Two-level data aggregation for WMSNs employing a novel VBEAO and HOSVD

Abstract

Recent growth in camera/video technology, alongside the development of visual data aggregation and transmission algorithms, has facilitated a new kind of WSN called the Wireless Multimedia Sensor Network (WMSN). In traditional WSNs, environmental parameters such as temperature, humidity and pressure are analyzed with scalar sensor nodes, which compress redundant data using simple data aggregation functions to reduce transmission energy before sending the data to the receiver. However, this mechanism may not be suitable for image processing and image transmission in WMSNs. This is because camera sensor nodes handle vast quanta of redundant data, particularly if the Field of View (FoV) of camera sensor nodes overlaps with each other to increase monitoring accuracy. In a traditional WSN cluster-based protocol, much of the process relies on a cluster head (CH), which may run out of energy to become a dead node, eventually leading to network failure. To handle this scenario, a Distributed Two-Layer Cluster (DTLC) framework is proposed to minimize the energy consumption of individual nodes in WMSN by sharing the processes performed by a CH. This framework also extends the overall network lifetime by distributing the computational load among camera sensor nodes.A Two-Level Image Aggregation (TLIA) algorithm is proposed to remove the inter-view correlation in the DTLC-based node deployment. The local cluster head (LCH) performs first-level image aggregation (FLIA), which eliminates the correlated information of multi-view images from local cluster members (LCM) by combining multi-view images together, using a novel Varying Bit Encoding based on Arithmetic Operations (VBEAO) with bit reduction. The FLIA results in a compression ratio of 2.4. The master cluster head (MCH) performs second-level image aggregation (SLIA) to further eliminate redundant data from LCHs using a Higher-Order SVD (HOSVD), followed by logarithmic quantization on both the decomposed tensor core and factor matrices. SLIA results in the next level of compression, with a ratio of about 8.9. A comparison of the performance with existing approaches demonstrates the superiority of WMSNs, with improvements in terms of network lifetime and energy consumption.