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Research On Data-Oriented Energy-Saving Mechanism In Wireless Sensor Networks

Posted on:2009-11-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:R HuangFull Text:PDF
GTID:1118360305956383Subject:Electronic Science and Technology
Abstract/Summary:PDF Full Text Request
Wireless Sensor Networks as a novel technology, which could change the interactive mode between human being and physical world, has a wide application prospect and great research significance. The key purpose to study WSN is to maximize network lifetime by the premise of meeting QoS of networks. As energy resource constraint is the fundamental issue, thus the research on energy-saving mechanism is at the core position in WSN field. The dissertation focuses on essential characteristic of data-oriented and application-oriented in sensor networks, by analyzing the internal property, distribution of data, and combining energy consumption model, topologic structure of networks. For the aim of prolonging the lifetime of sensor networks, the dissertation studies energy efficient operation mechanism of sensor networks, through the research on reducing and balancing energy consumption.Firstly, the dissertation proposes prediction-mode-based filtering mechanism and energy-aware routing mechanism to solve the problems of waste of transmission energy cost and funnel effect respectively caused by time-redundant data and imbalanced flow distribution mode, according to the characteristic of temporal correlation on time series data in sensor networks. The design framework of filtering mechanism for time-redundant data is composed of prediction module for capturing the change law of time domain, self-learning module for updating model, and driving module for controlling data filtering operation. To build time-redundancy data filtering system, allocation rule on threshold of prediction accuracy and prediction-error-driven rule are introduced, personalized prediction threshold is allocated according to node energy status, internal information included in the data variation patterns is precisely judged and obtained based on prediction error bound, so as to further improve the recognition and filtering effect for time redundancy data. The design of energy-aware routing mechanism combines the advantages of ACO principle, which is self-adaptive to dynamic network situation, and the advantages of prediction module, which reveals the law of data flow change. By introducing node-load-factor into both construction of heuristic factor and design of local pheromone updating rule in ACO, artificial ant agents are endowed with perception ability of local energy status in WSN, and the self-adaptability and energy-cost-balance of routing construction are improved. The experiment result shows that, the above energy-saving mechanism effectively reduces and balances the energy cost of data gathering mechanism by mining the temporal redundancy and associability, and introducing ACO.Secondly, the dissertation studies energy-saving data gathering mechanism based on dipartite degree of service quality to solve the problems of energy waste and short life time of source node, according to QoS-oriented data value characteristic. The classified judgement methodology of data value is proposed, and formalized to the structure of data-value-factor. On the basis of data-value-factor, contribution-driven node scheduling mechanism which is mapped into SCP, and value redundancy data filtering system are designed. Contribution-driven node scheduling mechanism introduces data-value-factor into IMAH for the design of heuristic factor and global pheromone updating rule, which guides the artificial ant in solution space to obtain optimal solution based on value orientation, and further obtain the global optimization solution by iteration mode, on the premise of meeting the covering requirement. The design idea of value-diversity-based data filtering system is to transfer high-value packets with high priority and inhibit transmission of low-value packets by introducing data-value-factor into backoff mechanism in QoS-MAC layer, and finally reduces transmitted data amount, achieves filtering effect. The above energy-saving mechanism driven by QoS-oriented requirement is different from traditional modes with data statistical characteristic. The experiment result shows that, the proposed mechanism can adaptively adjust energy consumption according to different QoS levels, therefore, it is helpful to improve energy-saving effect.Thirdly, the dissertation proposes the methodologies for cluster construction and structure optimization based on mining data association rule, as well as content redundancy based filtering mechanism, to solve the problems of structure cluster's optimization caused by content-low-correlation, and energy waste caused by content-highly-correlation. By using association rule to analyze the data content relevance, content-highly-correlation cluster is constructed, the existing structure of cluster through rebuilding and self-healing algorithm is further optimized. Furthermore, content-redundancy-based filtering algorithm is designed to filter the content-redundant transmitted data in cluster by building the negotiation mechanism which takes content characteristics code (CCC) as core. Then, according to known content-correlation, distributed source coding is explored for energy efficient lossless-data-fusion. The above energy-saving mechanism well considers the content-similarity-based universal phenomenon in WSN. The experiment result shows that, the energy cost is significantly decreased by introducing the content-highly-correlation cluster and the filtering operation on redundant data. Finally, according to the statistical-characteristic of data in WSN,GMM is adopted to describe the statistical distribution characteristics of heterogeneous and incomplete data using semi-supervised learning method, to solve the difficulty of building model for heterogeneous incomplete data and mining distribution-mode-law. Based on accurate data distribution model, adaptive filtering mechanism driven by model matching degree is proposed, it adopts hypothesis testing method to judge the similarity between different distribution patterns of data sequence, and reduces redundant data amount generated in the source of data gathering, in order to achieve the energy-saving aim by mining and filtering the redundant distributed flow data. Data compression algorithm based on cluster mode and principal component analysis (PCA) is proposed in external-cluster data communication process, by filtering the redundant attributes and data reconstruction based on PCA. On the premise of meeting the cumulative variance contribution rate, data dimensionality is reduced, and energy-saving effect is achieved. The above energy-saving mechanism effectively solved the modeling and redundancy extraction problem on multi-attribute and incomplete nature of heterogeneous data. From the statistical-trait-oriented view, the novel data gathering mechanism on incomplete heterogeneous data is proposed.In the dissertation, the efficiency of the proposed mechanisms and algorithms are proved by both theoretical analysis and simulation verification. Besides, the dissertation provides the helpful exploration to the data-characteristic-oriented energy-saving mechanism in WSN.
Keywords/Search Tags:Wireless Sensor Networks, Data-oriented, Energy Efficiency, Energy-Saving Mechanism, Data gathering, ACO
PDF Full Text Request
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