Learning Automata-Based Node Scheduling Algorithm in Multimedia Sensor Networks

In the application of wireless multimedia sensor networks (WMSNs), wireless multimedia sensor (WMS) nodes generate a massive amount of multimedia data, such as images, audio, and video. However, this results in a significant amount of redundant multimedia data that requires enormous energy resources for processing and communication. Since, randomly deployed nodes in the network have limited energy resources, they suffer from a short network lifetime due to unnecessary energy consumption required for processing and communicating the redundant data. In this paper, we propose a learning automata-based node scheduling (LANS) algorithm based on a learning automata (LA) technique to address the short network lifetime issue of multimedia sensor networks. This learning-based scheduling algorithm resides inside each node and tries to learn the optimal scheduling strategy to conserve energy resources. In this paper, we also propose a subroutine called redundancy measurement subalgorithm (RMS) that proposed algorithm calls during its learning phase to find the redundancy level of the image data. The main objective of the proposed algorithm is to enable each node to learn its optimal action based on the redundancy level and the energy level, so that the node with highly redundant image data and low energy levels switches to the sleep state to save energy. To show the performance of the proposed algorithm, our work is authenticated by results demonstrating its efficiency in scheduling nodes. Hence, it is found that the proposed scheduling algorithm achieves an increment of 50.2% in network lifetime and 27.8% decrement in average energy consumption compared to the state-of-the-art algorithms.