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Recent developments in wireless networking and communication have demonstrated improvements in many areas of our lives at a fast pace. Yet still the wireless industry is still not where it wants to be despite these advancements in technology, but have nonetheless accomplished a lot in such a short time and are continually working on improvements and new innovations. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management. [ABSTRACT FROM AUTHOR]

Analysis of the data obtained from the UAV is hampered by the low performance of the calculation unit on board the device. Most often, the procedure is carried out on a remote unit, data transmission to which is carried out via a wireless communication channel. The need for such a solution is due to the requirements for the operations performed, including both the analysis and formation of the control actions of the UAV and the decision-making on changes in the current flight tasks. The paper proposes an algorithm for the automated selection of areas with great detail. The algorithm allows you to reduce the amount of analyzed data. Reducing the size of the analyzed blocks allows you to introduce parallelization of processes, reduce the requirements for computing resources, select processing parameters and increase the speed of data analysis. The proposed algorithm is based on data analysis in local windows, with an automated selection of coefficients. The test dataset shows examples of masks of detailed objects that have different sizes. The proposed algorithm detects detailed elements in images with a complex object structure from 80% to 10% from the size image. For a small object size, the block was deleted. For large sizes, the algorithm searches elements inside the object by gouging out a part of it. The proposed algorithm allowed for an increase in the speed of analysis by 24% compared to the previously developed approach and made it possible to analyze data in the infrared range. [ABSTRACT FROM AUTHOR]