The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

Mobile ad-hoc networks (MANET) are one of wireless networks implementation. MANET is very popular technology initially based on military purposes. Specific modifications of MANET created a possibility to implement several new wireless networks. One of them is a wireless mesh network (WMN). Over the last ten years, WMNs have gained more and more attention and are now considered as a convincing solution for providing better Internet access services for end users. WMN is an emerging technology that offers a cost-effective and scalable method to connect wireless devices. The main problem in WMNs is a routing protocol, especially because it has to enable the access to network for both mesh and conventional clients. Most of the existing ad hoc routing protocols use minimum hop-count as a metric for identifying the best packet routes. This paper presents neural network based approach to routing protocol for WMN. Neural networks are capable to analyze very complex network environments and solve routing problems on optimal (or almost optimal) way.

Web applications are the most common type of application in modern society since they can be accessed by a large number of users at any time from any device. The only condition for their use is an Internet connection. Most applications run using the HTTP protocol and client-server architecture. This architecture is based on the use of API (Application programming interface), most often REST architecture (Representational State Transfer). If there are several different functionalities on the website that fill their content with data from the web server, for most of them a special HTTP request must be generated with one of the existing methods (GET, POST, PUT, DELETE). This way of communication can be a big problem if the connection to the Internet is weak, there are a lot of HTTP requests because you have to wait for each request to be executed and for the web server to return the data. In this paper, one implementation of GraphQL is presented. GraphQL is an open-source data query and manipulation language for APIs. GraphQL enables faster application development and has less server code. The key advantage is the number of HTTP requests because all the desired data of the page is obtained with one request. This paper will show a comparative analysis on the example of a real website in the case of using the REST architecture and GraphQL in the case of different qualities of Internet connections, code complexity and the number of required requests.

A content-based image retrieval system where an active learning strategy is used to gain relevance feedback (RF) is described. In this way retrieving process may be highly accelerated without significant degradation of accuracy Searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the user's relevance feedback. Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network. In this process user change feature vector which became more refined and appropriate for future search. In practice, several iterative steps are sufficient, as confirmed by intensive simulations.