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Range-based localisation and tracking methods use the time-of-arrival (TOA) between the mobile station and several base stations, but the multipath propagation of non-line-of-sight channels complicates the estimation and processing. For channel modelling, the Gaussian scatterer distribution model has been reported to have a reasonable match between its TOA probability density distribution (PDF) and measured TOA data. In this study, this TOA PDF is adapted, along with selection from multiple motion models of the mobile station, for a new location and tracking algorithm. Since the TOA PDF is non-Gaussian and is a non-linear function of the position of the mobile, particle filtering is used which increases the complexity of the algorithm. The focus is on the tracking performance, and this is evaluated by simulation using idealised statistical channels, allowing direct comparison between different location algorithms. In this context, the presented algorithm is more accurate than the benchmarks of extended Kalman filter tracking, and positioning using least squares.

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.

The localization of underwater sensors is the most crucial task in underwater wireless sensor networks (UWSNs). The sensors, which are situated under the water, sense data from the environment, and sensed data is transmitted to the monitoring station. Although the monitoring station receives the sensed data, the data is meaningless without knowing the exact position of the sensor. Localization is the major issue in UWSN to be resolved. There are several localization algorithms available for terrestrial wireless sensor networks (WSN), but there are comparatively few localization algorithms available for UWSNs. An improved range-based localization method is introduced in this paper to discover localization issue. To evaluate the location of the target sensors, localization error is further to be reduced. The localization error is reduced by applying the whale optimization algorithm (WOA) in this technique. Simulation results demonstrate that performance metrics of the proposed approach outperform the existing work in terms of localization error and localization coverage.

The localization of underwater sensors is the most crucial task in underwater wireless sensor networks (UWSNs). The sensors which are situated under the water sense data from the environment and transmit to the monitoring station. Although the monitoring station receives the data, it is meaningless without knowing the exact position of the sensor. Localization is the major issue in UWSN to be resolved. There are several localization algorithms available for terrestrial wireless sensor networks (WSN) but there are comparatively few localization algorithms are available for UWSNs. An improved range-based localization method is introduced in this article to discover localization issue. To evaluate the location of the target sensors, the localization error is further reduced. The localization error is reduced by applying the Whale Optimization Algorithm (WOA) in this technique. Simulation results demonstrate that performance metrics of the proposed approach outperforms that of the existing work in terms of localization error and localization coverage.

The localization of the nodes in wireless sensor networks is essential in establishing effective communication among different devices connected, within the Internet of Things. This paper proposes a novel method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilizes the signal strength received at the target node to identify its location in the localized grid system. The Most Valuable Player Algorithm is used to solve the localization problem. Initially, the algorithm is implemented on four test cases with a varying number of sensor nodes to display its robustness under different network occupancies. Afterward, the study is extended to incorporate actual readings from both indoor and outdoor environments. The results display higher accuracy in the localization of unknown sensor nodes than previously reported.

Cet article tente d’améliorer la précision de la localisation dans l’environnement des terres agricoles. La précision des algorithmes de localisation basés sur l'indicateur d'intensité du signal reçu (RSSI) dépend de l'environnement de travail. Cependant, de nombreuses perturbations se produisent pendant la propagation du signal sans fil dans le réseau de capteurs sans fil (WSN) des terres agricoles, telles que le déclin, la blindage, la réflexion et la diffusion.Les impacts de ces perturbations varient avec la croissance de la plante. Compte tenu de ces éléments, l'auteur a adopté une méthode de localisation améliorée basée sur RSSI, qui divise la zone cible en plusieurs petits triangles et laisse chaque nœud décider de son triangle local.Ensuite, un exposant de perte de chemin global a été calculé pour l’ensemble de la zone de localisation et des exposants locaux ont également été calculés pour chaque petit triangle. Au moyen d'exemples de vérification, il a été prouvé que l'algorithme proposé avait une bonne précision de localisation et une bonne adaptabilité à l'environnement variable dans les terres agricoles.Les résultats de la recherche fournissent une référence pour l’estimation de la localisation dans les terres agricoles à grande échelle et la modélisation des canaux en temps réel. This paper attempts to improve the localization accuracy under the farmland environment. The accuracy of the localization algorithms based on received signal strength indicator (RSSI) hinges on the working environment. However, there are many disturbances during the wireless signal propagation in farmland wireless sensor network (WSN), such as fading, shielding, reflection and scattering. The impacts of these disturbances vary with the plant growth. Considering these, the author adopted an improved RSSI-based localization method, which divides the target area into multiple small triangles and let each node decides its local triangle. Then, a global path loss exponent was calculated for the entire localization area, and local exponents were also computed for each small triangle. Through example verification, the proposed algorithm was proved to have good localization accuracy and good adaptability to the time-varying environment in farmland. The research findings provide a reference for location estimation in large-scale farmland and real-time channel modeling.

Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.