(Not so) intuitive results from a smart agriculture low-power wireless mesh deployment

Brun‐Laguna, Keoma; Diedrichs, Ana; Dujovne, Diego; Léone, Rémy; Vilajosana, Xavier; Watteyne, Thomas

doi:10.1145/2979683.2979696

Cited by 10 publications

(11 citation statements)

References 6 publications

(13 reference statements)

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“…This value is calculated by subtracting a uniform variance of 40 dB from the Friis model equation output. The model was independently verified in an experimental setting . RSSI values are then converted to PDR values by a conversion table that is based on real‐world deployments.…”

Section: The 6tisch Simulatormentioning

confidence: 99%

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Simulating 6TiSCH networks

Municio

Daneels

Vučinić

et al. 2018

Trans Emerging Tel Tech

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6TiSCH is a working group at the IETF, which is standardizing how to combine IEEE802.15.4 time‐slotted channel hopping (TSCH) with IPv6. The result is a solution that offers both industrial performance and seamless integration into the Internet and is therefore seen as a key technology for the Industrial Internet of Things. This article presents the 6TiSCH simulator, created as part of the standardization activity, and which has been used extensively by the working group. The goal of the simulator is to benchmark 6TiSCH against realistic scenarios, something which is hard to do using formal models or real‐world deployments. This article discusses the overall architecture of the simulator, details the different models it uses (ie, energy and propagation), compares it to other simulation/emulation platforms, and presents five published examples of how the 6TiSCH simulator has been used.

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Section: The 6tisch Simulatormentioning

confidence: 99%

“…The model was independently verified in an experimental setting. 32 RSSI values are then converted to PDR values by a conversion table that is based on real-world deployments. ¶ It accurately reflects the relationship between the RSSI and PDR in large indoors industrial scenarios at the 2.4-GHz band.…”

Section: Propagation Modelmentioning

confidence: 99%

Simulating 6TiSCH networks

Municio

Daneels

Vučinić

et al. 2018

Trans Emerging Tel Tech

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“…The edge node is usually located in the farm. An example of a wireless meshed sensor network based on the Time Slotted Channel Hopping technology (TSCH) deployed in a peach-orchard is described in [14]. Other technologies are also possible like ZigBee, also based on the IEEE 802.15.4 standard, or LoRaWAN for long range communications.…”

Section: Towards Smart Farmingmentioning

confidence: 99%

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

Ammouri

Minet

Boudiaf³

et al. 2019

2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

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Soil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture.

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“…TSCH networks deployed in real-world conditions [3] in particular, in industrial conditions [4] -have to cope with dynamic and unpredictable temperature changes, which…”

Section: Introductionmentioning

confidence: 99%

Temperature-Resilient Time Synchronization for the Internet of Things

Elsts

Fafoutis

Duquennoy

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-Networks deployed in real-world conditions have to cope with dynamic, unpredictable environmental temperature changes. These changes affect the clock rate on network nodes, and can cause faster clock de-synchronization compared to situations where devices are operating under stable temperature conditions. Wireless network protocols such as Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard are affected by this problem, since they require tight clock synchronization among all nodes for the network to remain operational. This paper proposes a method for autonomously compensating temperature-dependent clock rate changes. After a calibration stage, nodes continuously perform temperature measurements to compensate for clock drifts at run-time. The method is implemented on low-power IoT nodes and evaluated through experiments in a temperature chamber, indoor and outdoor environments, as well as with numerical simulations. The results show that applying the method reduces the maximum synchronization error more than 10 times. In this way, the method allows reduce the total energy spent for time synchronization, which is practically relevant concern for low data rate, low energy budget TSCH networks, especially those exposed to environments with changing temperature.

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(Not so) intuitive results from a smart agriculture low-power wireless mesh deployment

Cited by 10 publications

References 6 publications

Simulating 6TiSCH networks

Simulating 6TiSCH networks

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

Temperature-Resilient Time Synchronization for the Internet of Things

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