LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

Andersen, 1995 Hockney, 1996 Varghese, 2021, A survey on edge performance benchmarking, ACM Comput. Surv., 54, 1, 10.1145/3444692 John, 2018 Sánchez Sánchez, 2021, A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets, IEEE Commun. Surv. Tutor., 23, 1048, 10.1109/COMST.2021.3064259 El-Halwagi, 2017 Wyant, 2012, Computing performance benchmarks among CPU, GPU, and FPGA, Computing Dong, 2021, A survey on deep learning and its applications, Comp. Sci. Rev., 40 Xingzhou Zhang, Yifan Wang, Weisong Shi, pCAMP: Performance comparison of machine learning packages on the edges, in: USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18), 2018. Stephan Patrick Baller, Anshul Jindal, Mohak Chadha, Michael Gerndt, DeepEdgeBench: Benchmarking deep neural networks on edge devices, in: 2021 IEEE International Conference on Cloud Engineering (IC2E), 2021, pp. 20–30. Sánchez Sánchez, 2021 Sánchez Sánchez, 2022 Ivan Grasso, Petar Radojkovic, Nikola Rajovic, Isaac Gelado, Alex Ramirez, Energy efficient HPC on embedded SoCs: Optimization techniques for Mali GPU, in: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014, pp. 123–132. Cloutier, 2016, A Raspberry Pi cluster instrumented for fine-grained power measurement, Electronics, 5, 61, 10.3390/electronics5040061 Limaye, 2018, HERMIT: A benchmark suite for the internet of medical things, IEEE Internet Things J., 5, 4212, 10.1109/JIOT.2018.2849859 Anirban Das, Stacy Patterson, Mike Wittie, EdgeBench: Benchmarking edge computing platforms, in: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion, 2018, pp. 175–180. Jonathan McChesney, Nan Wang, Ashish Tanwer, Eyal de Lara, Blesson Varghese, DeFog: Fog computing benchmarks, in: 4th ACM/IEEE Symposium on Edge Computing, 2019, pp. 47–58. Klervie Toczé, Norbert Schmitt, Ulf Kargén, Atakan Aral, Ivona Brandić, Edge Workload Trace Gathering and Analysis for Benchmarking, in: 2022 IEEE 6th International Conference on Fog and Edge Computing, ICFEC, 2022, pp. 34–41. Daniel Hawthorne, Michael Kapralos, Raymond W Blaine, Suzanne J Matthews, Evaluating cryptographic performance of Raspberry Pi clusters, in: 2020 IEEE High Performance Extreme Computing Conference, HPEC, 2020, pp. 1–9. Uluagac, 2014 Hagelskjær, 2020 Basford, 2020, Performance analysis of single board computer clusters, Future Gener. Comput. Syst., 102, 278, 10.1016/j.future.2019.07.040 Su, 2021 Flávio Ramalho, Augusto Neto, Virtualization at the network edge: A performance comparison, in: 2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2016, pp. 1–6. Pincheira, 2022, Benchmarking constrained IoT devices in blockchain-based agri-food traceability applications, 212 Wright, 2019, Benchmarking an 11-qubit quantum computer, Nature Commun., 10, 1, 10.1038/s41467-019-13534-2 Gao, 2021 Baldini, 2020 Wiki, 2021 Idein, 2021 Idein, 2021 Broadcom, 2013 Babun, 2021, CPS device-class identification via behavioral fingerprinting: From theory to practice, IEEE Trans. Inf. Forensics Secur., 16, 2413, 10.1109/TIFS.2021.3054968 Feitelson, 2015 Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek, A general framework for increasing the robustness of PCA-based correlation clustering algorithms, in: International Conference on Scientific and Statistical Database Management, 2008, pp. 418–435. Arunan Sivanathan, Hassan Habibi Gharakheili, Vijay Sivaraman, Inferring IoT device types from network behavior using unsupervised clustering, in: 2019 IEEE 44th Conference on Local Computer Networks, LCN, 2019, pp. 230–233. Liu, 2018, An intrusion detection method for internet of things based on suppressed fuzzy clustering, EURASIP J. Wireless Commun. Networking, 2018, 1, 10.1186/s13638-018-1128-z Granato, 2018, Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective, Trends Food Sci. Technol., 72, 83, 10.1016/j.tifs.2017.12.006 Sivanathan, 2018, Classifying IoT devices in smart environments using network traffic characteristics, IEEE Trans. Mob. Comput., 18, 1745, 10.1109/TMC.2018.2866249 Cvitić, 2021, Ensemble machine learning approach for classification of IoT devices in smart home, Int. J. Mach. Learn. Cybern., 12, 3179, 10.1007/s13042-020-01241-0 Soofi, 2017, Classification techniques in machine learning: applications and issues, J. Basic Appl. Sci., 13, 459, 10.6000/1927-5129.2017.13.76 Grandini, 2020 Sánchez Sánchez, 2021 Dimitris Gizopoulos, George Papadimitriou, Athanasios Chatzidimitriou, Vijay Janapa Reddi, Behzad Salami, Osman S Unsal, Adrian Cristal Kestelman, Jingwen Leng, Modern hardware margins: CPUs, GPUs, FPGAs recent system-level studies, in: 2019 IEEE 25th International Symposium on on-Line Testing and Robust System Design, IOLTS, 2019, pp. 129–134. Harris, 2016 Developers, 2021 Developers, 2021