Enhanced hybrid positioning in wireless networks I: AoA-ToA

Localization in wireless networks presents enormous challenges for scientists and engineers. Some of the most commonly used techniques for localization are based on time of arrival (ToA), received signal strength (RSS) and angle of arrival (AoA) of the signals. In this paper we analyze and propose improvements to the location accuracy of hybrid (AoA-ToA) localization systems. The location coordinates are obtained using a linear least squares (LLS) algorithm. A closed form expression for the mean square error (MSE) of the LLS estimator is derived. Furthermore, the information present in the covariance of the incoming signals is utilized and a novel weighted linear least squares (WLLS) method is proposed. It is shown via simulation that the theoretical MSE accurately predicts the performance of the LLS estimator. It is also shown via simulation that the WLLS algorithm exhibits better performance than the LLS algorithm

Optimized hybrid localisation with cooperation in wireless sensor networks

In this study, the authors introduce a novel hybrid cooperative localisation scheme when both distance and angle measurements are available. Two linear least squares (LLS) hybrid cooperative schemes based on angle of arrival–time of arrival (AoA–ToA) and AoA–received signal strength (AoA–RSS) signals are proposed. The proposed algorithms are modified to accommodate cooperative localisation in resource constrained networks where only distance measurements are available between target sensors (TSs) while both distance and angle measurements are available between reference sensors and TSs. Furthermore, an optimised version of the LLS estimator is proposed to further enhance the localisation performance. Moreover, localisation of sensor nodes in networks with limited connectivity (partially connected networks) is also investigated. Finally, computational complexity analysis of the proposed algorithms is presented. Through simulation, the superior performance of the proposed algorithms over its non-cooperative counterpart and the hybrid signal based iterative non-linear least squares algorithms is demonstrated

Cooperative positioning using angle of arrival and time of arrival

Localization has been one of the most highly researched topics in wireless communications in the past decade. Localization of wireless nodes can be achieved using a variety of techniques, in which range measurement and angle measurement are most commonly used. In the presence of both angle and range measurement, a hybrid model can be developed. In this paper we analyze a hybrid angle of arrival-time of arrival (AoA-ToA) model for localization of wireless nodes, the model is modified to remove the bias from the estimated positions. We also explore the idea of cooperative localization using both angle and range measurements and develop a linear least squares (LLS) scheme. It is shown via simulation that the modified model is unbiased and that the performance of the proposed cooperative LLS is superior to its non-cooperative counterpart

Enhanced hybrid positioning in wireless networks II: AoA-RSS

In order to achieve higher location estimation accuracy through utilizing all the available information, in this paper we propose a hybrid localization system. We use the angle of arrival (AoA) measurement with the inherent received signal strength (RSS) information to develop an AoA-RSS linear least squares (LLS) location estimator. To accurately predict the performance of the LLS estimator, a closed form expression for the mean square error (MSE) is also derived. Furthermore, the information present in the covariance of the incoming signals is utilized and a novel weighted linear least squares (WLLS) method is proposed. It is shown via simulation that the theoretical MSE accurately predicts the performance of the LLS estimator. It is also shown via simulation that the WLLS algorithm exhibits better performance than the LLS algorithm

Tracking of wireless mobile nodes in he presence of unknown path-loss characteristics

Due to the difficult characterization of the propagation model, most studies on racking of mobile nodes assume the correct knowledge of the power-distance gradients or the path-loss exponents (PLEs). In this paper, we first investigate the impact of erroneous PLEs on positioning of a wireless nodes when both distance and bearing measurements are available. Thus, an analytical expression of the mean square error (MSE) in location estimation is derived in case of erroneous PLEs. Second, we propose a novel online PLE estimation and tracking algorithm in dynamic environments. The proposed algorithm estimates the PLE of individual links a every time-step using he generalized pattern search (GenPS) algorithm. The PLE estimates update the observation vector which is used in a Kalman filter (KF) and a particle filter (PF) for tracking. Simulation results show that the racking performance degrades drastically with an incorrect assumption for the PLE values. Further simulations show that tracking with PLE estimation performs considerably beer compared to tracking with incorrectly assumed PLEs

On the Positioning of Sensors with Simultaneous Bearing and Range Measurement in Wireless Sensor Networks

