  Timely processing has been increasingly required on smart IoT devices, which
leads to directly implementing information processing tasks on an IoT device
for bandwidth savings and privacy assurance. Particularly, monitoring and
tracking the observed signals in continuous form are common tasks for a variety
of near real-time processing IoT devices, such as in smart homes, body-area and
environmental sensing applications. However, these systems are likely low-cost
resource-constrained embedded systems, equipped with compact memory space,
whereby the ability to store the full information state of continuous signals
is limited. Hence, in this paper, we develop solutions of efficient timely
processing embedded systems for online classification and tracking of
continuous signals with compact memory space. Particularly, we focus on the
application of smart plugs that are capable of timely classification of
appliance types and tracking of appliance behavior in a standalone manner. We
implemented a smart plug prototype using low-cost Arduino platform with small
amount of memory space to demonstrate the following timely processing
operations: (1) learning and classifying the patterns associated with the
continuous power consumption signals, and (2) tracking the occurrences of
signal patterns using small local memory space. Furthermore, our system designs
are also sufficiently generic for timely monitoring and tracking applications
in other resource-constrained IoT devices.
