A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

This dissertation addresses the target localization problem in wireless sensor networks (WSNs). WSNs is now a widely applicable technology which can have numerous practical applications and offer the possibility to improve people’s lives. A required feature to many functions of a WSN, is the ability to indicate where the data reported by each sensor was measured. For this reason, locating each sensor node in a WSN is an essential issue that should be considered. In this dissertation, a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) is presented. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. The measurements are then used to derive a convex estimator, based on second-order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. The results obtained in this dissertation confirm the novel algorithms’ performance and accuracy. Additionally, a change to the algorithms is proposed, allowing the study of a more realistic and challenging scenario where different probabilities of communication failure between neighbor nodes at the broadcast phase are considered. Computational simulations performed in the scope of this dissertation, show that the algorithms’ performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

3-D Hybrid Localization with RSS/AoA in Wireless Sensor Networks: Centralized Approach

This dissertation addresses one of the most important issues present in Wireless Sensor Networks (WSNs), which is the sensor’s localization problem in non-cooperative and cooperative 3-D WSNs, for both cases of known and unknown source transmit power PT . The localization of sensor nodes in a network is essential data. There exists a large number of applications for WSNs and the fact that sensors are robust, low cost and do not require maintenance, makes these types of networks an optimal asset to study or manage harsh and remote environments. The main objective of these networks is to collect different types of data such as temperature, humidity, or any other data type, depending on the intended application. The knowledge of the sensors’ locations is a key feature for many applications; knowing where the data originates from, allows to take particular type of actions that are suitable for each case. To face this localization problem a hybrid system fusing distance and angle measurements is employed. The measurements are assumed to be collected through received signal strength indicator and from antennas, extracting the received signal strength (RSS) and angle of arrival (AoA) information. For non-cooperativeWSN, it resorts to these measurements models and, following the least squares (LS) criteria, a non-convex estimator is developed. Next, it is shown that by following the square range (SR) approach, the estimator can be transformed into a general trust region subproblem (GTRS) framework. For cooperative WSN it resorts also to the measurement models mentioned above and it is shown that the estimator can be converted into a convex problem using semidefinite programming (SDP) relaxation techniques.It is also shown that the proposed estimators have a straightforward generalization from the known PT case to the unknown PT case. This generalization is done by making use of the maximum likelihood (ML) estimator to compute the value of the PT . The results obtained from simulations demonstrate a good estimation accuracy, thus validating the exceptional performance of the considered approaches for this hybrid localization system

Collaborative Sensor Network Localization: Algorithms and Practical Issues

Emerging communication network applications including fifth-generation (5G) cellular and the Internet-of-Things (IoT) will almost certainly require location information at as many network nodes as possible. Given the energy requirements and lack of indoor coverage of Global Positioning System (GPS), collaborative localization appears to be a powerful tool for such networks. In this paper, we survey the state of the art in collaborative localization with an eye toward 5G cellular and IoT applications. In particular, we discuss theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques

Optimised Localisation in Wireless Sensor Networks

Wireless sensor networks (WSNs) comprise of tens, hundreds or thousands of low powered, low cost wireless nodes, capable of sensing environmental data such as humidity and temperature. Other than these sensing abilities, these nodes are also able to locate themselves. Different techniques can be found in literature to localise wireless nodes in WSNs. These localisation algorithms are based on the distance estimates between the nodes, the angle estimates between the nodes or hybrid schemes. In the context of range based algorithms, two prime techniques based on the time of arrival (ToA) and the received signal strength (RSS) are commonly used. On the other hand, angle based approach is based on the angle of arrival (AoA) of the signal. A hybrid approach is sometimes used to localise wireless nodes. Hybrid algorithms are more accurate than range and angle based algorithms because of additional observations. Modern WSNs consist of a small group of highly resourced wireless nodes with known locations called anchor nodes (ANs) and a large group of low resourced wireless nodes known as the target nodes (TNs). The ANs can locate themselves through GPS or they may have a predetermined location given to them during network deployment. Based on these known locations and the range/angle estimates, the TNs are localised. Since hybrid algorithms (a combination of RSS, ToA and AoA) are more accurate than other algorithms, a major portion of this thesis will focus on these approaches. Two prime hybrid signal models are discussed: i) The AoA-RSS hybrid model and ii) the AoA-ToA hybrid signal model. A hybrid AoA-ToA model is first studied and is further improved by making the model unbiased and by developing a new weighted linear least squares algorithm for AoA-ToA signal (WLLS-AoA-ToA) that capitalise on the covariance matrix of the incoming signal. A similar approach is taken in deriving a WLLS algorithm for AoA-RSS signal (WLLS-AoA-RSS). Moreover expressions of theoretical mean square error (MSE) of the location estimate for both signal models are derived. Performances of both signal models are further improved by designing an optimum anchor selection (OAS) criterion for AoA-ToA signal model and a two step optimum anchor selection (TSOAS) criterion for AoA-RSS signal model. To bound the performance of WLLS algorithms linear Cramer Rao bounds (LCRB) are derived for both models, which will be referred to as LCRB-AoA-ToA and LCRB-AoA-RSS, for AoA-ToA and AoA-RSS signal models, respectively. These hybrid localisation schemes are taken one step further and a cooperative version of these algorithms (LLS-Coop) is designed. The cooperation between the TNs significantly improves the accuracy of final estimates. However this comes at a cost that not only the ANs but the TNs must also be able to estimate AoA and ToA/RSS simultaneously. Thus another version of the same cooperative model is designed (LLS-Coop-X) which eliminates the necessity of simultaneous angle-range estimation by TNs. A third version of cooperative model is also proposed (LLS-Opt-Coop) that capitalises the covariance matrix of incoming signal for performance improvement. Moreover complexity analysis is done for all three versions of the cooperative schemes and is compared with its non cooperative counterparts. In order to extract the distance estimate from the RSS the correct knowledge of path-loss exponent (PLE) is required. In most of the studies this PLE is assumed to be accurately known, also the same and fixed PLE value is used for all communication links. This is an oversimplification of real conditions. Thus error analysis of location estimates with incorrect PLE assumptions for LLS technique is done in their respective chapters. Moreover a mobile TN and an unknown PLE vector is considered which is changing continuously due to the motion of TN. Thus the PLE vector is first estimated using the generalized pattern search (GenPS) followed by the tracking of TN via the Kalman filter (KF) and the particle filter (PF). The performance comparison in terms of root mean square error (RMSE) is also done for KF, extended Kalman filter (EKF) and PF