  In the ever-growing Internet of Things (IoT) landscape, smart power
management algorithms combined with energy harvesting solutions are crucial to
obtain self-sustainability. This paper presents an energy-aware adaptive
sampling rate algorithm designed for embedded deployment in
resource-constrained, battery-powered IoT devices. The algorithm, based on a
finite state machine (FSM) and inspired by Transmission Control Protocol (TCP)
Reno's additive increase and multiplicative decrease, maximizes sensor sampling
rates, ensuring power self-sustainability without risking battery depletion.
Moreover, we characterized our solar cell with data acquired over 48 days and
used the model created to obtain energy data from an open-source world-wide
dataset. To validate our approach, we introduce the EcoTrack device, a
versatile device with global navigation satellite system (GNSS) capabilities
and Long-Term Evolution Machine Type Communication (LTE-M) connectivity,
supporting MQTT protocol for cloud data relay. This multi-purpose device can be
used, for instance, as a health and safety wearable, remote hazard monitoring
system, or as a global asset tracker. The results, validated on data from three
different European cities, show that the proposed algorithm enables
self-sustainability while maximizing sampled locations per day. In experiments
conducted with a 3000 mAh battery capacity, the algorithm consistently
maintained a minimum of 24 localizations per day and achieved peaks of up to
3000.
