AN IDENTITY-BASED KEY AGREEMENT SCHEME FOR LARGE SCALE SENSOR NETWORKS

To solve the problems of high memory occupation, low connectivity and poor resiliency against node capture, which existing in the random key pre-distribution techniques while applying to the large scale Wireless Sensor Networks （WSNs）, an Identity-Based Key Agreement Scheme （IBKAS） is proposed based on identity-based encryption and Elliptic Curve Diffie-Hellman （ECDH）. IBKAS can resist man-in-the-middle attacks and node-capture attacks through encrypting the key agreement parameters using identity-based encryption. Theoretical analysis indicates that comparing to the random key pre-distribution techniques, IBKAS achieves significant improvement in key connectivity, communication overhead, memory occupation, and security strength, and also enables efficient secure rekcying and network expansion. Furthermore, we implement IBKAS for TinyOS-2.1.2 based on the MICA2 motes, and the experiment results demonstrate that IBKAS is feasible for infrequent key distribution and rekeying for large scale sensor networks.

Estimation of Data Transfer Routes Fractal Dimension in Large Scale Networks

The article is devoted to the evaluation of fractal properties of routing data in computer large scale networks. Implemented the study of percolation network topological structures of large dimension and made their transformation into fractal macrostructure. An example of calculating the fractal dimension of the data path for the boundary of the phase transition between the states of network connectivity. The dependence of the fractal dimension of the percolation cluster on the size of the square δ-cover and conductivity value network of large dimension. It is shown that for the value of the fractal dimension of the route dc ≈ 1.5, network has a stable dynamics of development and size of clusters are optimized with respect to the current load on the network.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.

Multi-Wavelength Ultra-Weak Fiber Bragg Grating Arrays for Long-Distance Quasi-Distributed Sensing

Fiber Bragg grating(FBG)array,consisting of a number of sensing units in a single optical fiber,can be practically applied in quasi-distributed sensing networks.Serious signal crosstalk occurring between large-serial of identical FBGs,however,has limited the further increase in the number of sensing units,thus restricting applications only for short-distance sensing networks.To reduce the signal crosstalk,we design two novel types of 10-kilometer-long FBG arrays with 10000 equally spaced gratings,written on-line using a customized grating inscription system,which is affiliated to a drawing tower.Main factors causing signal crosstalk,such as spectral shadowing and multiple reflections,are firstly investigated in theory.Consistent with the theoretical findings,experimental results are proving that ultra-weak(the reflectivity of—40 dB)and multi-wavelength gratings of a number more than 10000 can be readily identified,with satisfied low crosstalk.The maximum attenuation of grating signal and minimum signal-to-noise ratio(SNR)in a single-wavelength array are 10.69 dB and 5.62 dB,respectively.As a comparison,by increasing the number of central wavelengths to three,the attenuation can be effectively reduced to 5.54dB and the minimum SNR has been improved to 8.14 dB.The current study significantly enhances the multiplexing capacity of FBG arrays and demonstrates promising potentials for establishing large-capacity quasi-distributed sensing networks.