WSN has been directed from military applications to various civil applications. However, many applications are not ready for real world deployment. Most challenging issues are still unresolved. The main challenge facing the operation of WSN is saving energy to prolong the network lifetime. Clustering is an efficient technique used for managing energy consumption. However, clustering is an NP hard optimization problem that can't be solved effectively by traditional methods. Computational Intelligence (CI) paradigms are suitable to adapt for WSN dynamic nature. This paper explores the advantages of CI techniques and how they may be used to solve varies problems associated to WSN. Finally, a short conclusion and future recommendation is being provided.

Abstract:Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a cluster head (CH) and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSNs to make efficient use of the available energy sources and reducing the traffic transmission can be achieved. In solving this problem we need to find the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. In this paper, we propose our initial idea on providing a hybrid clustering algorithm based on K-means clustering and particle swarm optimization (PSO); named KPSO to achieve efficient energy management of WSNs. Our KPSO algorithm is compared with traditional clustering techniques such as the low energy adaptive clustering hierarchy (LEACH) protocol and K-means clustering separately.

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The tremendous effects of air quality in large cities have been considered a severe environmental problem all over the world. Therefore, the international community agreed to develop air quality standards to monitor and control pollution rates around industrial communities. Harmful emission into the air is a sign that could extremely affect man's health, natural life and agriculture. Forecasting models is essential for predicting air quality. CO 2 emissions have been an international concern because of fossil fuels. In this study, Particle Swarm Optimization (PSO) is used for analyzing world CO 2 emission based on the global energy consumption. A parametric PSO model is developed to forecast CO 2 emission based set of attributes. They include: global oil, natural gas, coal, and primary energy consumption. A data set collected during the years 1980 and 2010 were used in this study. Experimental results show that PSO can provide good modeling results using a limited number of measurements compared to other linear models.