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Optimal Number of Message Transmissions for Probabilistic Guarantee of Latency in the IoT

The Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its a...

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Detalles Bibliográficos
Autores principales: Minet, Pascale, Tanaka, Yasuyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767674/
https://www.ncbi.nlm.nih.gov/pubmed/31540058
http://dx.doi.org/10.3390/s19183970
Descripción
Sumario:The Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least [Formula: see text] of messages generated by sensor nodes are delivered to the sink with a latency ≤L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. [Formula: see text] is very easy to compute, whereas [Formula: see text] minimizes the total number of transmissions necessary for a message to reach the sink. [Formula: see text] and [Formula: see text] are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions [Formula: see text] is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming [Formula: see text] is fixed. For both TSCH networks, we show that [Formula: see text] provides a better reliability and a longer lifetime than [Formula: see text] , which provides a shorter average end-to-end latency. [Formula: see text] provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler.