Scheduling data transmissions of underwater sensor nodes for maximizing value of information

We consider an underwater wireless sensor network where baseline communication happens over acoustic, multi-hop routes from the underwater nodes to an on-shore station. The data collected by the nodes greatly exceeds the baseline communication capability. At best, the nodes can transmit digests of their full observations. In order for the sink to receive all sensed data, an autonomous underwater vehicle (AUV) is sent to each node for collecting data over short-distance, high data rate optical connections. The AUV then offloads all collected information to the terrestrial station via wireless communication when it surfaces. The observations made by the nodes vary in size and urgency. The information they provide has an associated value. Given a path of the AUV, we design scheduling strategies for the nodes to decide when and how much information (i.e., which digest) to transmit via acoustic routes so that the value of information reaching the terrestrial station is maximized. These strategies are compared via simulations on realistic scenarios. Our results show that scheduling algorithms that are able to locally estimate the value of information of a data digest provide the delivery of data with a significantly higher value of information. In contrast, uninformed algorithms, i.e., strategies that do not consider the value of information at the node level, provide only a marginal increase over the benchmark case of using only the AUV for data collection.