Research On Application-oriented Data Aggregation In Dense Wireless Sensor Networks

With the development of information technology, Wireless Sensor Network is increasingly focused. Both of data gathering and tracking are the key application of Wireless Sensor Network. Aiming at the problem of high message complexity in data gathering of the large scale mobile Wireless Sensor Network, taking tracking as our main applications, we develop human-Behavior based Mobile Clustering Mechanism (BMC) and Probability-based multi-Cluster for Fast Data gathering (PCFD) for large-scale mobile network. The above algorithms are successfully applied in the pro-ject"CNGI and WSN Based Mine Underground Localization and Integrated Emer-gency Response System".Signal interference will wobble the clusters in practical environment, so it de-grades the performance of the clustering algorithm dramatically. To resolve the issue, Delay-ensured Best-effort Signal Smoothness (DBSS) and Dual Threshold Clustering (DTC) are developed. We also theoretically analyze the selection rules of the smoothness parameters. With the optimized parameters, DBSS smoothes out more than 90% of the fluctuations and the stubborn ones left are also weakened by about 50%. DTC adopts two thresholds lines to guard joining and leaving of a cluster. The thresholds can be adjusted according to the requirement of the system. It can reduce the message complexity greatly, thereby stabilizing the clustering algorithm.In traditional tracking systems, the mobiles report their locations periodically. With the number of the mobiles increasing, these methods will result in high loss rate of packets and rapid depletion of the network energy. In practice, we observe that some mobiles are so close to each other that we are informed the same location from the localization algorithm. So it is necessary and possible to reduce message complex-ity through merging the location messages. By exploiting the Received Signal Strength Indicator (RSSI), this paper proposes a human-Behavior based Mobile Clus-tering Mechanism for Radio Frequency (RF)-based Person Tracking Systems. It con-structs clusters according to the distance between mobiles and maintains clusters effi-ciently. In BMC, only the cluster-heads report locations periodically instead of each node, thus the message complexity is reduced greatly. In the cluster maintenance stage, we devise the named Probability-based State Informing mechanism (PSI), which can maintain the clusters effectively. The simulation shows that BMC based location re- port outperforms traditional ones by 64% of message reduction on average in impar-tial testing scenes.To gathering the data quickly and effectively in large-scale mobile Wireless Sensor Network, we develop Probability-based multi-Cluster for Fast Data gathering. PCFD selects cluster head with a variable probability. Then every cluster collects its own message and sends to the network. This method can not only control the number of clusters though adjusting the probability is that, but also construct the clusters ac-cording to the interests. Both simulation and theoretical analysis prove that the mes-sage complexity is O(M+n), where M is the scale of the network and n is the number of clusters.The contributions of this dissertation are listed as follows:1. We propose a signal smoothness algorithm DBSS, which is based on weighted means and dual curves. Also we analyze and optimize the selection rules of DBSS.2. Exploiting the traits of human behavior, we design a location data gathering algo-rithm for person tracking and the probability based state informing mechanism (PSI) is devised to maintain the cluster effectively.3. The probability based clustering algorithm PCFD is proposed to gathering the nodes'information quickly and effectively.4. Based on the algorithm and theory proposed in this dissertation, we develop and implement the CNGI and WSN Based Mine Underground Localization and Inte-grated Emergency Response System on the Micaz platform. The project is China the Next Generation Internet Project Important Special Item from the State De-velopment and Reform Commission of China.