A Task Execution Scheme for Dew Computing with State-of-the-Art Smartphones
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Hermann Kaindl | Alejandro Zunino | Cristian Mateos | Matías Hirsch | Tor-Morten Grønli | Tim A. Majchrzak | H. Kaindl | Alejandro Zunino | C. Mateos | Tor-Morten Grønli | Matías Hirsch
[1] Alejandro Zunino,et al. Motrol 2.0: A Dew-oriented hardware/software platform for batch-benchmarking smartphones , 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC).
[2] Alejandro Zunino,et al. A platform for automating battery-driven batch benchmarking and profiling of Android-based mobile devices , 2021, Simul. Model. Pract. Theory.
[3] Houbing Song,et al. A Volunteer-Supported Fog Computing Environment for Delay-Sensitive IoT Applications , 2021, IEEE Internet of Things Journal.
[4] Hermann Kaindl,et al. A Simulation-based Performance Evaluation of Heuristics for Dew Computing , 2021, HICSS.
[5] Kotagiri Ramamohanarao,et al. Application Management in Fog Computing Environments , 2020, ACM Comput. Surv..
[6] Alejandro Zunino,et al. DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments , 2020, Softw. Pract. Exp..
[7] Hong-Yuan Mark Liao,et al. YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.
[8] Luc Van Gool,et al. AI Benchmark: All About Deep Learning on Smartphones in 2019 , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[9] Tim A. Majchrzak,et al. Towards a resilience management guideline — Cities as a starting point for societal resilience , 2019, Sustainable Cities and Society.
[10] Fan Zhang,et al. AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence , 2018, Bench.
[11] Athena Vakali,et al. A Systematic Review for Smart City Data Analytics , 2018, ACM Comput. Surv..
[12] Alejandro Zunino,et al. Augmenting computing capabilities at the edge by jointly exploiting mobile devices: A survey , 2018, Future Gener. Comput. Syst..
[13] Klara Nahrstedt,et al. DROPLET: Distributed Operator Placement for IoT Applications Spanning Edge and Cloud Resources , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).
[14] Mohamed Faten Zhani,et al. Opportunistic Edge Computing: Concepts, Opportunities and Research Challenges , 2018, Future Gener. Comput. Syst..
[15] Alejandro Zunino,et al. A performance comparison of data-aware heuristics for scheduling jobs in mobile grids , 2017, 2017 XLIII Latin American Computer Conference (CLEI).
[16] Christoph Rieger,et al. A Taxonomy for App-Enabled Devices: Mastering the Mobile Device Jungle , 2017, WEBIST.
[17] Francesco Benedetto,et al. A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices , 2017, Adv. Eng. Informatics.
[18] Xu Chen,et al. Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing , 2017, IEEE Wireless Communications.
[19] Alejandro Zunino,et al. A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids , 2017, Journal of Grid Computing.
[20] Ejaz Ahmed,et al. Heterogeneity-Aware Task Allocation in Mobile Ad Hoc Cloud , 2017, IEEE Access.
[21] Jae-Ho Nah,et al. L-Bench: An Android benchmark set for low-power mobile GPUs , 2016, Comput. Graph..
[22] Alejandro Zunino,et al. Battery-aware centralized schedulers for CPU-bound jobs in mobile Grids , 2016, Pervasive Mob. Comput..
[23] Dario Pompili,et al. MobiDiC: Exploiting the untapped potential of mobile distributed computing via approximation , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[24] Francisco Airton Silva,et al. Benchmark applications used in mobile cloud computing research: a systematic mapping study , 2016, The Journal of Supercomputing.
[25] Zhu Wang,et al. Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..
[26] Dario Pompili,et al. Uncertainty-Aware Autonomic Resource Provisioning for Mobile Cloud Computing , 2015, IEEE Transactions on Parallel and Distributed Systems.
[27] Behrouz Shahgholi Ghahfarokhi,et al. Context-aware multi-objective resource allocation in mobile cloud , 2015, Comput. Electr. Eng..
[28] Sajal K. Das,et al. Incentive Mechanisms for Participatory Sensing , 2015, ACM Trans. Sens. Networks.
[29] Kin K. Leung,et al. A Survey of Incentive Mechanisms for Participatory Sensing , 2015, IEEE Communications Surveys & Tutorials.
[30] René Mayrhofer,et al. Mobile Device Usage Characteristics: The Effect of Context and Form Factor on Locked and Unlocked Usage , 2014, MoMM.
[31] Ciprian Dobre,et al. Intelligent services for Big Data science , 2014, Future Gener. Comput. Syst..
[32] Ke Ding,et al. Application Scheduling in Mobile Cloud Computing with Load Balancing , 2013, J. Appl. Math..
[33] Ning Ding,et al. Characterizing and modeling the impact of wireless signal strength on smartphone battery drain , 2013, SIGMETRICS '13.
[34] Oliver P. Waldhorst,et al. Energy-aware resource sharing with mobile devices , 2011, 2011 Eighth International Conference on Wireless On-Demand Network Systems and Services.
[35] Jakob Engblom,et al. The worst-case execution-time problem—overview of methods and survey of tools , 2008, TECS.
[36] Raj Jain,et al. A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.