The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols. Also, they allow data analytics (e.g., anomaly detection and trend prediction) directly on symbols, benefiting large classes of edge applications. However, existing SR algorithms are centralized in design and work offline with batch data, which is infeasible for real-time cases. We propose SymED - Symbolic Edge Data representation method, i.e., an online, adaptive, and distributed approach for symbolic representation of data on edge. SymED is based on the Adaptive Brownian Bridge-based Aggregation (ABBA), where we assume low-powered IoT devices do initial data compression (senders) and the more robust edge devices do the symbolic conversion (receivers). We evaluate SymED by measuring compression performance, reconstruction accuracy through Dynamic Time Warping (DTW) distance, and computational latency. The results show that SymED is able to (i) reduce the raw data with an average compression rate of 9.5%; (ii) keep a low reconstruction error of 13.25 in the DTW space; (iii) simultaneously provide real-time adaptability for online streaming IoT data at typical latencies of 42ms per symbol, reducing the overall network traffic.

翻译：边缘计算范式有助于在靠近数据源处处理物联网（IoT）生成的数据。在资源受限的边缘设备上传输、存储和处理快速增长的数据量面临挑战。符号化表示（SR）算法通过将实际原始数据转换为符号来减小数据规模，是一种有前景的解决方案。同时，它们允许直接对符号进行数据分析（例如异常检测和趋势预测），从而惠及大规模边缘应用。然而，现有SR算法在设计上是集中式的，且需离线处理批量数据，这在实时场景中不可行。我们提出SymED——符号化边缘数据表示方法，这是一种在线、自适应且分布式的边缘数据符号化方法。SymED基于自适应布朗桥聚合（ABBA），其中假设低功耗物联网设备执行初始数据压缩（发送端），而更强健的边缘设备执行符号转换（接收端）。我们通过压缩性能、基于动态时间规整（DTW）距离的重建精度以及计算延迟来评估SymED。结果表明，SymED能够：（i）以平均压缩率9.5%压缩原始数据；（ii）在DTW空间中保持13.25的低重建误差；（iii）同时为在线流式物联网数据提供实时自适应性，典型延迟为每符号42毫秒，从而降低整体网络流量。