI":"10.23919\/SoftCOM52868.2021.9559098"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Krzhizhanovskaya, V.V., Z\u00e1vodszky, G., Lees, M.H., Dongarra, J.J., Sloot, P.M.A., Brissos, S., and Teixeira, J. (2020). Improving Coverage Area in Sensor Deployment Using Genetic Algorithm. Computational Science\u2014ICCS 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-50436-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ins.2019.02.059","article-title":"An efficient genetic algorithm for maximizing area coverage in wireless sensor networks","volume":"488","author":"Hanh","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_31","unstructured":"Bi, Y., Kapoor, S., and Bhatia, R. (2018). Particle Swarm Optimization Algorithms for Maximizing Area Coverage in Wireless Sensor Networks. Proceedings of the SAI Intelligent Systems Conference (IntelliSys) 2016, Springer International Publishing."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Buras, M.P., and Solano Donado, F. (2021). Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network. Sensors, 21.","DOI":"10.3390\/s21103426"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chachu\u0142a, K., Nowak, R., and Solano, F. (2021). Pollution Source Localization in Wastewater Networks. Sensors, 21.","DOI":"10.3390\/s21030826"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chachu\u0142a, K., S\u0142ojewski, T.M., and Nowak, R. (2022). Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks. Sensors, 22.","DOI":"10.3390\/s22010387"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wallace, S.W. (1989). AMPL: A Mathematical Programing Language. Algorithms and Model Formulations in Mathematical Programming, Springer.","DOI":"10.1007\/978-3-642-83724-1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kozdrowski, S., \u017botkiewicz, M., Wnuk, K., Sikorski, A., and Sujecki, S. (2020). A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design. Appl. Sci., 10.","DOI":"10.3390\/app10196840"},{"key":"ref_37","unstructured":"Tandler.com (2021, November 21). Software for Water Management ++ Systems Isar. Available online: www.tandler.com."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:28:55Z","timestamp":1760142535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,10]]},"references-count":37,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186854"],"URL":"https:\/\/doi.org\/10.3390\/s22186854","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,9,10]]}}}