Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

Abstract

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.

A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks

“…Lu et al proposed a cluster structure based on JPEG2000, in which various roles were assigned to different nodes; the two hop cluster structure was adopted to transmit images [18]. Based on the role division of network nodes, Heng et al adopted a multihop, layered routing scheme, which balances the energy consumption of each node, to transmit images [19]. Bejaoui et al proposed a cluster based routing protocol from the perspective of network congestion, considering maximum cluster head utilization; the possible congestion in and the energy consumption of the network can be reduced, and the number of cluster heads can be minimized by balancing the number of nodes in each cluster [20].…”

“…If these tasks are concentrated on a single camera node, the used storage and processing pressure are increased, leading to instantaneous energy depletion. Through distributed image compression, the energy consumption of the network can be balanced, and the life cycle of the network can be extended [18][19][20]. However, because the networks still use the limited battery for power sources, if the depleted battery is not replaced, the network will deplete soon.…”

Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

“…e availability of low-cost small-sized cameras attached to wearable sensors and portable imaging devices has opened up a wide range of imaging-oriented applications, including assisted living, smart healthcare, traffic monitoring, virtual sports experiences, and posture recognition [1][2][3][4][5][6][7][8][9][10][11][12]. An interconnection of visual sensor nodes (sensor nodes with attached camera) is known as visual sensor network (VSN) [13,14] or as wireless multimedia sensor network (WMSN) [15,16]. Wearable visual sensors may also be a part of the Internet of things (IoT) [17][18][19][20][21].…”

Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

“…The distributed source coding (DSC) algorithms [ 42 , 43 , 44 , 45 , 46 ] are other promising approaches that can be used to reduce the redundant data in multiview VSN scenarios. Each DSC encoder operates independently, but, at the same time, relies on joint decoding operations at the sink (remote monitoring station).…”