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

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

Process evaluation of integrated early child development care at private clinics in poor urban Pakistan: a mixed methods study

Background: In poor urban Pakistan, private GP clinics lack adequate services to promote early child development (ECD) care. A clinic-based contextualised ECD intervention was developed for quarterly tool-assisted counselling of mothers. Aim: To explore the experience and implementation of ECD intervention by the private care providers and clients, for further adaptation for scaling of quality ECD care, at primary level private healthcare facilities in Pakistan. Design & setting: A mixed methods approach using quantitative records review and qualitative interviews at poor urban clinics in Rawalpindi and Lahore, Pakistan. Method: Quantitative data from study-specific records were reviewed for 1242 mother–child pairs registered in the intervention. A total of 18 semi-structured interviews with clinic staff, mothers, and research staff were conducted at four clinics. The interviews were audiorecorded and transcribed verbatim. Results: District Health Office (DHO) support allowed transparent and effective selection and training of clinic providers. Public endorsement of ECD care at private clinics and the addition of community advocates promoted ECD care uptake. Clinic settings were found feasible for clinic assistants, and acceptable to mothers, for counselling sessions. Mothers found ECD counselling methods more engaging compared to the usual care provided. Conclusion: In poor urban settings where public health care is scarce, minimal programme investment on staff training and provision of minor equipment can engage private clinics effectively in delivering ECD care

Clinical outcome data of children treated with cannabis-based medicinal products for treatment resistant epilepsy - analysis from the UK medical cannabis registry.

BACKGROUND:  There is a paucity of high-quality evidence of the efficacy and safety of cannabis-based medicinal products in treatment of treatment-resistant epilepsy (TRE) in children. METHODS:  A case series of children (<18 years old) with TRE from the UK Medical Cannabis Registry was analyzed. Primary outcomes were ≥50% reduction in seizure frequency, changes in the Impact of Pediatric Epilepsy Score (IPES), and incidence of adverse events. RESULTS:  Thirty-five patients were included in the analysis. Patients were prescribed during their treatment with the following: CBD isolate oils (n = 19), CBD broad-spectrum oils (n = 17), and CBD/Δ9-THC combination therapy (n = 17). Twenty-three (65.7%) patients achieved a ≥50% reduction in seizure frequency. 94.1% (n = 16) of patients treated with CBD and Δ9-THC observed a ≥50% reduction in seizure frequency compared to 31.6% (n = 6) and 17.6% (n = 3) of patients treated with CBD isolates and broad-spectrum CBD products, respectively (p< 0.001). Twenty-six (74.3%) adverse events were reported by 16 patients (45.7%). The majority of these were mild (n = 12; 34.2%) and moderate (n = 10; 28.6%). CONCLUSION:  The results of this study demonstrate a positive signal of improved seizure frequency in children treated with Cannabis-based medicinal products (CBMPs) for TRE. Moreover, the results suggest that CBMPs are well-tolerated in the short term. The limitations mean causation cannot be determined in this open-label, case series

Selective Modulation of α5 GABAA Receptors Exacerbates Aberrant Inhibition at Key Hippocampal Neuronal Circuits in APP Mouse Model of Alzheimer’s Disease

Selective negative allosteric modulators (NAMs), targeting α5 subunit-containing GABAA receptors (GABAARs) as potential therapeutic targets for disorders associated with cognitive deficits, including Alzheimer’s disease (AD), continually fail clinical trials. We investigated whether this was due to the change in the expression of α5 GABAARs, consequently altering synaptic function during AD pathogenesis. Using medicinal chemistry and computational modeling, we developed aqueous soluble hybrids of 6,6-dimethyl-3-(2-hydroxyethyl) thio-1-(thiazol-2-yl)-6,7-dihydro-2-benzothiophene-4(5H)-one, that demonstrated selective binding and high negative allosteric modulation, specifically for the α5 GABAAR subtypes in constructed HEK293 stable cell-lines. Using a knock-in mouse model of AD (APPNL−F/NL−F), which expresses a mutant form of human amyloid-β (Aβ), we performed immunofluorescence studies combined with electrophysiological whole-cell recordings to investigate the effects of our key molecule, α5-SOP002 in the hippocampal CA1 region. In aged APPNL−F/NL−F mice, selective preservation of α5 GABAARs was observed in, calretinin- (CR), cholecystokinin- (CCK), somatostatin- (SST) expressing interneurons, and pyramidal cells. Previously, we reported that CR dis-inhibitory interneurons, specialized in regulating other interneurons displayed abnormally high levels of synaptic inhibition in the APPNL−F/NL−F mouse model, here we show that this excessive inhibition was “normalized” to control values with bath-applied α5-SOP002 (1 μM). However, α5-SOP002, further impaired inhibition onto CCK and pyramidal cells that were already largely compromised by exhibiting a deficit of inhibition in the AD model. In summary, using a multi-disciplinary approach, we show that exposure to α5 GABAAR NAMs may further compromise aberrant synapses in AD. We, therefore, suggest that the α5 GABAAR is not a suitable therapeutic target for the treatment of AD or other cognitive deficits due to the widespread neuronal-networks that use α5 GABAARs

An observational study of safety and clinical outcome measures across patient groups in the United Kingdom Medical Cannabis Registry.

BACKGROUND: There is a paucity of high-quality data on patient outcomes and safety after initiating treatment with cannabis-based medicinal products (CBMPs). The aim of this study was to assess the clinical outcomes and safety of CBMPs by analyzing patient-reported outcome measures and adverse events across a broad spectrum of chronic conditions. RESEARCH DESIGN AND METHODS: This study analyzed patients enrolled in the UK Medical Cannabis Registry. Participants completed the EQ-5D-5L to assess health-related quality of life, Generalized Anxiety Disorder-7 (GAD-7) questionnaire to measure anxiety severity, and the Single-item Sleep Quality Scale (SQS) to rate sleep quality at baseline and follow-up after 1, 3, 6, and 12 months. RESULTS: A total of 2833 participants met inclusion criteria. The EQ-5D-5L index value, GAD-7, and SQS all improved at each follow-up (p  0.050). Adverse events were reported by 474 (16.73%) participants. CONCLUSIONS: This study suggests that CBMPs are associated with an improvement in health-related quality of life in UK patients with chronic diseases. Treatment was tolerated well by most participants, but adverse events were more common in female and cannabis-naïve patients