# Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective
## 2 Related Work
However, the behavior of hidden mobile app activities is unseen, which affects mobile devices in terms of energy consumption and security. [PERSON] et al. [12] proposes a kernel customization approach for reducing the power consumption in a smartphone application. In this approach, authors customize the kernel by optimizing CPU frequency scale, input-output (I/O) scheduling, under-lacking/under-voting and timer coalescing to adjust the power and workings of an app. The authors perform several tests on the customize kernel. Therefore, the modified kernel efficiently reduces the power consumption of the app. In [13], the authors studied the optimal service allocation for a group of mobile applications in mobile cloud computing. They proposed a novel framework named location-time workflows (LTW) that is used to model the mobile applications for handling the service allocation during mobility. Furthermore, the framework optimally partitioned the workflow over mobile applications in 2-tier architecture based on the utility metrics, energy consumption, cost of the services, and delay of the mobile applications. The proposed system is evaluated using varying mobility models include RandomWaypoint and Manhattan models. It achieves 20% less mobile energy consumption and a reduced (30%) network delay. [PERSON] et al. [14], studied wireless sensor deployment and monitoring problems. They also discussed infrastructures and technologies to support the use of sensors in the smart city. For efficient network deployment configuration, they investigate different aspects including coverage, lifetime, and connectivity. Similarly, in the case of monitoring (mobile and static sensors), they also analyze sensing time, location, devices, and power consumption. Finally, the authors identify some research opportunities and directions to further explore sensor deployment and monitoring. The authors in [15] proposed a knowledge-aware proactive node selection (KPNS) framework for an IoT environment. In KPNS, the selection of proactive nodes is based on their predicted preceding position. Furthermore, the KPNS system monitors the quality and energy efficiency of nodes. It also reduces the hot spot regions by efficiently utilizing the power of nodes. Considering energy wastage and ignorance of processing requests by the network, the authors proposed an EAR-ADS algorithm (including energy-aware routing and an adaptive delayed shutdown mechanism) in [16]. This algorithm deployed dynamic service function chains (SFC), which means offline and on-off nodes in the network. Furthermore, this technique saves the power consumption of servers. Due to the adaptive delay shutdown mechanism, the energy wastage is further reduced. Table 1 presents a comparative analysis of related work. As we see, existing approaches focus either on estimating the hidden ad energy consumption on the mobile app or to reducing the energy consumption of app in different perspectives such as code optimization, kernel optimization, etc. However, the hidden cost of ads over mobile apps is not addressed. Therefore, we propose an energy-efficient mechanism to reduce the hidden cost of ads on the mobile app.