Context-Aware Collaborative-Intelligence with Spatio-Temporal In-Sensor-Analytics in a Large-Area IoT Testbed
user-61447a76e55422cecdaf7d19(2020)
摘要
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
worst-case node lifetime by an additional 50
network lifetime of 104 days, which is >90
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.
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关键词
context-aware,collaborative-intelligence,spatio-temporal,in-sensor-analytics,large-area
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