Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distr...Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.展开更多
The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based o...The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based on the historical data collected by the buoys with sensing capacities, a novel data compression algorithm called adaptive time piecewise constant vector quantization （ATPCVQ） is proposed to utilize the principal components. The proposed system is capable of lowering the budget of wireless communication and enhancing the lifetime of sensor nodes subject to the constrain of data precision. Furthermore, the proposed algorithm is verified by using the practical data in Qinhuangdao Port of China.展开更多
For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing...For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.展开更多
基金the National Basic Research Program of China，the National Natural Science Foundation of China，the open research fund of National Mobile Communications Research Laboratory,Southeast University，the Postdoctoral Science Foundation of Jiangsu Province，the University Natural Science Research Program of Jiangsu Province，the Basic Research Program of Jiangsu Province (Natural Science Foundation)
文摘Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.
基金key project of the National Natural Science Foundation of China，Information Acquirement and Publish System of Shipping Lane in Harbor，the fund of Beijing Science and Technology Commission Network Monitoring and Application Demonstration in Food Security,the Program for New Century Excellent Talents in University，National Natural Science Foundation of ChinaProject，Fundamental Research Funds for the Central Universities
文摘The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based on the historical data collected by the buoys with sensing capacities, a novel data compression algorithm called adaptive time piecewise constant vector quantization （ATPCVQ） is proposed to utilize the principal components. The proposed system is capable of lowering the budget of wireless communication and enhancing the lifetime of sensor nodes subject to the constrain of data precision. Furthermore, the proposed algorithm is verified by using the practical data in Qinhuangdao Port of China.
文摘For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.
