Localization in wireless sensor networks

In this thesis we examine localization in wireless sensor networks starting with a brief overview of the basics of radiolocation techniques and then look at some of the most well-known commercial positioning techniques and localization algorithms. We then concentrate on the application of the Fastmap (FM) algorithm in the field of wireless sensor localization. Our first contribution in this thesis is the mathematical analysis of the FM algorithm in terms of the mean squared error (MSE) of the coordinate estimate under a multiplicative noise model followed by the optimum placement of anchor nodes. The algorithm is compared to Linear Least Squares (LLS) algorithm, which is well known and has a similar complexity to that of FM. Another contribution is proposing the angle-projected FM algorithm for wireless sensor nodes localization in order to enhance the connectivity of the network and the overall performance. A comprehensive study and mathematical analysis in terms of the MSE for this algorithm is presented and it is also compared with the original FM algorithm. We also propose a weighted Fastmap (WFM) algorithm in which more than one pair of anchor nodes is used to evaluate the first coordinate (i.e., x-coordinate) of the unknown nodes in order to reduce the effect of error dependency in the y-coordinate estimation. (In the original FM algorithm only one pair of anchor nodes was employed.) The optimal WFM weights are determined via (constrained) minimization of the MSE of the estimated node coordinates. A simplification of the WFM is also introduced, called the averaged FM (AFM), where complexity is reduced at the expense of degradation in the overall WFM performance. Both the WFM and AFM exhibit improved performance over the original FM algorithm. Finally, an unbiased version of the WFM, AFM and FM is presented in which an estimate of the bias term is removed to improve the overall MSE performance. The effect of this modification on the algorithms' performance is then analysed and discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality