الفهرس 
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Abstract
Wireless Sensor Network (WSN) has achieved a great contribution in establishing the recent technological revolution. Each task performed by a sensor node requires an amount of energy to complete, however the sensor nodes are powered by limited capacity batteries and distributed in remote locations. So, extending the lifetime of the WSN is considered as an important challenge. Most previous Works on extending the WSN lifetime focused on traditional energy conservation techniques: duty cycle, data-driven and mobility. In this thesis, we survey and focus on two recent and efficient techniques: software defined network (SDN) and one of the most efficient and common machine learning techniques which is the clustering approach. We present an updated and comprehensive evaluation for the Software Defined Network (SDN) as an energy conservation technique. Furthermore, in this thesis, recent WSN clustering algorithms are addressed and compared based on novel and efficient comparison dimensions that help us to generate new results which prove that cluster head (CH)-load reduction has the largest effect on enhancing the energy efficiency of the WSNs compared to some other energy efficiency- enhancing factors. So, we also propose a model that divides the load of the CH role into small parts/values, in an optimized manner, and allocates these values to the whole nodes in the cluster. Therefore, using the Wireless Power Transfer (WPT) strategy, each member node in the cluster will transfer a specific amount of energy (equal to a part of the CH load that is assigned to the node) to the CH node which will also bear a suitable and a calculated part of the load instead of the whole load. So, the lifetime of the WSN is enhanced. Our simulation results show that our proposed model achieves high lifetime improvement over Leach and K-means clustering algorithms respectively.