Supervised classification for dynamic CoAP mode selection in real time wireless IoT networks

Abstract

The Internet Engineering Task Force recommends the use of a group of well-defined protocols to support Internet of ThingsLow-Power Low-Rate Networks (LLNs). These mechanisms range from physical and media access layer technologies like IEEE 802.15.4 and Bluetooth Low Energy to session and application layer protocols like the Constrained Application Protocol (CoAP). Specifically, CoAP provides, by means of two different modes of operation, great flexibility to deal with the power requirements of wireless LLNs. One mode supports fire-and-forget packet transmission while the other, through retransmissions, guarantees delivery. The trade-off between these mechanisms is the exchange of high packet loss by high latency and increased power consumption. In this paper we introduce an algorithm that dynamically predicts these parameters, by means of supervised learning, based on network conditions that result from Maximum Likelihood Estimation. This prediction, in turns, can be used for on-the-fly CoAP mode selection that accomplishes quality goals.