  Internet of Things (IoT) is a technology paradigm where millions of sensors
monitor, and help inform or manage, physical, envi- ronmental and human systems
in real-time. The inherent closed-loop re- sponsiveness and decision making of
IoT applications makes them ideal candidates for using low latency and scalable
stream processing plat- forms. Distributed Stream Processing Systems (DSPS) are
becoming es- sential components of any IoT stack, but the efficacy and
performance of contemporary DSPS have not been rigorously studied for IoT data
streams and applications. Here, we develop a benchmark suite and per- formance
metrics to evaluate DSPS for streaming IoT applications. The benchmark includes
13 common IoT tasks classified across various func- tional categories and
forming micro-benchmarks, and two IoT applica- tions for statistical
summarization and predictive analytics that leverage various dataflow
compositional features of DSPS. These are coupled with stream workloads sourced
from real IoT observations from smart cities. We validate the IoT benchmark for
the popular Apache Storm DSPS, and present empirical results.
