Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.

This article proposes a new method of recognizing human gait. The proposed method is based on the idea that the problem of human gait recognition can be transformed from a spatio-temporal problem into the spatial domain, specifically the 2D image domain. This is done by representing a sample of a human gait as a still image. By doing so, all the recorded information is kept while enabling the use of proven content-based image retrieval (CBIR) techniques for recognition. The proposed method uses Microsoft Kinect human-computer interaction technology for data acquisition. To prove the validity of the proposed approach, the authors conducted a study with 50 participants.

In our study, we attempted to further investigate how Web 2.0 technologies influence workplace learning. Our particular interest was on using Wiki as a tool for corporate exchange of knowledge with the focus on informal learning. In this study, we collaborated with a multinational software development company that uses Wiki as a corporate tool since 2001. For our research, we used three different sources for acquisition of data. Primarily, we did an interview with top management. Next we acquired the data on usage statistics from the company Wiki. And finally we distributed a questionnaire in order to acquire users' feedback. Analysis provided many interesting results. One of the main conclusions is that Wiki is successfully used in this company, and large majority of employees finds it useful. Additionally, Wiki did aid informal learning, but there is still plenty of room for improvement.