Energy consumption of wireless network communication is still a big issue and a lot of research papers have proposed many solutions to increase node life time. The WMN architecture is made up of a fixed and mobile component, whereas the wireless mesh networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. The reduction in the distance by a factor of two can result in at least four times more powerful signals at the receiver. This paper presents suggestions that the links are more reliable without the increase in power of the transmitter in individual nodes. As a result, the present simulations networks are nine mobile nodes for considering coverage issues of the service area. The analytic results show that the link power node for direct communication between two nodes with long distance consuming more power than it is cleared. The improvement in the network performance for maintaining is available and this solution can be used to implement mobility in such case with low power region for the wireless mesh networks.

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<p>Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.</p>