  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.
