No abstract

No abstract

Summary For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models.

Earlier, web services are used to retrieve data for small amount of user only, but now a day's more data retrieved through web services by more number of users in all the actions of day to day life. There are numerous web services available in the internet to collect the data. Today internet plays a vital role in every body's life. Internet provides all the information one can access through the web service. To make the retrieval more efficient, Hidden Markov Models (HMM) &Mediator Agent Model are used in the project. The Hidden Markov Models can be used to measure and predict the behavior of Web Services in terms of response time, and thus can be used to rank services quantitatively. The mediator agent Model is used to select web services with the help of ranking method. The Mediator Agent Model will provide web service to the user from the web service provider. It will be processed in such a way that optimal result is obtained. If many users refer the optimal site then server loading will occur to reduce that problem server redirection is used in this project.

No abstract

In this paper, a multilayered clustering framework is proposed to build a service portfolio to select web services of choice. It is important for every service provider to create a service portfolio in order to facilitate the service selection process for someone to obtain the desired service in the absence of public UDDI registries. To address this problem, a multilayered clustering approach applied on a variety of data pertaining to web services in order to filter and group the services of a similar kind which in turn will improve the leniency in the process of service selection is used. The advantages of the layer approach are reduced search space, combination of incremental learning and competitive learning strategies, reduced computational labour, scalability, robustness and fault tolerance. The results are subjected to cluster analysis to verify their degree of compactness and isolation and appropriate evaluation indices are used. The results were found passable with an improved degree of similarity.

No abstract

No abstract