Hybrid range and bearing based approach towards active localization of beacons will be widely celebrated in the near future, due to the protocols used for data transmission through targeted beam of radiation in 5G networks. This technique, which is one of the building blocks of 5G infrastructure does not only allow extremely high data rates but will also allow the estimation of direction of arrival/departure of the signal. Thus, in this paper a hybrid angle/range based approach towards positioning is under focus. A linear least squares approach will be applied to the unbiased version of hybrid direction of arrival-time of flight (DoA-ToF) measurement model. Thus, the unbiasing constant is first calculated followed by the theoretical mean squares expression calculation, to be utilized for selecting only those reference beacons that guarantee an improvement in the accuracy of the least squares approach. A critical distance expression is also derived that determines the relationship between the noise variance of angle and range estimates in terms of the distance between nodes. Furthermore, a weighted least squares solution is presented which exploits the noise covariance matrix of the hybrid measurement model. Finally, the weighted solution is bounded by the linear Cramér-Rao bound (LCRB) for the hybrid signal model

Neuroinflammation and structural injury of the fetal ovine brain following intra-amniotic Candida albicans exposure.

BackgroundIntra-amniotic Candida albicans (C. Albicans) infection is associated with preterm birth and high morbidity and mortality rates. Survivors are prone to adverse neurodevelopmental outcomes. The mechanisms leading to these adverse neonatal brain outcomes remain largely unknown. To better understand the mechanisms underlying C. albicans-induced fetal brain injury, we studied immunological responses and structural changes of the fetal brain in a well-established translational ovine model of intra-amniotic C. albicans infection. In addition, we tested whether these potential adverse outcomes of the fetal brain were improved in utero by antifungal treatment with fluconazole.MethodsPregnant ewes received an intra-amniotic injection of 10(7) colony-forming units C. albicans or saline (controls) at 3 or 5 days before preterm delivery at 0.8 of gestation (term ~ 150 days). Fetal intra-amniotic/intra-peritoneal injections of fluconazole or saline (controls) were administered 2 days after C. albicans exposure. Post mortem analyses for fungal burden, peripheral immune activation, neuroinflammation, and white matter/neuronal injury were performed to determine the effects of intra-amniotic C. albicans and fluconazole treatment.ResultsIntra-amniotic exposure to C. albicans caused a severe systemic inflammatory response, illustrated by a robust increase of plasma interleukin-6 concentrations. Cerebrospinal fluid cultures were positive for C. albicans in the majority of the 3-day C. albicans-exposed animals whereas no positive cultures were present in the 5-day C. albicans-exposed and fluconazole-treated animals. Although C. albicans was not detected in the brain parenchyma, a neuroinflammatory response in the hippocampus and white matter was seen which was characterized by increased microglial and astrocyte activation. These neuroinflammatory changes were accompanied by structural white matter injury. Intra-amniotic fluconazole reduced fetal mortality but did not attenuate neuroinflammation and white matter injury.ConclusionsIntra-amniotic C. albicans exposure provoked acute systemic and neuroinflammatory responses with concomitant white matter injury. Fluconazole treatment prevented systemic inflammation without attenuating cerebral inflammation and injury

A major genetic locus in <i>Trypanosoma brucei</i> is a determinant of host pathology

The progression and variation of pathology during infections can be due to components from both host or pathogen, and/or the interaction between them. The influence of host genetic variation on disease pathology during infections with trypanosomes has been well studied in recent years, but the role of parasite genetic variation has not been extensively studied. We have shown that there is parasite strain-specific variation in the level of splenomegaly and hepatomegaly in infected mice and used a forward genetic approach to identify the parasite loci that determine this variation. This approach allowed us to dissect and identify the parasite loci that determine the complex phenotypes induced by infection. Using the available trypanosome genetic map, a major quantitative trait locus (QTL) was identified on T. brucei chromosome 3 (LOD = 7.2) that accounted for approximately two thirds of the variance observed in each of two correlated phenotypes, splenomegaly and hepatomegaly, in the infected mice (named &lt;i&gt;TbOrg1&lt;/i&gt;). In addition, a second locus was identified that contributed to splenomegaly, hepatomegaly and reticulocytosis (&lt;i&gt;TbOrg2&lt;/i&gt;). This is the first use of quantitative trait locus mapping in a diploid protozoan and shows that there are trypanosome genes that directly contribute to the progression of pathology during infections and, therefore, that parasite genetic variation can be a critical factor in disease outcome. The identification of parasite loci is a first step towards identifying the genes that are responsible for these important traits and shows the power of genetic analysis as a tool for dissecting complex quantitative phenotypic traits