NeTS: Small: Collaborative Research: Improving Spectrum Efficiency for Hyper-Dense IoT Networks

The emerging Internet of Things (IoT) technology will enable a whole new set of applications, imposing far reaching influence on many aspects of society, including management of health, transportation, agriculture, safety and emergency and disaster response. At the same time, the massive growth of IoT deployments poses several grand challenges to the wireless industry for their successful operation. This project aims to develop novel IoT architectures and algorithms to enable large-scale IoT deployments. In particular, through close collaboration of three academic institutions and industry, the proposed research will make advances by introducing holistic, cross-layer design framework and protocols for improving spectral/energy efficiency and latency of massive IoT networks. The research outcomes will support emerging standardization activities to enable IoT in next-generation wireless networks.

This project aims at tackling the following challenges: 1) System-Centric Waveform Design for Massive IoT: The researchers will couple stochastic geometry techniques and ambiguity functions of carrier waveforms to perform system-wide capacity analysis, compare waveform candidates for deployment suitability, develop joint equalizer and waveform designs for massive IoT deployments, and propose new waveforms having a good spectral efficiency, interference resilience, and latency characteristics. 2) Graph-based Radio Resource Management: Judicious radio resource management is critical to the proper functioning of hyper-dense IoT networks. The project will exploit weighted partitioning and matching tools from graph theory to develop waveform-aware dynamic resource allocation schemes to suppress mutual interference among a diverse set of wireless IoT links and improve spectrum utilization efficiency in single-hop and multi-hop massive IoT deployments. 3) Core network Connection Efficiency: Rethinking the core network functions for connection of massive IoT is required as they are not designed considering the IoT traffic characteristics. The project will develop and rigorously study an aggregation based connectivity model to manage multiple IoT device traffic using the same resources (i.e., bearers). For efficient groupings of the IoT devices that will share the same connection, researchers will use efficient evolutionary clustering methods, while giving preference to the interference minimizing groups obtained in earlier thrusts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.