Energy and Spectrum Efficient Communication Protocols, Signal Processing Techniques and Tradeoffs for Wireless Sensor Networks
A wireless Sensor Network (WSN) is a collection of sensors connected through a wireless medium with limited resources, such as batteries, bandwidth and computational power. We focus on signal processing and communication techniques intended to achieve best operational results under system constraints. First, we focus on distributed transmission scheduling for lifetime maximization of energy-limited WSNs. We present a novel distributed Medium Access Control (MAC) protocol, dubbed Time Varying Opportunistic Protocol (TOP) for lifetime maximization. Second, we investigate distributed transmission scheduling protocols for optimal detection while minimizing the total transmission power. Unlike existing approaches, we study the significance of exploiting both Channel State Information (CSI) and Likelihood Ratio Information (LRI) to design an adequate MAC protocol that minimizes the total transmission energy required for optimal detection. Third, we focus on multiple access schemes for detection over fading channel. Due to bandwidth constraint, the error probability does not approach zero as the number of sensors approaches infinity when using orthogonal channels. Therefore, for a large-scale WSN, multiple access schemes are preferred. We use large deviations theory and bounding techniques to analyze the likelihood-based multiple access detector over non-i.i.d fading channels and non-i.i.d observations. Finally, we consider multiple WSNs that co-exist in the same frequency band. We provide game theoretic approach to investigate throughput optimization of multi-channel ALOHA in cognitive radio networks.
* Thesis directed by Prof. Amir Leshem