Relevance-based partial reliability in wireless sensor networks

Wireless sensor networks can provide valuable information for a lot of measurement, tracking, surveillance, automation, and general-purpose monitoring applications. Information as humidity, temperature, pressure, infrared images, and noises will be sensed and packetized for distribution over the network, but corruption during transmission may compromise the accuracy of the retrieved information and even put people in danger. In fact, the sensed data may have different relevancies for the applications, altering the impact of packet corruptions. We propose a relevance-based partially reliable transmission approach to provide data delivery with different reliability guaranties, exploiting the relevancies of transmitted data when choosing the proper error recovery service. In this context, some error scenarios are investigated, considering different configurations of error bursts. We expect that the proposed partially reliable transmission mechanism can save energy over the network while assuring acceptable quality for sensing monitoring.