Quality of information with minimum requirements for emergency communications

In emergency situations like the recent Australian bushfires, it is crucial for civilians and firefighters to receive critical information such as available escape routes with guarantees on accuracy, timeliness, reliability, and completeness. Mobile Ad-hoc Networks (MANETs) can provide communications in bushfires but guaranteeing information delivery that meets user needs is not easy with current MANET forwarding solutions. Quality of Information (QoI) based source selection has recently been developed in MANETs for this purpose. The most popular QoI scheme uses the analytic hierarchy process (AHP), an intensive pairwise comparison procedure to score sources using a linear combination of low-level network metrics in a hierarchical two-step process. Current QoI-AHP, by directing traffic to a single high scoring source, often creates additional bottlenecks and does not guarantee the higher-level information needs of users. In this work, we develop a novel low overhead source selection scheme, QoI with Thresholds (QoIT), that is designed to deliver the required target performance thresholds for users. QoIT introduces two modifications to QoI-AHP. Firstly, it replaces the raw network performance value by the ratio of the network metric value to the user’s demand for that performance, a goodness measure of how far the value of the network metric is from meeting the user’s demand. The ratios are fed into QoIT’s process for assigning multi-dimensional scores to each source. Secondly, QoIT uses new source selection criteria to choose the source that best delivers the information that meets the user needs. To evaluate QoIT and QoI-AHP performances in emergency communications, we develop a novel simulation software with realistic models across multiple network layers. Our QoIT algorithm works with any combination of quality thresholds for any subset of network metrics. Simulation results show that QoIT out-performs QoI-AHP by increasing user usability by 57%, ensuring high priority requirements are met, reducing source bottlenecks, and decreasing the source selection time by 62.5%.