  Decades of continuous scaling has reduced the energy of unit computing to
virtually zero, while energy-efficient communication has remained the primary
bottleneck in achieving fully energy-autonomous IoT nodes. This paper presents
and analyzes the trade-offs between the energies required for communication and
computation in a wireless sensor network, deployed in a mesh architecture over
a 2400-acre university campus, and is targeted towards multi-sensor measurement
of temperature, humidity and water nitrate concentration for smart agriculture.
Several scenarios involving In-Sensor-Analytics (ISA), Collaborative
Intelligence (CI) and Context-Aware-Switching (CAS) of the cluster-head during
CI has been considered. A real-time co-optimization algorithm has been
developed for minimizing the energy consumption in the network, hence
maximizing the overall battery lifetime of individual nodes. Measurement
results show that the proposed ISA consumes ~467X lower energy as compared to
traditional Bluetooth Low Energy (BLE) communication, and ~69,500X lower energy
as compared with Long Range (LoRa) communication. When the ISA is implemented
in conjunction with LoRa, the lifetime of the node increases from a mere 4.3
hours to 66.6 days with a 230 mAh coin cell battery, while preserving more than
98% of the total information. The CI and CAS algorithms help in extending the
worst-case node lifetime by an additional 50%, thereby exhibiting an overall
network lifetime of ~104 days, which is >90% of the theoretical limits as posed
by the leakage currents present in the system, while effectively transferring
information sampled every second. A web-based monitoring system was developed
to archive the measured data in a continuous manner, and to report anomalies in
the measured data.
