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 R % 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. M F a i r is very easy to compute, whereas M O p t minimizes the total number of transmissions necessary for a message to reach the sink. M F a i r and M O p t 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 M a x T r a n s is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M a x T r a n s is fixed. For both TSCH networks, we show that M O p t provides a better reliability and a longer lifetime than M F a i r , which provides a shorter average end-to-end latency. M O p t provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler.
Keywords: ETX; IEEE802.15.4e; IoT; PDR; TSCH; industrial IoT; latency; network lifetime; probabilistic guarantee; reliability; retransmission; scheduling.