A combined inductive and deductive sense data extraction and visualisation service

Wireless sensor networks (WSNs) have an intimate interaction, via sensors, with the physical environment they operate within. Application domains have a significant effect on applications performance because WSNs are usually deployed to perform application specific tasks. The part of the world with which an application is concerned is defined as that application's domain. The application domain may help scientists to leverage computational power to simulate, visualise, manipulate, predict and gain intuition about monitored phenomenon. In this paper we propose a new visualisation framework, called Multi-Dimensional Application Domain-driven (M-DAD), that elevates the capabilities of the sense data extraction and visualisation mapping service proposed in [1]. M- DAD exploits the application domain to dynamically minimise the mapping service predictive error. It is capable of visualising an arbitrary number of sense modalities. In M-DAD the visualisation performance is improved by utilising the relations between independent sense modalities as well as other parameters of the application domain. M-DAD can meet the goal of reliability and reactivity, and demonstrates satisfactory robustness using the information they collect about the environment they operate within to adapt its behaviour to changes in the environment. Self-adaptation is a fundamental capability of M-DAD which is required to operate in dynamic environments that impose varying functional and performance requirements on WSNs applications. This self-adaptation scheme makes M-DAD more resilient to faults by substituting for faulty nodes, auto-calibrate sensors, and recover form modelling errors. The experimental results demonstrate that M-DAD performs as well or better than mapping services without its extended capabilities.