Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

In this study,it is proposed that the diffusion least mean square(LMS)algorithm can be improved by applying the fractional order signal processing methodologies.Application of Caputo’s fractional derivatives are considered in the optimization of cost function.It is suggested to derive a fractional order variant of the diffusion LMS algorithm.The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium.The topology of the network is selected such that a smaller number of nodes are informed.In the network,a random sleep strategy is followed to conserve the transmission power at the nodes.The proposed fractional ordermodified diffusionLMS algorithms are applied in the two configurations of combine-then-adapt and adapt-then-combine.The average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel parameters.Amathematical proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues decreases.This increases the convergence as well as the steady state response even for the larger step sizes.Experimental results are shown for different number of nodes and fractional orders.The simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts,therefore,helps better solves the channel gains estimation problem in a distributed wireless environment.The algorithm has the potential to be applied in other applications related to learning and adaptation.

Recent development of transient electronics

Transient electronics are an emerging class of electronics with the unique characteristic to completely dissolve within a programmed period of time. Since no harmful byproducts are released, these electronics can be used in the human body as a diagnostic tool, for instance, or they can be used as environmentally friendly alternatives to existing electronics which disintegrate when exposed to water. Thus, the most crucial aspect of transient electronics is their ability to disintegrate in a practical manner and a review of the literature on this topic is essential for understanding the current capabilities of transient electronics and areas of future research. In the past, only partial dissolution of transient electronics was possible, however, total dissolution has been achieved with a recent discovery that silicon nanomembrane undergoes hydrolysis. The use of single- and multi-layered structures has also been explored as a way to extend the lifetime of the electronics. Analytical models have been developed to study the dissolution of various functional materials as well as the devices constructed from this set of functional materials and these models prove to be useful in the design of the transient electronics.

Diffusion logistic regression algorithms over multiagent networks

In this paper,a distributed scheme is proposed for ensemble learning method of bagging,which aims to address the classification problems for large dataset by developing a group of cooperative logistic regression learners in a connected network.Moveover,each weak learner/agent can share the local weight vector with its immediate neighbors through diffusion strategy in a fully distributed manner Our diffusion logistic regression algorithms can effectively avoid overfitting and obtain high classification accuracy compared to the non-cooperation mode.Furthermore,simulations with a real dataset are given to demonstrate the effectiveness of the proposed methods in comparison with the centralized one.

On the development of cat swarm metaheuristic using distributed learning strategies and the applications

Purpose–The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems.The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations.In addition,the models are tested for nonlinear systems with different noise conditions.In a nutshell,the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.Design/methodology/approach–Population-based evolutionary algorithms such as genetic algorithm(GA),particle swarm optimization(PSO)and cat swarm optimization(CSO)are implemented in a distributed form to address the system identification problem having distributed input data.Out of different distributed approaches mentioned in the literature,the study has considered incremental and diffusion strategies.Findings–Performances of the proposed distributed learning-based algorithms are compared for different noise conditions.The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate,but incremental CSO is slightly superior to diffusion CSO.Originality/value–This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems.Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.