Energy-optimized mapping of application to smartphone platform — A case study of mobile face recognition

Wang, Yi-Chu; Cheng, Kwang-Ting

doi:10.1109/cvprw.2011.5981820

Cited by 23 publications

(19 citation statements)

References 9 publications

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“…Fig. 2 shows the data of each application phase of the face recognition application, obtained by experiments in [20].…”

Section: Jobs and Application Phasesmentioning

confidence: 99%

“…Fig. 2 shows a typical diagram of application phases of a face recognition application (similar to [7], [20]), where phases 3 and 4 are exit phases. Conventionally, without cloud or cloudlets, the user has to execute all the application phases on the mobile device, using local execution components, i.e., processor, memory and cache.…”

Section: System Descriptionmentioning

confidence: 99%

“…We use the linear structured face recognition program [7], [20] as an example to illustrate the numerical results. Unless otherwise stated, the following scenario and parameter settings are employed in the performance evaluation.…”

Section: Parameter Setting and Performance Metricsmentioning

confidence: 99%

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Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Zhang

Niyato

Wang

2015

IEEE Trans. on Mobile Comput.

278

110

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The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

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“…Fig. 2 shows the data of each application phase of the face recognition application, obtained by experiments in [20].…”

Section: Jobs and Application Phasesmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Zhang

Niyato

Wang

2015

IEEE Trans. on Mobile Comput.

278

110

View full text Add to dashboard Cite

show abstract

“…The utilization of almost real-time face recognition techniques in mobile devices is now feasible due to the increases in processing power [4,5,6]. To date, biometric authentication in mobile devices is used for …”

Section: Related Workmentioning

confidence: 99%

Toward a non-intrusive, physio- behavioral biometric for smartphones

Vasiete

Chen

Char

et al. 2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

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Biometric authentication relies on an individual's inner characteristics and traits. We propose an active authentication system on a mobile device that relies on two biometric modalities: 3D gestures and face recognition. The novelty of our approach is to combine 3D gesture and face recognition in a non-intrusive and unconstrained environment; the active authentication system is running in the background while the user is performing his/her main task.

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“…Mobile GPUs are also successfully applied to develop applications of computer vision [35][36][37][38][39][40][41]. Rister et al developed a fast and efficient sift detector for mobile GPU [35].…”

mentioning

confidence: 99%

Fast filter bank convolution for three-dimensional wavelet transform by shared memory on mobile GPU computing

Zhao

2015

J Supercomput

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Mobile GPU applications usually constrain by the real-time requirement. However, FLOPS of mobile GPU is limited by the size and power supply of the SoC systems. Same to desktop GPUs, the mobile GPU consists of an on-chip memory hierarchy, and proper usage of memory hierarchy accelerates mobile GPU applications such as Discrete Wavelet Transform (DWT) to satisfy the real-time requirement. In this paper, by taking advantage of GPU shared memory in Tegra K1, a mobile GPU from Nvidia, we develop Bank Conflict Free Shared Memory Parallel DWT for mobile GPU applications. Computational results show that, with the display resolution of 640 × 350 (EGA), Bank Conflict Free Shared Memory Parallel DWT is significantly faster than SoC CPU-based DWT. Computational results also show that, with the display resolution of 320 × 200 (CGA), 640 × 480 (VGA), 800 × 600 (SVGA) and 1024 × 768 (XGA), Bank Conflict Free Shared Memory Parallel DWT can generally satisfy the real-time requirement.

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Energy-optimized mapping of application to smartphone platform — A case study of mobile face recognition

Cited by 23 publications

References 9 publications

Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Toward a non-intrusive, physio- behavioral biometric for smartphones

Fast filter bank convolution for three-dimensional wavelet transform by shared memory on mobile GPU computing

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