A Method of Data Aggregation for Wearable Sensor Systems.

Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can't adapt to the demand. In this paper, we study the system composed of many wearable devices with sensors, such as the network of a tactical unit, and introduce an energy consumption-balanced method of data aggregation, named LDA-RT. In the proposed method, we develop a query algorithm based on the idea of 'happened-before' to construct a dynamic and energy-balancing routing tree. We also present a distributed data aggregating and sorting algorithm to execute top-k query and decrease the data that must be transferred among wearable devices. Combining these algorithms, LDA-RT tries to balance the energy consumptions for prolonging the lifetime of wearable sensor systems. Results of evaluation indicate that LDA-RT performs well in constructing routing trees and energy balances. It also outperforms the filter-based top-k monitoring approach in energy consumption, load balance, and the network's lifetime, especially for highly dynamic data sources.

Analysis of manganese exposure level in different welding jobs and its effect on welding workers' health / 中国职业医学

OBJECTIVE: To examine the difference of manganese exposure level in different types of welding jobs and its effects on electric welding workers' health. METHODS: Eight-seven electric welding workers recruited in 2009 were selected by cluster sampling method as exposure group. The exposure group was divided into low,middle and high sub-exposure groups based on the different manganese exposure levels in workplace air. Thirty administrative and technical personnel were selected as control group. Manganese exposure levels of exposure group in workplace air were continuously measured from 2009 to 2013 for 5 years. The urine manganese levels of control and exposure groups were detected. Occupational health examination was conducted in these two groups in 2013. RESULTS: The trend of manganese exposure level in workplace air of exposure group from low to high was submerged arc welding post < gas tungsten arc welding post < hand welding post( P < 0. 01). The urine manganese levels in exposure group in 2010-2013 were higher than those of control group in the same corresponding year( P < 0. 01). With the increase of manganese exposure time,urine manganese levels increased in exposure group and the 3 sub-exposure groups( P < 0. 01); in 2012 and 2013,the urine manganese levels of exposure group showed an increasing trend with the increase of manganese exposure level( P < 0. 01). The positive rates of dizziness,body ache and memory lost in exposure group had an increasing trend with the increase of manganese exposure level( P < 0. 05). CONCLUSION: The difference of manganese exposure level in different types of welding jobs affects the urine manganese levels and health status of electric welding workers.

Random and directed walk-based top-(k) queries in wireless sensor networks.

In wireless sensor networks, filter-based top-  query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors' readings and declines in the overall range of all the readings. In this work, a random walk-based top-  query approach called RWTQ and a directed walk-based top-  query approach called DWTQ are proposed. At the beginning of a top-  query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the "right" way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.

Double cluster heads model for secure and accurate data fusion in wireless sensor networks.

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy.

An integrated model for robust multisensor data fusion.

This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM). Compared to some universal classification methods, the proposed one can be directly applied in multisensor data fusion applications, but not only for conventional classifications. Experimental results demonstrate that the proposed model is able to yield robust and reliable results in multisensor data fusion problems. In addition, this paper also draws some meaningful conclusions, which have significant implications for future studies.

A matched nested case-control study on the risk factors of metabolic syndrome among male criminal policemen / 中华劳动卫生职业病杂志

<p><b>OBJECTIVE</b>To explore the risk factors and their differences of metabolic syndrome (MS) on male criminal police, thereby provide the scientific basis to make prevention and control strategies about the metabolic syndrome for the criminal police career.</p><p><b>METHODS</b>Based on physical examination data of criminal police in 2010, 439 patients with MS (CDS) were randomly selected as cases. And as the 1:2 matched nested case-control study, 878 health controls were employed, which were matched with on the basis of sex and age (±1 year). An face-to-face epidemiological investigations on the past exposure status of several possible risk factors was conducted, such as the family history of hypertension and other social economic status, as well as body height and weight, waist circumference, blood pressure, serum lipid and plasma sugar. and the data were analyzed with logistic regression.</p><p><b>RESULTS</b>1317 cases were surveyed, through single factor logistic regression analysis found that 12 factors are related to exposure. Multivariate logistic regression analysis suggested that six factors, such as stress events (OR = 1.989, 95%CI: 1.467∼2.696), snoring (OR = 1.672, 95%CI: 1.218∼2.294), sweets (OR = 0.562, 95%CI: 0.412∼0.766), meat and products (OR = 1.494, 95%CI: 1.065∼2.094), siting after dinner for more than 3 h (OR = 1.399, 95%CI: 1.023∼1.915).</p><p><b>CONCLUSIONS</b>MS has become a important public health problems among criminal police. For their professional special, a series of bad habits , unhealthy life style and psychological problems became important risk factors of MS on criminal police. Targeted prevention and control measures should be taken to reduce the incidence of MS.</p>