Benchmarking of localization solutions : guidelines for the selection of evaluation points

Indoor localization solutions are key enablers for next-generation indoor navigation and track and tracing solutions. As a result, an increasing number of different localization algorithms have been proposed and evaluated in scientific literature. However, many of these publications do not accurately substantiate the used evaluation methods. In particular, many authors utilize a different number of evaluation points, but they do not (i) analyze if the number of used evaluation points is sufficient to accurately evaluate the performance of their solutions and (ii) report on the uncertainty of the published results. To remedy this, this paper evaluates the influence of the selection of evaluation points. Based on statistical parameters such as the standard error of the mean value, an estimator is defined that can be used to quantitatively analyze the impact of the number of used evaluation points on the confidence interval of the mean value of the obtained results. This estimator is used to estimate the uncertainty of the presented accuracy results, and can be used to identify if more evaluations are required. To validate the proposed estimator, two different localization algorithms are evaluated in different testbeds and using different types of technology, showing that the number of required evaluation points does indeed vary significantly depending on the evaluated solution. (C) 2017 Elsevier B.V. All rights reserved

A hybrid indoor localization solution using a generic architectural framework for sparse distributed wireless sensor networks

Indoor localization and navigation using wireless sensor networks is still a big challenge if expensive sensor nodes are not involved. Previous research has shown that in a sparse distributed sensor network the error distance is way too high. Even room accuracy can not be guaranteed. In this paper, an easy-to-use generic positioning framework is proposed, which allows users to plug in a single or multiple positioning algorithms. We illustrate the usability of the framework by discussing a new hybrid positioning solution. The combination of a weighted (range-based) and proximity (range-free) algorithm is made. Roth solutions separately have an average error distance of 13.5m and 2.5m respectively. The latter result is quite accurate due to the fact that our testbeds are not sparse distributed. Our hybrid algorithm has an average error distance of 2.66m only using a selected set of nodes, simulating a sparse distributed sensor network. All our experiments have been executed in the iMinds testbed: namely at "de Zuiderpoort". These algorithms are also deployed in two real-life environments: "De Vooruit" and "De Vijvers"

Depletion of RIPK3 or MLKL blocks TNF-driven necroptosis and switches towards a delayed RIPK1 kinase-dependent apoptosis

In human cells, the RIPK1-RIPK3-MLKL-PGAM5-Drp1 axis drives tumor necrosis factor (TNF)-induced necroptosis through mitochondrial fission, but whether this pathway is conserved among mammals is not known. To answer this question, we analyzed the presence and functionality of the reported necroptotic axis in mice. As in humans, knockdown of receptorinteracting kinase-3 (RIPK3) or mixed lineage kinase domain like (MLKL) blocks TNF-induced necroptosis in L929 fibrosarcoma cells. However, repression of either of these proteins did not protect the cells from death, but instead induced a switch from TNF-induced necroptosis to receptor-interacting kinase-1 (RIPK1) kinase-dependent apoptosis. In addition, although mitochondrial fission also occurs during TNF-induced necroptosis in L929 cells, we found that knockdown of phosphoglycerate mutase 5 (PGAM5) and dynamin 1 like protein (Drp1) did not markedly protect the cells from TNF-induced necroptosis. Depletion of Pink1, a reported interactor of both PGAM5 and Drp1, did not affect TNF-induced necroptosis. These results indicate that in these murine cells mitochondrial fission and Pink1 dependent processes, including Pink-Parkin dependent mitophagy, apparently do not promote necroptosis. Our data demonstrate that the core components of the necrosome (RIPK1, RIPK3 and MLKL) are crucial to induce TNF-dependent necroptosis both in human and in mouse cells, but the associated mechanisms may differ between the two species or cell types