Optimizing Situational Awareness in Disaster Response Networks

In a wireless disaster response network, how does the quality of experience (QoE) of the user affect the level of situational awareness (SA)? Is maximum QoE necessary for high SA? In this empirical study, we propose a novel measurement approach to quantify SA based on the QoE of the user. The relationship between QoE and network quality of service (QoS) metrics such as delay and packet loss is well known. Therefore, quantifying the SA-QoE-QoS relationship will help the network operator to ensure a high level of SA when the network is under load, through parsimonious allocation of network resources such as spectrum and power. We first define an objective expression for SA in four contexts: surroundings awareness, target awareness, location awareness, and responsiveness (SETLR model). Using empirical data gathered from real-world experiments, we mathematically formulate SA as a function of QoE and show that this relationship is logistic in nature. An important observation is that maximum QoE is not necessary to ensure high SA; a mean opinion score of just 2-3 is necessary in our scenario. Next, we show through simulations that this new mathematical model of SA can be instantiated in a long-term evolution radio access network and efficiently used in network optimization techniques to avoid over-provisioning of resources.