A metric and framework for measuring information utility in mission-oriented networks

Abstract

In recent years, utility-based resource allocation for wired and wireless networks has been extensively studied. A key goal of utility-based analysis is to provide evaluation criteria for an efficient network operation based on subjective user assessments such as usefulness and value of data. Yet, the vast majority of prior work has focused on topics like mathematical functions (concave or otherwise) of network metrics, information entropy, or user-perceived quality (e.g., MOS) for interpreting utility. In this work, 1 1 Prepared through the PILSNER program. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. we propose an alternative mission-oriented metric for information utility, called the Mean Cognition Score (MCS), that is based on how useful the data is for a mission, in terms of attributes such as accuracy and timeliness with which a task is completed using data. Like previous work, our definition supports the design and engineering of networks by mapping utility metrics to typical network design metrics (bandwidth, delay, loss, etc.). Unlike previous work, it also permits simple solutions to important questions such as the joint utility or usefulness of different data streams, the impact on the utility of one data stream by another, and cross-sensory utility of multimedia streams (e.g., the impact of a side audio channel on an image processing task, or a side audio channel on a video processing task). We present a novel experimental approach to the quantification of MCS, and provide empirical results for the four most important types of data in tactical networks, viz., audio, video, image and situational awareness data. The results quantify the effects of information encoding and the impairments incurred during transmission through imperfect networks on the information’s usefulness to end-users in terms of being able to complete tasks correctly and on time. Furthermore, we develop functional models based on the empirical results for calculating the MCS-based information utility of audio and video streams both independently and jointly, and discuss the applications of such models.