Geographic routing resilient to location errors

Geographic routing is an attractive option for large scale wireless sensor networks (WSNs) because of its low overhead and energy expenditure, but is inefficient in realistic localization conditions. Positioning systems are inevitably imprecise because of inexact range measurements and location errors lead to poor performance of geographic routing in terms of packet delivery ratio (PDR) and energy efficiency. This paper proposes a novel, low-complexity, error-resilient geographic routing method, named conditioned mean square error ratio (CMSER) routing, intended to efficiently make use of existing network information and to successfully route packets when localization is inaccurate. Next hop selection is based on the largest distance to destination (minimizing the number of forwarding hops) and on the smallest estimated error figure associated with the measured neighbor coordinates. It is found that CMSER outperforms other basic greedy forwarding techniques employed by algorithms such as most forward within range (MFR), maximum expectation progress (MEP) and least expected distance (LED). Simulation results show that the throughput for CMSER is higher than for other methods, additionally it also reduces the energy wasted on lost packets by keeping their routing paths short

Transductive Learning with String Kernels for Cross-Domain Text Classification

For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting. Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as native language identification or automatic essay scoring. Moreover, classifiers based on string kernels have been found to be robust to the distribution gap between different domains. In this paper, we formally describe an algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings. By adapting string kernels to the test set without using the ground-truth test labels, we report significantly better accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap with arXiv:1808.0840

Energy efficient geographic routing robust against location errors

Realistic geographic routing algorithms need to ensure quality of services in wireless sensor network applications while being resilient to the inherent localization errors of positioning algorithms. A number of solutions robust against location errors have been proposed in the literature and their design focuses either on a high throughput or on a balanced energy consumption. Ideally, both aspects need to be addressed by the same algorithm, but in most cases, the proposed routing techniques compromise between the two. The present work aims to minimize such a tradeoff and to facilitate a higher packet delivery ratio than similar geographic routing techniques, while still being energy efficient. This is achieved through a novel proposal entitled energy conditioned mean square error algorithm (ECMSE), which makes use of statistical assumptions of Gaussianly distributed location error and Ricianly distributed distances between sensor nodes. In addition, it makes use of an energy efficient feature, which includes information about the energy cost of the forwarding decision. By using a location-error-resilient and distance-based power metric, the ECMSE provides an improved performance in realistic simulations in comparison with other error-coping geographic routing algorithms

The case for retraining of ML models for IoT device identification at the edge

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, using resources available at the edge of the network. In this paper, we compare the accuracy of five different machine learning models (tree-based and neural network-based) for identifying IoT devices by using packet trace data from a large IoT test-bed, showing that all models need to be updated over time to avoid significant degradation in accuracy. In order to effectively update the models, we find that it is necessary to use data gathered from the deployment environment, e.g., the household. We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment. We show that updating neural network-based models at the edge is feasible, as they require low computational and memory resources and their structure is amenable to being updated. Our results show that it is possible to achieve device identification and categorization with over 80% and 90% accuracy respectively at the edge

A formalized general theory of syntax with bindings

We present the formalization of a theory of syntax with bindings that has been developed and refined over the last decade to support several large formalization efforts. Terms are defined for an arbitrary number of constructors of varying numbers of inputs, quotiented to alpha-equivalence and sorted according to a binding signature. The theory includes a rich collection of properties of the standard operators on terms, such as substitution and freshness. It also includes induction and recursion principles and support for semantic interpretation, all tailored for smooth interaction with the bindings and the standard operators

Galaxy And Mass Assembly (GAMA): the wavelength dependence of galaxy structure versus redshift and luminosity

We study how the sizes and radial profiles of galaxies vary with wavelength, by fitting Sersic functions simultaneously to imaging in nine optical and near-infrared bands. To quantify the wavelength dependence of effective radius we use the ratio, $\mathcal{R}$, of measurements in two restframe bands. The dependence of Sersic index on wavelength, $\mathcal{N}$, is computed correspondingly. Vulcani et al. (2014) have demonstrated that different galaxy populations present sharply contrasting behaviour in terms of $\mathcal{R}$ and $\mathcal{N}$. Here we study the luminosity dependence of this result. We find that at higher luminosities, early-type galaxies display a more substantial decrease in effective radius with wavelength, whereas late-types present a more pronounced increase in Sersic index. The structural contrast between types thus increases with luminosity. By considering samples at different redshifts, we demonstrate that lower data quality reduces the apparent difference between the main galaxy populations. However, our conclusions remain robust to this effect. We show that accounting for different redshift and luminosity selections partly reconciles the size variation measured by Vulcani et al. with the weaker trends found by other recent studies. Dividing galaxies by visual morphology confirms the behaviour inferred using morphological proxies, although the sample size is greatly reduced. Finally, we demonstrate that varying dust opacity and disc inclination can account for features of the joint distribution of $\mathcal{R}$ and $\mathcal{N}$ for late-type galaxies. However, dust does not appear to explain the highest values of $\mathcal{R}$ and $\mathcal{N}$. The bulge-disc nature of galaxies must also contribute to the wavelength-dependence of their structure