A Real-Time Massive Data Processing Technique for Densely Distributed Sensor Networks

International audienceToday, we are awash in a flood of data coming from different data generating sources. Wireless sensor networks (WSNs) are one of big data contributors where data is being collected at unprecedented scale. Unfortunately, much of this data is of no interest, meaningless and redundant. Hence, data reduction an is becoming fundamental operation in order to decrease the communication costs and enhance data mining in WSNs. In this work, we propose a two-level data reduction approach for sensor networks. The first level operated by the sensor nodes consists on compressing collected data while using the Pearson coefficient. The second level is executed at intermediate nodes (e.g. aggregators, cluster heads, etc.). The objective of the second level is to eliminate redundant data generated by neighboring nodes using two adapted clustering methods: EKmeans and TopK. Through both simulations and real experiments on real telosB sensors, we show the relevance of our approach in terms of minimizing the big data collected in WSNs and enhancing network lifetime, compared to other existing techniques

Data redundancy reduction for energy-efficiency in wireless sensor networks: a comprehensive review

Wireless Sensor Networks (WSNs) play a significant role in providing an extraordinary infrastructure for monitoring environmental variations such as climate change, volcanoes, and other natural disasters. In a hostile environment, sensors' energy is one of the crucial concerns in collecting and analyzing accurate data. However, various environmental conditions, short-distance adjacent devices, and extreme usage of resources, i.e., battery power in WSNs, lead to a high possibility of redundant data. Accordingly, the reduction in redundant data is required for both resources and accurate information. In this context, this paper presents a comprehensive review of the existing energy-efficient data redundancy reduction schemes with their benefits and limitations for WSNs. The entire concept of data redundancy reduction is classified into three levels, which are node, cluster head, and sink. Additionally, this paper highlights existing key issues and challenges and suggested future work in reducing data redundancy for future research