Baowei Wang

Temperature error correction based on BP neural network in meteorological wireless sensor network

Using meteorological wireless sensor network (WSN) to monitor the air temperature (AT) can greatly reduce the costs of monitoring. And it has the characteristics of easy deployment and high mobility. But low cost sensor is easily affected by external environment, often leading to inaccurate measurements. Previous research has shown that there is a close relationship between AT and solar radiation (SR). Therefore, we designed a back propagation (BP) neural network model using SR as the input parameter to establish the relationship between SR and AT error (ATE) with all the data in May. Then we used the trained BP model to correct the errors in other months. We evaluated the performance on the datasets in previous research and then compared the maximum absolute error, mean absolute error and standard deviation respectively. The experimental results show that our method achieves competitive performance. It proves that BP neural network is very suitable for solving this problem due to its powerful functions o...

Temperature Error Correction Based on BP Neural Network in Meteorological Wireless Sensor Network

Using meteorological wireless sensor network to monitor the air temperature (AT) can greatly reduce the costs of monitoring. And it has the characteristics of easy deployment and high mobility. But low cost sensor is easily affected by external environment, often lead to inaccurate measurements. Previous research has shown that there is a close relationship between AT and solar radiation (SR). Therefore, We designed a back propagation (BP) neural network model using SR as the input parameter to establish the relationship between SR and AT error (ATE) with all the data in May. Then we used the trained BP model to correct the errors in other months. We evaluated the performance on the data sets in previous research and then compare the maximum absolute error, mean absolute Error and standard deviation respectively. The experimental results show that our method achieves competitive performance. It proves that BP neural network is very suitable for solving this problem due to its powerful functions of non-linear fitting.

An authorized identity authentication-based data access control scheme in cloud

Cloud computing is a newfound service which has a rapid growth in IT industry recent years. Despite it has a huge contribution to the development of the technology and society, the cloud still exists some security deficiencies to block its development such as data leakage, illegal access and privacy risks. Hence, access control and user authentication is very important in cloud environment. Some related access control schemes has been proposed to solve the security problems in cloud, however, the high computation cost is a crucial factor in practical use. In this paper we propose a novel lightweight identity authentication-based access control scheme for cloud, due to adopt an authorized agency to assist the authentication and key distribution, this scheme is more efficient and practical than the related work.

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously.

A practical RFID grouping authentication protocol in multiple-tag arrangement with adequate security assurance

Radio Frequency Identification (RFID) is considered to be an authentication technology of great potential. Due to the bright future of low-cost RFID tags in practical situations, the authentication towards multiple tags and tag groups has become the research hotspot. However, there are many concerns about the security risks and privacy issues in lightweight RFID authentication scenarios. Many researches achievements have been made focusing on the existence of single tag in one object, while the arrangement that multiple tags attached to one object is out of consideration. In this paper, we propose a practical RFID grouping authentication protocol in multiple-tag arrangement with adequate security assurance. In our assumption, one object to be authenticated is attached with a group of RFID tags. The feedback towards various cases of the RFID tags is timely provided, which is necessary in practical situations. Additionally, the probable position and status of the object can be ascertained with a number of tags combined with the object. Moreover, the protocol is proved to offer enough security assurances and have resistance to various attacks under the security analysis. The regular operation of RFID system will not be severely damaged by the incidents occurred during the authentication process.

VPCH: A Consistent Hashing Algorithm for Better Load Balancing in a Hadoop Environment

MapReduce (MR) is a popular programming model for the purposes of processing large data sets among data clusters or grids, e.g. a Hadoop environment. Load balancing as a key factor affecting the performance of map resource distribution, has recently gained high concerns to optimize. Current MR processes in the realization of distributing tasks to clusters use hashing with random modulo operations, which can lead to uneven data distribution and inclined loads, thereby obstruct the performance of the entire distribution system. In this paper, a virtual partition consistent hashing (VPCH) algorithm is proposed for the reduce stage of MR processes, in order to achieve such a trade-off on job allocation. According to the results, using our method can reduce task execution time with or without MJR (mapreduce.job.reduce.slowstart.completedmaps) parameter set.

An Optimization Scheme in MapReduce for Reduce Stage

As a widely used programming model for the purposes of processing large data sets, MapReduce (MR) becomes inevitable in data clusters or grids, e.g. a Hadoop environment. Load balancing as a key factor affecting the performance of map resource distribution, has recently gained high concerns to optimize. Current MR processes in the realization of distributed tasks to clusters use hashing with random modulo operations, which can lead to uneven data distribution and inclined loads, thereby obstruct the performance of the entire distribution system. In this paper, a virtual partition consistent hashing (VPCH) algorithm is proposed for the reduce stage of MR processes, in order to achieve such a trade-off on job allocation. Besides, experienced programmers are needed to decide the number of reducers used during the reduce phase of the MR, which makes the quality of MR scripts differ. So, an extreme learning method is employed to recommend potential number of reducer a mapped task needs. Execution time is also predicted for user to better arrange their tasks. According to the results, VPCH can lead to load balancing and our prediction model can provide fast prediction than SVM with similar accuracy maintained.

An Extreme Learning Approach to Fast Prediction in the Reduce Phase of a Cloud Platform

As a widely used programming model for the purposes of processing large data sets, MapReduce (MR) becomes inevitable in data clusters or grids, e.g. a Hadoop environment. However, experienced programmers are needed to decide the number of reducers used during the reduce phase of the MR, which makes the quality of MR scripts differ. In this paper, an extreme learning method is employed to recommend potential number of reducer a mapped task needs. Execution time is also predicted for user to better arrange their tasks. According to the results, our method can provide fast prediction than SVM with similar accuracy maintained.