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Research Interests / Research Profile

• 1. Energy Efficient Sensor Networking Algorithms: The research is focused on designing lifetime maximization algorithms including clustering algorithms, adaptive duty cycling schemes and energy harvesting technologies for wireless sensor networks. The adaptive duty cycling schemes adopt the Continuous time Markov chain (CTMC) and Markov decision process (MDP) with reinforcement learning (RL).

  2. Statistical Sensor Network Modeling: The research also focused on applying Gaussian Process regression, quantile regression and related stochastic processes in modeling realistic wireless sensor network problems, specifically, event detection algorithms for wireless sensor networks with random unknown sensor locations and spatial field reconstruction where the real data follows heavy tailed distributions. Also, the field covers the heterogenous networks with energy harvesting and localization of things with audibility.

  3. Machine Learning: The research also focused on applying multimodal fusion in neural networks for estimation, regression, prediction. The proposed method can be implemented in distributed manner for data processing in sensor nodes.