# Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective
## 1 Introduction  
In the modern era, the smartphone has a considerable impact on communications, studying, infotainment, finance, and various other factors of human life. According to Gartner statistics, the number of mobile phone users has been increased from 12% to 116% from 2000 to 2018--and is projected to reach 453.19 billion in 2022 [1; 2]. In order to make the usability of mobile gadgets better, various solutions have been proposed. However, due to its user-friendly interface and cost-effectiveness, Android is considered one of the most popular platforms in the smart world. Therefore, with the increase in Android usability, the range of Android applications is growingexponentially with the passage of time [3,4]. Therefore, Android developers focus on app development to get huge revenue. In this context, advertisements through smartphone apps have gained a great attention. A recent analysis shows that more than $30 billion were spent on marketing through smartphones in 2014. Furthermore, the analysis predicts the amount of mobile advertisements even exceed TV advertisement [5]. Based upon this work, it was felt that overall optimization is required for enhancing the overall experience. Typically, the Android developer gets ads from Google Ads Services and uses the mobile app to generate revenue; on the other hand, end users get the free app. It is a \"win-win\" situation for developers and users. Presently, a lot of Android developers develop an app without considering energy efficiency. Therefore, smart devices with limited resources of battery expend energy rapidly. Because of the quick power dissipation of mobile, end users under-rate an app, meaning that the developer needs to redesign an app, which is costly in terms of time and money [6]. Recently, a lot of research works have been conducted on energy-efficient techniques within software, application, and hardware level. More specifically, researchers have focused on application level because the developer is unable to buy and operate specialized hardware while designing a mobile app. On the other hand, a recent survey of the mobile ecosystem shows that the perception of free apps is misleading. In fact, almost half of the apps on the Google Play Store contain an excessive amount of ads [7,8]. Consequently, mobile ads contain hidden costs both for an end user and developer. The hidden ad cost affects end users as they slow down the response (consume an excessive amount of CPU memory), increase network usage, and increase energy cost (an excessive amount of battery usage). For the developer, the hidden costs may result in bad feedback [9]. Hence, the developer wants an approach to estimate hidden ad energy cost on a mobile app. Therefore, [PERSON] et al. [5] have proposed an approach for the estimation of hidden power cost of ads at the application level of a smartphone. The estimation approach works in two phases: the first one is before the app implementation phase, where static modeling is applied to static ad configuration to generate energy consumption of mobile ads. The second one is after app implementation, where a dynamic model is applied on an implemented app to compute the energy consumption of ads. However, this work only estimates the hidden ad energy costs. This motivates us to propose an approach that reduces the hidden energy cost of ads over mobile app. In the proposed work, gamma correction is applied over mobile ads, which reduces the hidden energy cost in terms of power dissipation and network usage. Furthermore, the main contribution of the work is summarized below:  
* Gamma correction reduces the size of the mobile ad by adjusting pixels and reducing background color
* After size reduction, there is illumination of the content of the mobile ad  
The experiment validates that the proposed approach efficiently reduces power dissipation of the mobile app caused by mobile ads; therefore the developer applies this approach to improve app rating and feedback.