Plausibility check and energy management in a semi-autonomous sensor network using a model-based approach

The present dissertation carries out both energy management and model-based fault detection while using wireless sensor networks (WSNs). It deals with an application of a WSN which uses scattered sensor nodes inside a closed space container to monitor environmental variables, temperature and relative humidity. Since the environmental system under discussion is non-linear, multivariable and time variant, a hybrid mathematical model is extracted. A novel approach to simplify the hybrid model and decouple the monitoring variables is introduced for the first time in this research. This outstanding idea, so-called Floating Input Approach (FIA) exploits system identification as well as the properties of a distributed measurement systems to simplify the modeling task. It performs a Multi Input-Single Output (MISO) linear dynamic model and estimates environmental variables on a desired sensor node as output by using actual measured variables from surrounding sensor nodes as inputs. Developing both on-line and off-line model identifications based on the FIA, model-based fault detection and energy saving of the wireless sensor network without performance degradation is successfully achieved. The FIA-based techniques detect and discriminate different fault types in sensors and system under discussion. Moreover, in the basis of the proposed mathematical dynamic model, an effective technique is introduced to enlarge life time of the sensor nodes. A combinational fault detection and energy management is introduced at the end.Benefits of the addressed techniques are verified using simulations and implementations on a progressive platform of WSN (Imote2). They can also be developed simply for a wide variety of applications in the future.