This paper presents a universal platform "uSensing" to support smartphones to communicate with sensor nodes in Wireless Sensor Networks (WSNs).Since phones have different CPU processers and operating systems...This paper presents a universal platform "uSensing" to support smartphones to communicate with sensor nodes in Wireless Sensor Networks (WSNs).Since phones have different CPU processers and operating systems,it is a challenge to merge these heterogeneities and develop such a universal platform.In this paper,we design both hardware and software to support the "universal" feature of uSensing:1) "uSD" card:an IEEE 802.15.4 physical communication card with SD interface;2) "uSinkWare":a WSNs middleware running on smartphones.Integrated with uSD card and uSinkWare,phones become mobile data sinks to access into WSNs and parse messages from sensor nodes.We demonstrate the proposed uSensing platform in a commercial smartphone to connect with our WSNs testbed,and validate that the smartphone has the same WSNs functions as commercial fixed sink.Additionally,we evaluate the performance of uSensing platform through measuring phone's CPU load and power consumption,and analyze the performance of these metrics theoretically.The results suggest that the phone-based mobile sink has enough capability to serve as a mobile sink of WSNs and can work up to twenty hours due to low power consumption.展开更多
There are a number of dirty data in observation data set derived from integrated ocean observing network system. Thus, the data must be carefully and reasonably processed before they are used for forecasting or analys...There are a number of dirty data in observation data set derived from integrated ocean observing network system. Thus, the data must be carefully and reasonably processed before they are used for forecasting or analysis. This paper proposes a data pre-processing model based on intelligent algorithms. Firstly, we introduce the integrated network platform of ocean observation. Next, the preprocessing model of data is presemed, and an imelligent cleaning model of data is proposed. Based on fuzzy clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering. The proposed dynamic algorithm can automatically f＇md the new clustering center with the updated sample data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results through observation data analysis.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.60932005China and Europe Government Cooperation Projects of the Ministry of Science and Technology under Grant No.2010DFA11680the Tsinghua Sci-Tech Project under Grant No.2011THZ0
文摘This paper presents a universal platform "uSensing" to support smartphones to communicate with sensor nodes in Wireless Sensor Networks (WSNs).Since phones have different CPU processers and operating systems,it is a challenge to merge these heterogeneities and develop such a universal platform.In this paper,we design both hardware and software to support the "universal" feature of uSensing:1) "uSD" card:an IEEE 802.15.4 physical communication card with SD interface;2) "uSinkWare":a WSNs middleware running on smartphones.Integrated with uSD card and uSinkWare,phones become mobile data sinks to access into WSNs and parse messages from sensor nodes.We demonstrate the proposed uSensing platform in a commercial smartphone to connect with our WSNs testbed,and validate that the smartphone has the same WSNs functions as commercial fixed sink.Additionally,we evaluate the performance of uSensing platform through measuring phone's CPU load and power consumption,and analyze the performance of these metrics theoretically.The results suggest that the phone-based mobile sink has enough capability to serve as a mobile sink of WSNs and can work up to twenty hours due to low power consumption.
基金Key Science and Technology Project of the Shanghai Committee of Science and Technology, China (No.06dz1200921)Major Basic Research Project of the Shanghai Committee of Science and Technology(No.08JC1400100)+1 种基金Shanghai Talent Developing Foundation, China(No.001)Specialized Foundation for Excellent Talent of Shanghai,China
文摘There are a number of dirty data in observation data set derived from integrated ocean observing network system. Thus, the data must be carefully and reasonably processed before they are used for forecasting or analysis. This paper proposes a data pre-processing model based on intelligent algorithms. Firstly, we introduce the integrated network platform of ocean observation. Next, the preprocessing model of data is presemed, and an imelligent cleaning model of data is proposed. Based on fuzzy clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering. The proposed dynamic algorithm can automatically f＇md the new clustering center with the updated sample data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results through observation data analysis.
