Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Robust classification of animal tracking data
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Localized Sensor Area Coverage with Low Communication Overhead
IEEE Transactions on Mobile Computing
Pilot study to monitor body temperature of dairy cows with a rumen bolus
Computers and Electronics in Agriculture
EURASIP Journal on Wireless Communications and Networking
Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Review: Wireless sensors in agriculture and food industry-Recent development and future perspective
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer perceptron (MLP)-based artificial neural network (ANN). The best performance of the ANN in terms of the mean squared error (MSE) and the convergence speed was achieved when it was initialized and trained using the Nguyen-Widrow and Levenberg-Marquardt back-propagation algorithms, respectively. The success rate of behavior classification into five classes (i.e. grazing, lying down, walking, standing and others) was 76.2% (@s"m"e"a"n=1.06) on average. The results of this study showed an important improvement regarding the performance of the designed MANET and behavior classification compared to the results of other similar studies.