Resource allocation and sharing for heterogeneous data collection over conventional 3GPP LTE and emerging NB-IoT technologies

Abstract

Tremendous growth in the volumes and diversity of data to be collected in future Internet of Things (IoT) applications is one of the key challenges for the networking infrastructures as they evolve from 4G+ to true 5G systems. Particularly, ubiquitous deployments of wireless video surveillance cameras force the IoT service providers to support massive multimedia data transfer together with more ‘conventional’ machine-type communications. Hence, the data services in a complex IoT network may become heterogeneous, since several categories of traffic need to be supported simultaneously, each having its own loads, quality-of-service requirements, and radio technology preferences. An emerging example of the latter is the in-band deployment mode of the recently standardized NarrowBand IoT (NB-IoT) technology, where the same spectrum is shared between 3GPP LTE-connected high-end equipment and NB-IoT-connected low-end devices. However, while all of the necessary technology enablers are already in place, the question of how to share the available bandwidth efficiently has not been addressed comprehensively. Targeting the indicated challenge, this paper introduces feasible strategies for resource sharing between the multimedia and sensory data in a hybrid LTE/NB-IoT wireless deployment as well as compares them within our rigorous analytical methodology. The conducted numerical study advocates for one of the allocation strategies – dynamic resource sharing with reservation – as the preferred solution for reliable collection of heterogeneous data in large-scale 5G-grade IoT deployments.