Energy Optimization Of Mobile Terminals For Social Applications

With the rapid development of the mobile Internet and the emergence of various new mobile applications,people's demand for fast-paced social networking continues to increase,and mobile terminals are becoming more and more functional.Their energy and computing capacities have severely restricted the performance of social networking applications for mobile terminals,causing two problems: power over-consumption from instability of network access when using applications and optimization of software power consumption resulting from system hardware.To address the above problems,the author proposes an optimization model on both the hardware and software,which combines the users' communication patterns and the context of the device.The main tasks include the following aspects: 1.By researching the frequent network state switching behavior caused by the intermittent connection of the access network(such as transmission signal mutation and network disconnection)caused by the mobile environment changes during the use of mobile social applications,the power consumption of the terminal is significantly increased.The problem is to propose an energy optimization method that combines network sensing and dynamic CPU adjustment.The state information of the mobile network and the WiFi network for a certain period of time is firstly collected,and then reduced and classified through PCA and SVM method respectively to divide the network signal strength characteristics into a default mode and a high frequency mode.The power optimization can be achieved by dynamically adjusting the CPU operating mode according to the classification result,thus improve the experience of using social application eventually.2.The tightness of the permissions in mobile terminals under different operating systems causes barriers implementing power consumption optimization in a hardware-level,that is,the difficulty of obtaining permission to switch the operating mode of the CPU.This paper proposes a mobile social content based on the software level.Dynamically refreshed MDP(Markov Decision Process,MDP)energy consumption optimization method.This method bypasses system access barriers and comprehensively considers different network environment conditions.To be specific,based on the user preferences and mobile phone context,the collected data is transferred to the local server for processing,and an optimal decision table is calculated through the Markov decision process.Guided by this table,we dynamically regulate the way how a phone access the network as well as what best format of a picture in social applications should download.3.The results of simulation experiments show that the two optimization methods mentioned above can achieve the purpose of reducing the energy consumption while ensuring the user experience.The first method that combines network sensing and dynamic adjustment of CPU,optimization rate of energy consumption can be improved in different application scenarios as along with the increase using frequency,the top optimization rate is about 10.51%.The MDP energy consumption optimization method based on the dynamic refresh of mobile social content reduces the energy consumption of mobile social applications from the software level,and generates an optimal decision table for each user,ensuring the highest energy consumption efficiency of approximately 12.1% while ensuring different users experiences.