Using SmartMesh IP in Smart Agriculture and Smart Building applications

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

doi:10.1016/j.comcom.2018.03.010

Cited by 25 publications

(20 citation statements)

References 2 publications

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“…Elsts et al [11] adopted TSCH for a Health IoT sensing platform and achieved, on average, more than 99.9% reliability in a series of long-term residential deployments (up to 12 months) in a city environment. Similar works document real-world deployments that confirm that TSCH is able to achieve very high levels of reliability at outdoor environments [12] and smart buildings [13] as well.…”

Section: Introductionsupporting

confidence: 72%

Rethinking IoT Network Reliability in the Era of Machine Learning

Fafoutis

Marchegiani

2019

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE C

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In the Internet of Things (IoT), wireless sensor networks are often paired with machine learning frameworks to deliver applications of high societal impact and support critical infrastructures. In this context, this paper investigates the relationship between network reliability and the reliability of the machine learning framework in terms of prediction accuracy. Our experimental analysis leverages six data sets of various degrees of information redundancy and considers four machine learning algorithms that are commonly used for classification. In turn, packet loss is inserted in the raw input data, emulating various networking loss patterns in terms of burstiness. The experimental results consistently demonstrate a non-linear relationship between the reliability of the network and the accuracy of the machine learning classifier, indicating that not all data packets are equally valuable to the application performance. We conclude with recommendations for IoT practitioners and IoT system designers.

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Section: Introductionsupporting

confidence: 72%

Rethinking IoT Network Reliability in the Era of Machine Learning

Fafoutis

Marchegiani

2019

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE C

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“…In the example network, the node B is scheduled to transmit a packet to the node D at a cell (0,1) and receive packet from the node D at a cell (1,2). The node B is also scheduled to communicate with the node E at the cells (2,1) and (5,0). It is also scheduled to communicate with the node A at the cells (3,1) and (5,3).…”

Section: Literature Survey and Taxonomy A Background On Time Slomentioning

confidence: 99%

“…The node B is also scheduled to communicate with the node E at the cells (2,1) and (5,0). It is also scheduled to communicate with the node A at the cells (3,1) and (5,3). However, it must be stressed that the IEEE 802.15.4 standard does not define how to schedule the slotframes, leaving it an open research area.…”

Section: Literature Survey and Taxonomy A Background On Time Slomentioning

confidence: 99%

“…The TSCH protocol from the IEEE 802.15.4-2015 standard [1] brings highly reliable communications to the field of low-power wireless networks. TSCH has found its uses in industrial monitoring [2], smart homes for healthcare [3], [4], smart buildings [5], environmental monitoring [6] and other areas. One aspect not specified in the IEEE 802.15.4 standard is the construction of a TSCH schedule.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Survey of Autonomous Scheduling Methods for TSCH

Elsts

Kim

et al. 2020

IEEE Access

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Time Slotted Channel Hopping (TSCH) is a link layer protocol defined in the IEEE 802.15.4 standard. Although it is designed to provide highly reliable and efficient service targeting industrial automation systems, scheduling TSCH transmissions in the time and frequency dimensions is left to the implementers. We evaluate the performance of existing autonomous scheduling approaches for TSCH on various traffic patterns and network configurations. We thoroughly investigate the pros and cons of each scheme; moreover, we propose the use of node based channel allocation to improve the performance of the best scheme, and demonstrate its practicality and reliability, with up to 6 percentage points better packet delivery ratio than the second best option while retaining a similar radio duty cycle. Finally, based on our extensive performance evaluation, we provide some guidelines on how to select a scheduler for a given network. INDEX TERMS IEEE 802.15.4, TSCH, autonomous scheduling, MAC, low-power wireless protocols.

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“…The TSCH (Time-Slotted Channel Hopping) protocol [ 1 ] is suitable for wireless network applications where high reliability and predictability is desired in conjunction with low power consumption. These include applications in industrial IoT [ 2 ], smart homes for healthcare [ 3 ], smart buildings [ 4 ], agricultural monitoring [ 5 ], and other areas.…”

Section: Introductionmentioning

confidence: 99%

TSCH-Sim: Scaling Up Simulations of TSCH and 6TiSCH Networks

Elsts

2020

Sensors

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TSCH (Time-Slotted Channel Hopping) and 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) low-power wireless networks are becoming prominent in the industrial Internet of Things (IoT) and other areas where high reliability is needed in conjunction with energy efficiency. Due to the complexity of IoT deployments, network simulations are typically used for pre-deployment design and validation. However, it is currently difficult and time-consuming to simulate large-scale IoT networks with thousands of nodes. This paper proposes TSCH-Sim: a new discrete event simulator for IEEE 802.15.4-2015 TSCH and 6TiSCH networks. The evaluation shows that simulation results obtained with TSCH-Sim show a good match with results from other simulators that are commonly used to investigate TSCH networks. At the same time, TSCH-Sim is faster than these alternatives at least by an order of magnitude, making it more practical to carry out simulations of large networks.

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Using SmartMesh IP in Smart Agriculture and Smart Building applications

Cited by 25 publications

References 2 publications

Rethinking IoT Network Reliability in the Era of Machine Learning

Rethinking IoT Network Reliability in the Era of Machine Learning

An Empirical Survey of Autonomous Scheduling Methods for TSCH

TSCH-Sim: Scaling Up Simulations of TSCH and 6TiSCH Networks

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