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• W2971525145 abstract "To prolong battery life of mobile devices, image processing applications often exploit offloading techniques which run some or all of the computations on remote servers. Unfortunately, the existing offloading techniques do not consider the fact that data transmission time and energy consumption of wireless network interfaces exponentially increase when signal strength decreases. In this paper, we propose an adaptive offloading for image processing applications, which considers wireless signal strength. To improve performance and energy efficiency of offloading, we also propose to adaptively exploit local preprocessing (executing image preprocessing on local mobile devices), considering wireless signal strength; the local preprocessing usually reduces the size of transmission image in offloading. Our proposed technique estimates performance and energy consumption of the following three methods, depending on the wireless signal strength: 1) local execution (executing all the computations on the local mobile devices), 2) offloading without local preprocessing, and 3) offloading with local preprocessing. Based on the estimated performance and energy consumption, our technique employs one among the three methods, which is expected to result in the best performance or energy efficiency. In our evaluation on an off-the-shelf smartphone, when a user prefers performance to energy, our proposed technique improves performance by 27.1 percent, compared to the conventional offloading technique that does not consider the signal strength. On the other hand, when a user prefers energy to performance, our proposed technique saves system-wide (not just CPU nor wireless network interface) energy consumption by 26.3 percent, on average, compared to the conventional offloading technique." @default.
• W2971525145 created "2019-09-12" @default.
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• W2971525145 date "2020-01-01" @default.
• W2971525145 modified "2023-10-16" @default.
• W2971525145 title "Signal Strength-Aware Adaptive Offloading with Local Image Preprocessing for Energy Efficient Mobile Devices" @default.
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• W2971525145 doi "https://doi.org/10.1109/tc.2019.2939239" @default.
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