mca 18: e1

Research Article

Power Optimization of WiFi Networks based on RSSI-awareness

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  • @ARTICLE{10.4108/eai.14-12-2018.159335,
        author={Bo Chen and Xi Li and Xuehai Zhou},
        title={Power Optimization of WiFi Networks based on RSSI-awareness},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={MCA},
        year={2019},
        month={6},
        keywords={Android, WiFi, received signal strength indication (RSSI),Power Save Mode},
        doi={10.4108/eai.14-12-2018.159335}
    }
    
  • Bo Chen
    Xi Li
    Xuehai Zhou
    Year: 2019
    Power Optimization of WiFi Networks based on RSSI-awareness
    MCA
    EAI
    DOI: 10.4108/eai.14-12-2018.159335
Bo Chen1,*, Xi Li1, Xuehai Zhou1
  • 1: University of Science and Technology of China, Hefei 215123, China
*Contact email: chenbo2008@ustc.edu.cn

Abstract

This research analyzed the impact of Wi-Fi received signal strength indication (RSSI) on power consumption of smart phones under different network environments. It was found that bad signal may lead to decrease in network link speed but increase in power consumption; whereas good signal contributed to rapid transmission and low power consumption. To reduce the power consumption and prolong the battery life, through combination with the original IEEE 802.11 PS mechanism, an optimization mechanism based on RSSI-awareness was proposed which aggregated network packets and delayed transmission. In essence, the proposed mechanism functioned through decreasing the number of mode switches of Wi-Fi component and extending the time of Wi-Fi stay in the Power-Save (PS) mode. Specifically, the signal strength was divided into three levels, including "good", "weak" or "bad". The decision tree was used to choose the best transmission method (Normal / Lineral / Exporational Transmission) according to previous and current signal strength. Algorithms were introduced to select the best split of decision tree for partitioning specific records into smaller subsets. Finally, the proposed mechanism was performed on the TI pandaboard platform. The results indicated that the proposed mechanism was practical and able to reduce the power consumption of smartphones in an effective manner.