Addressing the reactiveness problem in sensor networks using rich task representation

Sensor networks are increasingly important in many domains, for example, environmental monitoring, emergency response, and military operations. There is a great interest in making these networks more flexible, so they can be more easily deployed to meet the needs of new tasks. The research problem is lack of reactiveness of a system utilising a sensor network in a dynamic real-time domain, where the state of sensors and tasks might change many times (e.g. due to a sensor malfunction, or a change in task requirements or priorities). In such domains (e.g. firefighting or the military) we want to minimise the time spent manually configuring the sensor network, as any delay dramatically endangers the outcome of a task or a delay’s effects might be unacceptable, e.g. the loss of a human life. The current way of deploying sensors in the problem context involves four consecutive steps: Direction, Collection, Processing and Dissemination (DCPD). These steps form a cycle, called the DCPD loop. Automating this loop as much as possible would be a big step towards solving the reactiveness problem. Service-Oriented Sensor Networks (SOSN), allow sensors to be discovered, accessed, and combined with other information-processing services, thus enabling an efficient sensor exploitation. They are only a partial solution to the problem, as they don’t employ explicit representations of a user’s information-requiring tasks. Therefore, a machine processable expression of a user’s task (task representation, TR), allowing automation of the DCPD steps, is needed. We showed that, currently, there is no TR that can completely automate the loop, but that we can create such a hybrid of current TRs (called HTR) that automates the loop more than the individual TRs. Our literature review revealed four TRs. Using the identified TRs, we formed three high level designs of task representations. None of them covered the loop completely thus by enrichment of one of the built HTRs with the missing concepts, we finally obtained one that covers the DCPD loop fully. We tested the four hybrids in a simulation run for four scenarios with distinctive likelihoods of change of task and platform states. It showed that significant benefits are gained just by reusing existing technologies and that the reactiveness problem can be effectively tackled by that approach, particularly visible in the emergency response scenario, characterised by low task and high platform changeability