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Open AccessArticle

Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks

Department of Electrical and Computer Engineering, Technische Universität Carolo-Wilhelmina zu Braunschweig, 38106 Braunschweig, Germany
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J. Sens. Actuator Netw. 2018, 7(3), 35; https://doi.org/10.3390/jsan7030035
Received: 15 June 2018 / Accepted: 26 July 2018 / Published: 9 August 2018
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’ 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— distinguished by latency and bandwidth requirements—at 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. View Full-Text
Keywords: fiber-wireless networks; edge (fog) computing; sensors; machine learning; ZigBee fiber-wireless networks; edge (fog) computing; sensors; machine learning; ZigBee
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Singh, S.K.; Carpio, F.; Jukan, A. Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks. J. Sens. Actuator Netw. 2018, 7, 35.

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