Distributed communications and sensing in IoT

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



E. Krijestorac, H. Sallouha, S. Sarkar, D. Cabric, "Agile Radio Map Prediction Using Deep Learning," in the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 2023
S. Hanna and D. Cabric, "Distributed Transmit Beamforming: Analyzing the Maximum Communication Range", to appear in IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2022)
Y. -C. Wang and D. Cabric, "Distributed UAV Swarm Placement Optimization for Compressive Sensing based Target Localization," 2023 International Conference on Computing, Networking and Communications (ICNC)
Krijestorac, Enes, Ghaith Hattab, Petar Popovski, and Danijela Cabric. "Multiband Massive IoT: A Learning Approach to Infrastructure Deployment." IEEE Transactions on Wireless Communications 21, no. 12 (2022): 10300-10314.
G. Hattab, S. Ucar, T. Higuchi, O. Altintas, F. Dressler, and D. Cabric, “Optimized Assignment of Computational Tasks in Vehicular Micro Clouds”, to appear in Int. Workshop on Edge Systems, Analytics and Networking (EdgeSys’19), Feb. 2019.