{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:43:14Z","timestamp":1777635794791,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T00:00:00Z","timestamp":1533772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud and edge (fog) computing. We propose an early warning system which detects wild animals near the road\/rail with the help of wireless sensor networks and alerts passing vehicles of possible animal crossing. Additionally, we show that animals\u2019 detection at the earliest and the related processing, if possible, at sensors would reduce the energy consumption of edge devices and the end-to-end delay in notifying vehicles, as compared to the scenarios where raw sensed data needs to be transferred up the base stations or the cloud. At the same time, machine learning helps in classification of captured images at edge devices, and in predicting different time-varying traffic profiles\u2014 distinguished by latency and bandwidth requirements\u2014at base stations, including animal appearance events at sensors, and allocating bandwidth in FiWi access networks accordingly. We compare three scenarios of processing data at sensor nodes, base stations and a hybrid case of processing sensed data at either sensors or at base stations, and showed that dynamic allocation of bandwidth in FiWi access networks and processing data at its origin lead to lowering the congestion of network traffic at base stations and reducing the average end-to-end delay.<\/jats:p>","DOI":"10.3390\/jsan7030035","type":"journal-article","created":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T10:36:31Z","timestamp":1533810991000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8734-9832","authenticated-orcid":false,"given":"Sandeep Kumar","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Technische Universit\u00e4t Carolo-Wilhelmina zu Braunschweig, 38106 Braunschweig, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6217-4052","authenticated-orcid":false,"given":"Francisco","family":"Carpio","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Technische Universit\u00e4t Carolo-Wilhelmina zu Braunschweig, 38106 Braunschweig, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Admela","family":"Jukan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Technische Universit\u00e4t Carolo-Wilhelmina zu Braunschweig, 38106 Braunschweig, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Suman, P., Gupta, P., Kassey, P.B., Saxena, N., Choudhary, Y., Singh, V., and Radhakrishna, M. (2015, January 17\u201320). Identification of trespasser from the signatures of buried single mode fiber optic sensor cable. Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India.","DOI":"10.1109\/INDICON.2015.7443850"},{"key":"ref_2","unstructured":"(2018, July 26). Wildunfall-Statistik 2016\/2017. Available online: http:\/\/www.jagdverband.de\/content\/wildunfallstatistik."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3041960","article-title":"Smart computing and sensing technologies for animal welfare: A systematic review","volume":"50","author":"Jukan","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.2307\/3802562","article-title":"Effectiveness of wildlife warning reflectors in reducing deer-vehicle collisions: A behavioral study","volume":"62","author":"Ujvari","year":"1998","journal-title":"J. Wildl. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.2193\/0091-7648(2006)34[1175:EOWWRF]2.0.CO;2","article-title":"Evaluation of wildlife warning reflectors for altering white-tailed deer behavior along roadways","volume":"34","author":"Gallagher","year":"2006","journal-title":"Wildl. Soc. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tennakoon, E., Madusanka, C., De Zoysa, K., Keppitiyagama, C., Iyer, V., Hewage, K., and Voigt, T. (2015, January 13\u201315). Sensor-based breakage detection for electric fences. Proceedings of the 2015 IEEE Sensors Applications Symposium (SAS), Zadar, Croatia.","DOI":"10.1109\/SAS.2015.7133589"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mathur, P., Nielsen, R.H., Prasad, N.R., and Prasad, R. (2014, January 11\u201314). Wildlife conservation and rail track monitoring using wireless sensor networks. Proceedings of the 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE), Aalborg, Denmark.","DOI":"10.1109\/VITAE.2014.6934504"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nakandala, M., Namasivayam, S., Chandima, D., and Udawatta, L. (2014, January 22\u201324). Detecting wild elephants via WSN for early warning system. Proceedings of the 7th International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka.","DOI":"10.1109\/ICIAFS.2014.7069632"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Viani, F., Rocca, P., Lizzi, L., Rocca, M., Benedetti, G., and Massa, A. (2011, January 12\u201316). WSN-based early alert system for preventing wildlife-vehicle collisions in Alps regions. Proceedings of the 2011 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, Torino, Italy.","DOI":"10.1109\/APWC.2011.6046747"},{"key":"ref_10","first-page":"104","article-title":"A wildlife monitoring system based on wireless image sensor networks","volume":"180","author":"Zhang","year":"2014","journal-title":"Sens. Transducers"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Viani, F., Polo, A., Giarola, E., Robol, F., Benedetti, G., and Zanetti, S. (2016, January 12\u201315). Performance assessment of a smart road management system for the wireless detection of wildlife road-crossing. Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy.","DOI":"10.1109\/ISC2.2016.7580835"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/LCOMM.2016.2612652","article-title":"Wireless sensor network for wildlife tracking and behavior classification of animals in Do\u00f1ana","volume":"20","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.ecoleng.2017.06.024","article-title":"Warning systems triggered by trains could reduce collisions with wildlife","volume":"106","author":"Backs","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/MWC.2016.7721750","article-title":"Foggy clouds and cloudy fogs: A real need for coordinated management of fog-to-cloud computing systems","volume":"23","author":"Tashakor","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.osn.2017.12.006","article-title":"Artificial intelligence (AI) methods in optical networks: A comprehensive survey","volume":"28","author":"Mata","year":"2018","journal-title":"Opt. Switch. Netw."},{"key":"ref_16","first-page":"1116","article-title":"Motor vehicle collisions with large animals","volume":"27","author":"Bashir","year":"2006","journal-title":"Saudi Med. J."},{"key":"ref_17","first-page":"646","article-title":"Highway mitigation fencing reduces wildlife-vehicle collisions","volume":"29","author":"Clevenger","year":"2001","journal-title":"Wildl. Soc. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1007\/s00114-002-0375-2","article-title":"African bees to control African elephants","volume":"89","author":"Vollrath","year":"2002","journal-title":"Naturwissenschaften"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ranaweera, C., Wong, E., Lim, C., and Nirmalathas, A. (2012). Next generation optical-wireless converged network architectures. IEEE Netw., 26.","DOI":"10.1364\/ACPC.2012.ATh2D.5"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Habel, K., Koepp, M., Weide, S., del Rosal, L.F., Kottke, C., and Jungnickel, V. (2017, January 19\u201323). 100G OFDM-PON for converged 5G networks: From concept to realtime prototype. Proceedings of the Optical Fiber Communication Conference, Los Angeles, CA, USA.","DOI":"10.1364\/OFC.2017.W1K.4"},{"key":"ref_21","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"D12","DOI":"10.1364\/JOCN.10.000D12","article-title":"Machine-learning-based prediction for resource (Re) allocation in optical data center networks","volume":"10","author":"Singh","year":"2018","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_23","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (arXiv, 2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","unstructured":"(2018, July 26). TensorFlow. Available online: https:\/\/www.tensorflow.org\/mobile\/tflite\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., and Sheth, A.P. (2017). Machine learning for Internet of Things data analysis: A survey. Digit. Commun. Netw.","DOI":"10.1016\/j.dcan.2017.10.002"},{"key":"ref_27","unstructured":"Riverbed (2014). Riverbed Modeler Academic Edition Release 17.5 A PL7, Riverbed."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/7\/3\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:17:40Z","timestamp":1760195860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/7\/3\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,9]]},"references-count":27,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["jsan7030035"],"URL":"https:\/\/doi.org\/10.3390\/jsan7030035","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201806.0281.v1","asserted-by":"object"},{"id-type":"doi","id":"10.20944\/preprints201806.0281.v2","asserted-by":"object"}]},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,9]]}}}