This work considers the sampling design for detection problems.Firstly,we focus on studying the effect of signal shape on sampling design for Gaussian detection problem.We then investigate the sampling design for distributed detection problems and compare the performance with the single sensor context. We also propose a sampling design scheme for the cluster-based wireless sensor networks.The cluster head employs a linear combination fusion to reduce the dimension of the sampled observation.Mathematical verification and simulation result show that the performance loss caused by the dimensionality reduction is exceedingly small as compared with the benchmark scheme,which is the sampling scheme without dimensionality reduction.In particular,there is no performance loss when the identical sampling points are employed at all sensor nodes.

Recently, wireless sensor networks have become one of the most exciting areas for research and development. However, sensor nodes are battery operated, thus the sensor's ability to perform its assigned tasks is limited by its battery capacity; therefore, energy efficiency is considered to be a key issue in designing WSN applications. Clustering has emerged as a useful mechanism for trade-off between certain design goal conflicts; the network life time, and the amount of data obtained. However, different sources of energy waste still exist. Furthermore, in such dynamic environments, different data rate requirements emerge due to the current network status, thus adapting a response to the changing network is essential, rather than following the same principle during the network's lifespan. This thesis presents dynamic techniques to adapt to network changes, through which the limited critical energy source can be wisely managed so that the WSN application can achieve its intended design goals. Two approaches have been taken to decreasing the energy use. The first approach is to develop two dynamic round time controllers, called the minimum round time controller MIN-RC and the variable round time controller VAR-RC, whereas the second approach improves intra-cluster communication using a Co-Cluster head; both approaches show better energy utilisation compared to traditional protocols. A third approach has been to develop a general hybrid protocol H-RC that can adapt different applications requirements; it can also tolerate different data rate requirements for the same application during the system's lifetime.

Recently, wireless sensor networks have become one of the most exciting areas for research and development. However, sensor nodes are battery operated, thus the sensor¿s ability to perform its assigned tasks is limited by its battery capacity; therefore, energy efficiency is considered to be a key issue in designing WSN applications. Clustering has emerged as a useful mechanism for trade-off between certain design goal conflicts; the network life time, and the amount of data obtained. However, different sources of energy waste still exist. Furthermore, in such dynamic environments, different data rate requirements emerge due to the current network status, thus adapting a response to the changing network is essential, rather than following the same principle during the network¿s lifespan. This thesis presents dynamic techniques to adapt to network changes, through which the limited critical energy source can be wisely managed so that the WSN application can achieve its intended design goals. Two approaches have been taken to decreasing the energy use. The first approach is to develop two dynamic round time controllers, called the minimum round time controller MIN-RC and the variable round time controller VAR-RC, whereas the second approach improves intra-cluster communication using a Co-Cluster head; both approaches show better energy utilisation compared to traditional protocols. A third approach has been to develop a general hybrid protocol H-RC that can adapt different applications requirements; it can also tolerate different data rate requirements for the same application during the system¿s lifetime.