An energy-efficient buffer management scheme based on data integrity and multivariate data reduction for wireless sensor networks

Abstract

Buffer management and data reduction in wireless sensor networks (WSNs) are critical wherever buffer overflow and number of transmissions cause power waste and data loss. To improve energy consumption, this paper presents an Energy-Efficient Buffer Management based on data ntegrity and multivariate data reduction (EEBM-IMR) scheme. To save the buffer space, EEBM-IMR classifies the data measured by sensors, based on its integrity, into \textit{malicious} or \textit{verified} packets. To reduce data transmissions, a multivariate data reduction scheme is introduced based on a binary tree data structure. The efficiency of EEBM-IMR is evaluated in terms of transmission ratio, dropping probability and throughput using a real-time world dataset of fifty sensors. The experimental results show that, as the number of sensors increases, EEBM-IMR saves energy and outperforms some existing models in previous studies.