Data gathering and anomaly detection in wireless sensors networks

Abstract : The use of Wireless Sensor Networks (WSN)s is steadily increasing to cover various applications and domains. This trend is supported by the technical advancements in sensor manufacturing process which allow a considerable reduction in the cost and size of these components. However, there are several challenges facing the deployment and the good functioning of this type of networks. Indeed, WSN's applications have to deal with the limited energy, memory and processing capacities of sensor nodes as well as the imperfection of the probed data. This dissertation addresses the problem of collecting data and detecting anomalies in WSNs. The aforementioned functionality needs to be achieved while ensuring a reliable data quality at the collector node, a good anomaly detection accuracy, a low false alarm rate as well as an efficient energy consumption solution. Throughout this work, we provide different solutions that allow to meet these requirements. Foremost, we propose a Compressive Sensing (CS) based solution that allows to equilibrate the traffic carried by nodes regardless their distance from the sink. This solution promotes a larger lifespan of the WSN since it balances the energy consumption between sensor nodes. Our approach differs from existing CS-based solutions by taking into account the sparsity of sensory representation in the temporal domain in addition to the spatial dimension. Moreover, we propose a new formulation to detect aberrant readings. The simulations carried on real datasets prove the efficiency of our approach in terms of data recovering and anomaly detection compared to existing solutions. Aiming to further optimize the use of WSN resources, we propose in our second contribution a Matrix Completion (MC) based data gathering and anomaly detection solution where an arbitrary subset of nodes contributes at the data gathering process at each operating period. To fill the missing values, we mainly relay on the low rank structure of sensory data as well as the sparsity of readings in some transform domain. The developed algorithm also allows to dissemble anomalies from the normal data structure. This solution is enhanced in our third contribution where we propose a constrained formulation of the data gathering and anomalies detection problem. We reformulate the textit{a prior} knowledge about the target data as hard convex constraints. Thus, the involved parameters into the developed algorithm become easy to adjust since they are related to some physical properties of the treated data. Both MC based approaches are tested on real datasets and demonstrate good capabilities in terms of data reconstruction quality and anomaly detection performance. Finally, we propose in the last contribution a position based compressive data gathering scheme where nodes cooperate to compute and transmit only the relevant positions of their sensory sparse representation. This technique provide an efficient tool to deal with the noisy nature of WSN environment as well as detecting spikes in the sensory data. Furthermore, we validate the efficiency of our solution by a theoretical analysis and corroborate it by a simulation evaluation
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Mohamed Ali Moussa. Data gathering and anomaly detection in wireless sensors networks. Signal and Image processing. Université Paris-Est, 2017. English. ⟨NNT : 2017PESC1082⟩. ⟨tel-01936285⟩

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