Energy-aware data compression and transmission range control for energy-harvesting wireless sensor networks

Abstract

Energy-harvesting nodes are now being employed in wireless sensor networks to extend the lifetime of the network by harvesting energy from the surrounding environments. However, unpremeditated energy consumption can incur energy problems, such as the blackout of nodes (due to their exceeding energy consumption over the amount of harvested energy) or inevitable disposal of harvested energy (in excess of the battery capacity). In this article, we propose an adaptive data compression and transmission range extension scheme that minimizes the blackout of sensor nodes and increases the amount of data collected at the sink node using the harvested energy efficiently. In this scheme, each node estimates the amount of harvested and consumed energy. When it determines that its remaining energy will exceed its storage capacity, it exploits the energy to compress the data or increase the transmission range. At this point, of the two methods, the method that can more effectively increase the network performance can be selected. The results of experiments conducted indicate that the proposed scheme significantly reduces the extent of node blackouts and increases the data collection rate of the sink node.

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

“…Ma et al [ 6 ] used a distributed video codec scheme to enhance the processing power of a single node for traditional data compression. Yi et al [ 7 ] proposed an adaptive data compression and transmission range extension scheme to improve the data collection rate of sink nodes. Hameed et al [ 8 ] used lossless compression technology and Huffman coding encryption technology to provide effective means for remote monitoring security and compressibility of electrocardiography (ECG) data.…”