In the effort to provide electrical power service and the sustaining fuel required to run generators at forward-deployed bases in Afghanistan and Iraq over more than 10 years, the US military spent billions of dollars and a paid a heavy toll in terms of human casualties. The green energy linear program for optimizing deployments (GELPOD) proof-of-concept model showed that a linear program could be used to optimize combat deployment of energy generation systems to minimize cost and casualties. Results indicated that reduction in both cost and casualties for renewable energy sources was highly dependent on fuel cost and deployment length. Neglected in the decision making process, however, were factors that impact the operational success of the mission. When deploying combat units, commanders must not only consider potential costs and casualties, they must also contend with battlefield mobility requirements, maintenance capability (or lack thereof), weather, and anticipated hostile action that could affect operational performance. This paper leverages the simple multi-attribute rating technique (SMART), pioneered by Edwards, to attempt to address this deficiency. The resulting simple multi-attribute rating technique for renewable energy deployment decisions (SMART REDD) model allows commanders to take mission attributes into consideration when making decisions on which energy source is most appropriate for the mission as well as providing information on operations costs, expected transportation requirements, and expected casualties.

Abstract-Nowadays, multimedia application and video streaming have gained a great popularity because of the boasting development on mobile hardware, and battery driven devices have emerged with a tremendous speed. Due to the important issue of battery efficiency on mobile devices, many optimization algorithms have being proposed toward various of battery powered platforms and scenarios. Most of the provided solutions choose to aim at the minimization of energy usage under a given task scheduling by adjusting parameters reside in the processes of different schemes. However, the battery discharging characteristics and its instant output pattern are still ignored if the optimization is done only from high level adjustment. In this paper, we propose a battery-aware optimization framework toward the H.264 video coding by applying dynamic frequency scaling on hardware platform. The CPU frequency can be dynamically adjusted according to the instant status of battery in order to maximize the number of the coding frame. Experimental results indicate the efficiency and effectiveness of the proposed optimization framework.

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