Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

An improved and provably secure three-factor user authentication scheme for wireless sensor networks

Abstract: In wireless sensor networks(WSNs), the process that a legal user retrieving the information in real-time from the sensor nodes should be based on mutual authentication among the user, the sensors and the gateway. So security issues have attracted researchers. In 2014, A. K. Das proposed a new three-factor user authentication scheme for WSNs to overcome the disadvantages in Jiang et al.’s two-factor user authentication scheme. However, we find that the scheme has several weaknesses including susceptibility to the off-line guessing attack and the de-synchronization attack and destitution of strong forward security. We also find weaknesses in two three-factor user authentication schemes for WSNs presented by A. K. Das in 2015, containing under the off-line password guessing attack and the user forgery attack. Also, the two schemes lack user anonymity and strong forward security. Then we give an improved three-factor remote authentication scheme for WSNs to eliminate the above weaknesses. To illustrate the security of our scheme, we give a standard formal proof in the random oracle model, a formal verification with ProVerif and the informal analysis of security properties. The results demonstrate that our scheme is robust enough to keep away from various security vulnerabilities. Through the comparison with some other recent schemes, ours is suitable for the application.

The physical–chemical characterization of mechanically-treated CFBC fly ash

Abstract: The physical–chemical characteristics of mechanically-treated circulating fluidized bed combustion (CFBC) fly ash, such as 45 μm sieve residue, granulometric distribution, water requirement, specific gravity, pH value, and mineralogical phases, were investigated. It was found that the grinding process can be divided into three stages. The increase in fineness of ground CFBC fly ash is very sharp in the first stage, then slows down in the second stage, and in the last stage it becomes almost invary. The water requirement decreases with prolonged grinding time, and slightly increases during the last stage of grinding. Ground CFBC fly ash shows a higher specific gravity due to the crushing of coarse particles and carbon particles. The pH of ground CFBC fly ash is greater than that of the original CFBC fly ash, indicating that ground samples react more rapidly with water. The mineralogical compositions remain unchanged with grinding, although the intensity of the crystalline phases decreases and the half peak width increases.

Electrochemical ozone sensors: A miniaturised alternative for ozone measurements in laboratory experiments and air-quality monitoring

Abstract: Ozone (O3) measurements are a critical component of air quality management and many atmospheric chemistry laboratory experiments. Conventional ozone monitoring devices based on UV absorption are relatively cumbersome and expensive, and have a relative high power consumption that limits their use to fixed sites. In this study electrochemical O3 sensors (OXB421, Alphasense) were used in a miniaturised O3 measurement device combined with LabJack and Labview data acquisition (DAQ). The device required a power supply of 5 V direct current (VDC) with a total power consumption of approximately 5 W. Total weight was less than 0.5 kg, low enough for portable in situ field deployment. The electrochemical O3 sensors produced a voltage signal positively proportional to O3 concentrations over the range of 5 ppb–10 ppm. There was excellent agreement between the performances of two O3 sensors with a good linear coefficient (R2 = 0.9995). The influences of relative humidity (RH) and gas sample flow rate on sensor calibrations and sensitivities have been investigated separately. Coincident calibration curves indicate that sensor performances were almost identical even at different RHs and flow rates after a re-zeroing process to offset the sensor baseline drifts. Rapid RH changes (∼20%/min) generate significant and instant changes in sensor signal, and the sensors consistently take up to 40 min to recover their original values after such a rapid RH change. In contrast, slow RH changes (∼0.1%/min) had little effect on sensor response. To test the performance of the miniaturised O3 device for real-world applications, the O3 sensors were employed for (i) laboratory experiments to measure O3 loss by seawater uptake and (ii) air quality monitoring over an 18-day period. It was found that ozone uptake by seawater was linear to the volume of linoleic acid on a sea surface microlayer and the calculated uptake coefficients based on sensor measurements were close to those from previous studies. For the 18-day period of air quality monitoring the corrected data from the O3 sensor was in a good agreement with those obtained by reference UV O3 analyser with an r2 of 0.83 (n = 8502). The novelty of this study is that the electrochemical O3 sensor was comprehensively investigated in O3 measurements in both laboratory and ambient air quality monitoring and it can to be a miniaturised alternative for conventional O3 monitoring devices due to its low cost, low power-consumption, portable and simple-conduction properties.

Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks

Abstract: The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.