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Sybil attacks pose a serious threat for Wireless Sensor Networks (WSN) security. They can create problems in routing, voting schemes, decision making, distributed storage and sensor re-programming. In a Sybil attack, the attacker masquerades as multiple sensor identities that are actually controlled by one or a few existing attacker nodes. Sybil identities are fabricated out of stolen keys, obtained by captured benign nodes. Existing Sybil defensive mechanisms suffer from the restricted sensor network size, or cause excessive resource consumption for the sensor network. In this work we propose a Sybil node detection mechanism, based on RANDOM Key DISTRIBUTION (RKD) schemes that can cope with large network sizes and minimize the waste of resources. We explain the techniques each node can use in a network running q-composite RKD to detect Sybil identities and restrict their number. Our method requires no trust to other sensors, which is important to defend against the attack.

We study the limiting DISTRIBUTION of the degree of a given node in a scaled attachment RANDOM recursive tree, a generalized RANDOM recursive tree, which is introduced by Devroye et. al (2011). In a scaled attachment RANDOM recursive tree, every node i is attached to the node labeled [iXi] where X0,…, Xn is a sequence of i.i.d. RANDOM variables, with support in [0, 1) and DISTRIBUTION function F. By imposing a condition on F, we show that the degree of a given node is asymptotically normal.

In this research, the friction between the contact surfaces was modeled and simulated with RANDOM DISTRIBUTION of roughness in Nano scale. For this purpose, firstly the modified AMM friction model was derived for the contact between smooth flat/ rough flat surfaces on the basis of JKR contact model, HK friction model and RANDOM DISTRIBUTION of Greenwood-Williamson (GW) model. According to the results of simulation, the modified AMM friction model predict more value for the friction force considering Nano scale surface interaction forces, and it also has more accuracy in comparison to the initial AMM model. In the following, the obtained model was extended for evaluating the friction between smooth spherical/rough flat surfaces and finally the friction between the tip of Atomic Force Microscopic on the rough surface, then the effects of geometric parameters of surface included of standard deviation of asperities’ height and radius of asperities’ peak on the magnitudes of normal load, friction force and finally friction coefficient between atomic force microscopic tip and rough surface has been investigated. The achieved results demonstrate that normal force and friction force both will raise by rising of standard deviation of asperities’ height while as friction coefficient will decrease. Furthermore, the normal and friction forces and also friction coefficient will increase by increase of roughness’ peaks radius. It is also observed that the effects of standard deviation of asperities’ height on friction parameters is more than the effects of asperities’ peaks radius.

Most of natural phenomena are modeled with univariate and multivariate normal DISTRIBUTIONs and their derivatives. Folded normal variables are defined as the absolute of normal RANDOM variables. So far, univariate and bivariate normal DISTRIBUTIONs, their characteristics and usages have been studied too. DISTRIBUTION of the maximum of dependent RANDOM variables which have elliptically contoured DISTRIBUTION, has been considered by others. In this paper, DISTRIBUTION of the maximum of dependent RANDOM variables with bivariate folded standard normal DISTRIBUTION, which their joint DISTRIBUTION is not of the elliptically contoured family, is calculated. Also, the mean, variance and moment generating function of this DISTRIBUTION are investigated.

Often‎, ‎reliability systems suffer shocks from external stress factors‎, ‎stressing the system at RANDOM‎. ‎These RANDOM shocks may have non-ignorable effects on the reliability of the system‎. ‎In this paper‎, ‎we provide sufficient and necessary conditions on components' lifetimes and their survival probabilities from RANDOM shocks for comparing the lifetimes of two fail-safe systems in two cases‎: ‎(i) when components are independent‎, ‎and then (ii) when components are dependent‎. ‎We then apply the results for some DISTRIBUTION-free RANDOM variables with possibly different parameters to illustrate the established results.

The use of the two-parameter exponential DISTRIBUTION model in fitting survival and reliability analysis data in the presence of censored RANDOM data has recently attracted the attention of a large number of authors. Considering the importance of the model, its parameter estimation is discussed using the method of moment, maximum likelihood and shrinkage estimation. To present the interval shrinkage estimator, it is first proved that the moment estimators are asymptotically unbiased and the interval shrinkage estimator performs better compared to other estimators. Finally, using two real data sets and statistical criteria, the goodness of fit of the model is compared with censored RANDOM data based on parameter estimation methods.

RANDOM Key DISTRIBUTION (RKD) schemes have been widely accepted to enable low-cost secure communications in Wireless Sensor Networks (WSNs). However, efficiency of secure link establishment comes with the risk of compromised communications between benign nodes by adversaries who physically capture sensor nodes. The challenge is to enhance resilience of WSN against node capture, while maintaining the flexibility and low cost features of RKD schemes. We address this problem by proposing an effective technique, namely KDel, which don’ t require any special-purpose hardware or expensive computations. We demonstrate that our approach significantly increases the resilience of RKD schemes against node capture at the cost of a little communications, while maintaining network connectivity at the same level. Moreover, our scheme is generally applicable as it can improve the resilience of any RKD scheme.