A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN

Reliability and Efficiency Analysis of Distributed Source Coding in Wireless Sensor Networks

The amount of measurements, that can be successfully gathered in a tree based wireless sensor networks (WSNs) employing distributed source coding (DSC) algorithms, is accurately evaluated in this paper in the presence of different coding topologies and packet aggregation schemes (PA). The system model includes a realistic network architecture with multi-hop communication, automatic repeat request protocol (ARQ), packet losses due to channel impairments and collisions. We specifically consider four topologies for DSC and three alternatives for PA. The analysis is carried out by first computing the packet loss probability; then we evaluate the reliability in terms of loss factor and the efficiency in terms of average energy consumption of the network. This theoretical framework is exploited to perform a comprehensive numerical evaluation of loss factor and energy consumption in various scenarios of aggregation and coding topologies, provided the correlation profile of the measurements