IoT has emerged as the next most important frontier for wireless networking with the goal of enabling many societally important applications in smart cities, industry, agriculture and health sectors for analytics, diagnostics, monitoring, tracking, and control.  Our goal is to enhance communications and sensing capabilities of these IoT network infrastructures using distributed communications, computing, learning and edge processing. One of the major novelties in our distributed approach is use of robots and UAVs to collaboratively coordinate, share and process information while increasing the network capacity, range and energy efficiency. We are one of the leading labs in the county in UAV based distributed beamforming and MIMO technologies that have demonstrated real-time synchronization and coherent signal processing of an UAV swarm using software-defined-radios (SDRs). We have also developed a novel framework for optimal placement of distributed nodes for maximum capacity and task oriented optimization of communications modalities. In smart city and massive IoT applications, we have investigated spectrum sharing protocols and distributed spectrum sensing algorithms for interference management and energy efficient access of millions of devices. We are also exploring distributed computing and communications in vehicular micro-clouds for latency sensitive applications.

Our research has been supported through

• SRC multi-university center CONIX
• NSF grant (1929874)
• Toyota

Projects

Deep Learning for Optimal UAV Basestation Placement

4publication(s)

Hybrid Vehicular and Cloud Distributed Computing

2publication(s)

Improving UAV Swarm Capacity using Distributed MIMO

6publication(s)

Realistic Wireless Communications UAV Simulator

0publication(s)

Spectrum Sharing for Massive Access in Unlicensed Ultra-Narrowband IoT Systems

4publication(s)

UAV Swarms Distributed Beamforming for Range Extension

4publication(s)

Publications