Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed

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 Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs 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 toward multisensor 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. Measurement results show that the proposed ISA consumes approximate to 467x lower energy as compared to traditional Bluetooth low energy (BLE) communication, and approximate to 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 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% 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 approximate to 104 days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.

Automated summary

The article discusses the tradeoffs between energy consumption for communication and computation in a wireless sensor network deployed for smart agriculture applications. It introduces a real-time co-optimization algorithm to minimize energy consumption and maximize battery lifetime, with measurement results showing significant energy savings compared to traditional communication methods. Collaborative intelligence and context-aware switching algorithms further extend node lifetime, achieving over 90% of the theoretical limits while effectively transferring sampled information.