A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

Abstract

The gradual increase in latency-sensitive, real-time applications for embedded systems encourages users to share sensor data simultaneously. Streamed sensor data have deficient performance. In this paper, we propose a new edge-based scheduling method with high-bandwidth for decreasing driver-profiling latency. The proposed multi-level memory scheduling method places data in a key-value storage, flushes sensor data when the edge memory is full, and reduces the number of I/O operations, network latency, and the number of REST API calls in the edge cloud. As a result, the proposed method provides significant read/write performance enhancement for real-time embedded systems. In fact, the proposed application improves the number of requests per second by 3.5, 5, and 4 times, respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions. The proposed application also improves the bandwidth by 5.89, 5.58, and 4.16 times respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions.