User-Driven Adaptive Sampling for Massive Internet of Things

Abstract

Energy conservation techniques are crucial to achieving high reliability in the Internet of Things (IoT) services, especially in the Massive IoT (MIoT), which stringently requires cost-effective and low-energy consumption for battery-powered devices. Most of the proposed techniques generally assume that data acquiring and processing consume significantly lower than that of communication. Unfortunately, this assumption is incorrect in the MIoT scenario, which mostly involves the low-power wide-area network (LPWAN) and complex data sensing operations (e.g., biological and seismic sensing) using “power-hungry” sensors (e.g., gas sensors, seismometers). Thus, sensing actions may consume even more energy than transmission. In addition, none of them support end-users in controlling the trade-off between energy conservation and data precision. To deal with these issues, we propose an adaptive sampling algorithm that estimates the optimal sampling frequencies in real-time for IoT devices based on the changes of collected data. Given a user's saving desire, our algorithm could minimize the device's energy consumption while ensuring the precision of collected information. Practical experiments over IoT datasets have shown that our algorithm can reduce the number of acquired samples up to 20 times compared with a traditional fixed-rate approach at extremely low Normal Mean Error value around 3.45%.

Keywords

• Constrained devices
• energy efficiency
• massive internet of things
• sampling algorithm