Localization and fault detection in wireless sensor networks. (Localisation et détection de fautes dans les réseaux de capteurs sans fil)

In this thesis three themes related to wireless sensor networks (WSNs) are covered. The first one concerns the power loss in a node signal due to voltage droop in the battery of the node. In the first part of the thesis a method is proposed to compensate for the apparent increase in the calculated distance between the related nodes due to decrease in the energy of the signal sending node battery. A function is proposed whose arguments are the apparently observed RSS and the current voltage of the emitter node battery. The return of the function is the corrected RSS that corresponds to the actual distance amongst the connected nodes. Hence increasing the efficiency of the RSS based localization methods in WSNs. In the second part of the thesis a position estimation method for localization of nodes in a WSN is proposed. In the proposed localization algorithm anchor nodes are used as landmark points. The localization method proposed here does not require any constraint on the placement of the anchors; rather any three randomly chosen nodes can serve as anchors. A heuristic approach is used to find the relative topology with the help of distance matrix. The purpose of the distance matrix is to indicate whether or not a pair of nodes has a connection between them and in case of connectivity it gives the estimated distance between the nodes. By using the information of connectivity between the nodes and their respective distances the topology of the nodes is calculated. This method is heuristic because it uses the point solution from the intersection of two circles instead of conventional triangulation method, where a system of three quadratic equations in two variables is used whereby the computational complexity of the position estimation method is increased. When two connected nodes have another node in common, then by using the information of distances between these interconnected nodes, two possible positions are calculated for the third node. The presence or absence of a connection between the third node and a fourth node helps in finding the accurate possibility out of the two. This process is iterated till all the nodes have been relatively placed. Once the relative topology has been calculated, we need to find the exact symmetry, orientation, and position of this topology in the plane. It is at this moment the knowledge of three nodes positions comes into action. From the relative topology we know the temporary coordinates of the nodes. By having a comparison of certain characteristics between the temporary coordinates and the exact coordinates; first the symmetry of relative topology is obtained that would correspond to the original topology. In other words it tells whether or not the relative topology is a mirror image of the original topology. Some geometrical operators are used to correct the topology position and orientation. Thus, all the nodes in the WSN are localized using exactly three anchors. The last part of the thesis focuses on the detection of faults in a WSN. There is always a possibility that a sensor of a node is not giving accurate measurements all of the time. Therefore, it is necessary to find if a node has developed a faulty sensor. With the precise information about the sensor health, one can determine the extent of reliance on its sensor measurement. To equip a node with multiple sensors is not an economical solution. Thus the sensor measurements of a node are modeled with the help of the fuzzy inference system (FIS). For each node, both recurrent and non-recurrent systems are used to model its sensor measurement. An FIS for a particular node is trained with input variables as the actual sensor measurements of the neighbor nodes and with output variable as the real sensor measurements of that node. The difference between the FIS approximated value and the actual measurement of the sensor is used as an indication for whether or not to declare a node as faulty