Design and Evaluation of a Low Energy Bluetooth Sensor Node for Animal Monitoring

Burman, Kunal Shyamadas; Schmidt, Sven; Houssaini, Dhouha El; Kanoun, Olfa

doi:10.1109/ssd52085.2021.9429390

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Last is the number of DIO failed transmission represented as DIO f ail . We set data transmission as one packet per second with the size of 20 bytes based on agriculture monitoring application [37].…”

Section: Performance Evaluationmentioning

confidence: 99%

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Fawwaz

Chung

2023

IEEE Access

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The 6TiSCH (IPv6 over IEEE802.15.4e time-slotted channel hopping mode) wireless sensor network architecture utilizes control packets to construct network formation. These control packets are essential for establishing communication links between nodes and configuring network settings. The trickle timer algorithm is utilized to broadcast the DIO control packet. DIO carries information about the available parent nodes, which is then used to form the routing tree. Sensors transmit control packets in one cell on each TSCH slotframe, called the minimal cell. This leads to the problem that RPL trickle timer algorithm encounters congestion in DIO control packet transmission with other control messages, particularly in dense networks. Moreover, high traffic transmission also leads to high queue usage, which then drops the DIO control packet. Failed DIO transmission can increase network formation time and energy consumption. To address this issue, we propose Q-Trickle, an adaptive trickle timer algorithm based on Q-learning that determines the optimal policy for transmitting or suppressing DIO based on minimal cell and transmission queue conditions. Q-Trickle adaptively selects a redundancy constant value and transmission interval that promotes fair transmission distribution and considers network condition. Additionally, a control scheme over minimal cell transmission is formulated to lower transmission congestion and faster synchronization. The proposed methods were assessed using simulation and actual testing on the FIT IoT-LAB testbed. The results indicated that Q-Trickle performed better than the benchmark methods. Q-Trickle decreases joining time, energy consumption, and number of failed DIO compared with the original algorithm by -13%, -11%, and -43%, respectively.

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Section: Performance Evaluationmentioning

confidence: 99%

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Fawwaz

Chung

2023

IEEE Access

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“…Maroto-Malino et al [139] developed a promising fusion system for a field setting with a few animals in the herd being fitted with a GPS collar for location determining and the rest of the animals being fitted with low-cost BLE tags to determine their relative location to the animals with the GPS collars. Nodes are becoming increasingly smaller and energy-efficient [140,141]. WSNs are often bioinspired, meaning that the network mechanisms are based on animal behaviour [142] and data collection may be real-time or more opportunistic [143].…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

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Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

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“…However, factors such as energy consumption of the nodes and other parameters must be taken into account. The use of large vehicles such as trucks or helicopters incurs high costs and requires highly specialized human intervention [15][16][17]. Moreover, the natural habitats of animals are likely to be disturbed.…”

Section: Introductionmentioning

confidence: 99%

Phase-Type Distributions of Animal Trajectories with Random Walks

Vera-Amaro,

Rivero-Ángeles,

Luviano-Juárez

2023

Mathematics

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Animal monitoring systems often rely on expensive and challenging GPS-based systems to obtain accurate trajectories. However, an alternative approach is to generate synthetic trajectories that exhibit similar statistical properties to real trajectories. These synthetic trajectories can be used effectively in the design of surveillance systems such as wireless sensor networks and drone-based techniques, which aid in data collection and the delineation of areas for animal conservation and reintroduction efforts. In this study, we propose a data generation method that utilizes simple phase-type distributions to produce synthetic animal trajectories. By employing probability distribution functions based on the exponential distribution, we achieve highly accurate approximations of the movement patterns of four distinct animal species. This approach significantly reduces processing time and complexity. The research primarily focuses on generating animal trajectories for four endangered species, comprising two terrestrial and two flying species, in order to demonstrate the efficacy of the proposed method.

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Design and Evaluation of a Low Energy Bluetooth Sensor Node for Animal Monitoring

Cited by 6 publications

References 17 publications

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Phase-Type Distributions of Animal Trajectories with Random Walks

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