This paper presents a Dynamic RB (Resource Blocks) Allocation (DRBA) in LTE (LongTerm Evolution) networks by utilizing Automatic Repeated Request (ARQ). From the ARQ status report, an Evolved-Node Base Station (eNodeB) computes the successfully received packets per unit time for each User Equipment (UE). Thus, an eNB can adequately allocate Resource Blocks (RB) for a UE. In DRBA, we consider three different traffic types (audio, video, and data) with the priority from the highest to the lowest. To prevent the starvation of data traffic, we set an upper bound of RBs for audio and video traffic. To demonstrate the superiority of DRBA, we perform NS-3 simulation. Simulation results show that DRBA can perform much better than the traditional Automatic Modulation and Coding (AMC) scheme. In particular, when a UE is in high interference/noise environment, DRBA can achieve higher utilization, lower blocking rate, and admit more successfully connected UE.

In this paper we have fitted the double binomial and multiplicative binomial distributions as log-linear models using sufficient statistics. This approach is not new as several authors have employed this approach, most especially in the analysis of the Human sex ratio in [1]. However, obtaining the estimated parameters of the distributions may be problematic, especially for the double binomial where the parameter estimate of p may not be readily available from the Log-Linear (LL) parameter estimates. Other issues associated with the LL approach is its implementation in the generalized linear model with covariates. The LL uses far more parameters than the procedure that employs conditional log-likelihoods functions where the marginal likelihood functions are minimized over the parameter space. This is the procedure employed in SAS PROC NLMIXED. The two procedures are essentially equivalent for frequency data. For models with covariates, the LL uses far more parameters and the marginal likelihood functions approach are employed here on three data set having covariates.

Emergence of the growing Location Based Services has a potential barrier of insecurity of users to use it due to privacy concerns. As these services requires, to broadcast constantly the user’s locality from untrusted server to get their position based on several services. The user will have privacy issues. LBS require trusted third party server if it is not meant to have peer-peer architecture, limited user’s security and large number of interactions. The work presented here implements two minor changes at two levels of LBS provision. The first one is the client’s system software based approach which allows no-internet zones as the most privacy protected zones. The second approach makes use of previous techniques of query processing by k anonymising. But by and large works on hierarchical k approach based on some intelligent selection by the clients/MOs. The results so far show an improving trend of using t.

Recent research has revealed a surge in the application of Stochastic Differential Equations (SDEs) in the modeling of infectious diseases. Factors emanating from this surge has been the ability of stochastic differential equations to lay one of the most imperative fundamental differences in the asymptotic dynamics of the deterministic and stochastic epidemic models [1]. These fundamental differences include the convergence of the sample path of stochastic models to the disease-free state and that of the deterministic model to the endemic equilibrium. Stochastic phenomenon occurs naturally in our environment, and when small number of reacting molecules is involved in the modeling system, deterministic models become inappropriate [2]. Stochastic models have properties which include the probability of an outbreak, the quasistationary probability distribution, the final size distribution of an epidemic and the expected duration of an epidemic that makes it unique from others [3]. Stochastic Processes can have moments and covariance function associated with them, which are functions of time, as do random variables [4].

In this research article, an optimal control model of malaria disease with standard incidence rate is proposed. Maximum Principle was employed to derive the necessary conditions for the existence of optimal control. Stochastic version of the model is derived by introducing a random perturbation in the main parameters of the model equations. Numerical solution of the optimality was derived and computed to investigate the optimum control strategy that would be efficacious to be implemented in reducing the number of exposed and infected humans as well as illustrating the explicit differences in the dynamics of the models.