Our main research areas
De-centralized Communications, In-network Processing and Intelligence for heterogeneous wireless sensor and communication networks
Distributed source-channel coding, adaptive cross-layer routing and MAC algorithms for collective intelligence, distributed computation and inference over wireless networks, in-network spectrum cartography, graph signal processing, and decentralized machine learning.
Signal Processing, Analytics and Machine learning for multi-sensor data
Efficient signal representation, sparse sampling, adaptive quantization, low-rank data modeling, dimensionality reduction, filter banks, robust statistical inference and learning
Multidisciplinary Tools and theoretical foundations for fundamental performance analysis and optimal design of signal processing algorithms and network protocols
Information Theory, Game Theory, Convex and non-convex Optimization, Lattices, Information science of complex systems, Bio-inspired algorithm design, Stochastic geometry, complexity analysis and approximation algorithms for NP-Hard problems.
In terms of implementation, the Lab has also experience in the analysis, design and deployment of testbeds for heterogeneous sensor networks and cyber-physical networked systems in various applications:
- Monitoring and Control of processes in Industrial Environments
- Remote and Intelligent Large-scale Environmental Monitoring
- Autonomous and Self-Organized Control of Natural Resources
- Cognitive Radios for Nomadic Broadband Access
- Distributed Intelligence for Heterogeneous Networks in Emergency Management Scenarios
- Ambient Intelligence for people with different types of disabilities
- Advanced co-operative Communications and Sensing for traffic applications