Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

Abstract

Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS2 (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS2/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS2/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services.