In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil's coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is a part of the open-source AIBench project, publicly available from http: //www.benchcouncil.org/AIBench/index.html. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues.

Two different methods for extracting fractionated rice bran protein (FRBP) from defatted rice bran were investigated according to the solubility of protein in different extraction solvents. The yields of the obtained proteins and their purity were first compared. Sodium dodecyl sulfate polyacrylamide gel electrophoresis, differential scanning calorimetry, protein surface hydrophobicity, and protein secondary molecular structure analyses were subsequently applied to identify and compare the compositional, structural, and functional characteristics of the obtained proteins. The highest yield (13.8%, w/w) and purity (45–47%) of FRBP products were obtained using 0.4 M NaCl, 80% ethanol, and 0.01 M NaOH as extraction solvents to fractionate albumin, globulin, prolamin, and glutelin. Several good properties were exhibited, including good functionality, specific denaturation temperature, and enthalpy values, for FRBP products prepared by the above method.

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