GMMR: A Gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks

Abstract Opportunistic IoT (OppIoT) network is a subclass of Internet of Things network, in which connections between the source and destination devices are intermittent. This infrequent connectivity is due to lack of network infrastructure and random mobility models followed by devices. These attributes of the network make routing in OppIoT, an increasingly complex problem. Moreover OppIoT shares its unique network characteristics with another class of networks called Opportunistic Networks (OppNets). This commonality enables the same routing designs to be applicable to both OppNets and OppIoT. Increased research interest in Machine Learning (ML) has led to its successful application in routing solutions for OppNets through protocols like KNNR and MLPROPH. In this paper we pursue utilizing ML to automate routing decisions in OppIoT. To this end we use Gaussian Mixture Models, an ML based soft clustering mechanism, to develop the proposed routing protocol called GMMR. The design of GMMR is such that it combines the advantages of both context-aware and context-free routing protocols. We compare the performance of GMMR with that of KNNR, HBPR, MLPROPH, and PROPHET using simulations run on Opportunistic Network Environment (ONE) simulator. The performance criteria for this comparison includes delivery probability, network overhead ratio, average hop count and number of messages dropped. We will show through the results of the simulations, that GMMR outperforms all of the aforementioned routing protocols in terms of every performance parameter.