Abstract
Wireless Sensor Networks (WSNs) have been widely applied to many different areas such as surveillance, healthcare, environmental and utility monitoring, etc. In WSNs, each sensor node has the characteristics of small size, limited power, and connected wirelessly. It is responsible for gathering and delivering sensing data over the network periodically. Thus, the energy consumption problem becomes a challenging issue to prolong the lifetime of WSNs. Several research works utilize data aggregation and/or data compression concept to reduce the quantity of necessary transmission, since it is the primary issue that consumes sensors’ power particularly. However, the implementation of these operations requires high computational power. In this thesis, two approaches adapting to sensing data distribution to largely reduce the amount of required data transmission with limited computation are proposed. They are: Adaptive Sampling with RD Model and Adaptive Sampling in Dynamic Mode. In the first approach, the target distortion is near-optimally distributed (in the rate-distortion sense) to every sensor node corresponding to their relative fluctuation. In the latter one, the possible occurrence of rapid data change in the sensing period is concerned and deliberately manipulated. To combine these two methods, we verify the data trend of each sensor when the prediction function needs to be updated. Then according to the data trend we can decide whether to use Adaptive Sampling with RD Model or Adaptive Sampling in Dynamic Mode. Finally, several real sensed data were gathered and employed to demonstrate the performance of the proposed methods.