Accurate indoor positioning of narrowband Internet-of-Things (IoT) sensors has drawn more attention in recent years. The introduction of Bluetooth Low Energy (BLE) technology is one of the latest developments of IoT and especially applicable for Ultra-Low Power (ULP) applications. BLE is an attractive technology for indoor positioning systems because of its low-cost deployment and reasonable accuracy. Efficient indoor positioning can be achieved by deducing the sensor position from the estimated signal Angle-of-Arrival (AoA) at multiple anchors. An anchor is a base station of known position and equipped with a narrowband multi-antenna array. However, the design and implementation of indoor positioning systems based on AoA measurements involve multiple challenges. The first part of this thesis mainly addresses the impact of hardware impairments on the accuracy of AoA measurements. In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to array calibration errors coming from hardware imperfections. A detailed experimental implementation is performed using a Software Defined Radio (SDR) platform to precisely evaluate the accuracy of AoA measurements. For this purpose, a new Over-the-Air (OTA) calibration method is proposed and the array calibration error is investigated. The experimental results are compared with the theoretical analysis. These results show that array calibration errors can cause some degrees of uncertainty in AoA estimation. Moreover, we propose iterative positioning algorithms based on AoA measurements for low capacity IoT sensors with high accuracy and fair computational complexity. Efficient positioning accuracy is obtained by iterating between the angle and position estimation steps. We first develop a Data-Aided Maximum a Posteriori (DA- MAP) estimator based on the preamble of the transmitted signal. DA-MAP estimator relies on the knowledge of the transmitted signal which makes it impractical for narrowband communications where the preamble is short. For this reason, a Non-Data- Aided Maximum a Posteriori (NDA-MAP) estimator is developed to improve the AoA accuracy. The iterative positioning algorithms are therefore classified as Data-Aided Iterative (DA-It) and Non-Data-Aided Iterative (NDA-It) depending on the knowledge of the transmitted signal that is used for estimation. Both numerical and experimental analyses are carried out to evaluate the performance of the proposed algorithms. The results show that DA-MAP and NDA-MAP estimators are more accurate than MUSIC. The results also show that DA-It comes very close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal, known as Direct Position Estimation (DPE). Furthermore, the NDA-It algorithm significantly outperforms the DA-It because it can use a much higher number of samples; however, it needs more iterations to converge. In addition, we evaluate the computational savings achieved by the iterative schemes compared to DPE through a detailed complexity analysis. Finally, we investigate the performance degradation of the proposed iterative algorithms due to the impact of multipath and NLOS propagation in indoor environments. Therefore, we develop an enhanced iterative positioning algorithm with an anchor selection method in order to identify and exclude NLOS anchors. The numerical results show that applying the anchor selection strategy significantly improves the positioning accuracy in indoor environments. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Various applications of Wireless Sensor Networks (WSNs) require accurate localization of sensor nodes. The quantity and locations of anchor nodes, which serve as reference points for distance estimates, as well as the localization process itself, affect the localization accuracy. Furthermore, because numerous communications are sent between nodes for localization, energy consumption must be considered. This work presents an energy-aware and accurate localization method. It is based on a combined anchor deployment and energy-aware localization. The proper number and distribution of anchors have been investigated to achieve full network coverage and connectivity based on an efficient and heterogeneous hexagonal deployment. Later, energy-aware localization is performed in three stages: Initialization, signal acquisition, and anchor selection. The initialization step allows the network to be adaptable to sudden changes by establishing anchor connectivity and creating the neighbors' list. Meanwhile, the Received Signal Strength Indicator (RSSI) is used for distance measurements between nodes, with the implementation of a Kalman filter to reduce signal attenuation and noise. Later, the anchor selection is done using fuzzy logic with inference parameters: RSSI, node density, and residual energy. This step ensures that only operable anchors engage in localization, while anchors with inadequate energy sources remain intact to ensure their future availability.:1 Introduction 2 Theoretical background 3 Energy-aware outdoor deployment and localization 4 Proposed anchor deployment method 5 Proposed energy-aware localization method 6 Experimental validation of the proposed localization method / Verschiedene Anwendungen von drahtlosen Sensornetzwerken (WSNs) erfordern eine genaue Lokalisierung von Sensorknoten. Die Anzahl und Standorte der Ankerknoten, die als Referenzpunkte für Entfernungsschätzungen dienen, sowie der Lokalisierungsprozess selbst beeinflussen die Lokalisierungsgenauigkeit. Da für die Lokalisierung zahlreiche Nachrichten zwischen den Knoten gesendet werden, muss außerdem der Energieverbrauch berücksichtigt werden. In dieser Arbeit wird eine energiebewusste und genaue Lokalisierungsmethode vorgestellt. Sie basiert auf einer Kombination aus effizienter Ankerknotennutzung und energiebewusster Lokalisierung. Die richtige Anzahl und Verteilung von Ankern wurde untersucht, um eine vollständige Netzabdeckung und Konnektivität auf der Grundlage einer effizienten und heterogenen hexagonalen Verteilung zu erreichen. Später wird die energiebewusste Lokalisierung in drei Stufen durchgeführt: Initialisierung, Signalerfassung und Ankerauswahl. Der Initialisierungsschritt ermöglicht es dem Netzwerk, sich an plötzliche Veränderungen anzupassen, indem es die Verbindung zu den Ankern und die Liste der Nachbarn erstellt. Zunächst wird der Received Signal Strength Indicator (RSSI) für die Entfernungsmessung zwischen den Knoten verwendet, wobei ein Kalman-Filter implementiert wird, um Signalabschwächung und Rauschen zu reduzieren. Später erfolgt die Ankerauswahl mit Hilfe von Fuzzy-Logik und Inferenzparametern: RSSI, Knotendichte und Restenergie. Dieser Schritt stellt sicher, dass nur funktionsfähige Anker an der Lokalisierung teilnehmen, während Anker mit unzureichenden Energiequellen intakt bleiben, um ihre zukünftige Verfügbarkeit zu gewährleisten.:1 Introduction 2 Theoretical background 3 Energy-aware outdoor deployment and localization 4 Proposed anchor deployment method 5 Proposed energy-aware localization method 6 Experimental validation of the proposed localization method