Comparison among Cognitive Radio Architectures for Spectrum Sensing

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed

Automatic Passenger Counting on the Edge via Unsupervised Clustering

We present a device- and network-based solution for automatic passnger counting that operates on the edge in real time. The proposed solution consists of a low-cost WiFi scanner device equipped with custom algorithms for dealing with MAC address randomization. Our low-cost scanner is able to capture and analyze 802.11 probe requests emitted by passengers' devices such as laptops, smartphones, and tablets. The device is configured with a Python data-processing pipeline that combines data coming from different types of sensors and processes them on the fly. For the analysis task, we have devised a lightweight version of the DBSCAN algorithm. Our software artifact is designed in a modular way in order to accommodate possible extensions of the pipeline, e.g., either additional filters or data sources. Furthermore, we exploit multi-threading and multi-processing for speeding up the entire computation. The proposed solution has been tested with different types of mobile devices, obtaining promising experimental results. In this paper, we present the key ingredients of our edge computing solution

Sex-Specific Association of Left Ventricular Hypertrophy With Rheumatoid Arthritis

Objectives: Clinical expression of rheumatoid arthritis (RA) varies by gender, but whether cardiovascular disease (CVD) is gender related in RA is unknown. Left ventricular (LV) hypertrophy (LVH) is a hallmark of CVD in RA patients. We investigated whether the association of LVH with RA is gender driven.Methods: Consecutive outpatients with established RA underwent echocardiography with measurement of LVH at baseline and one follow-up. All participants had no prior history of CVD or diabetes mellitus. We assessed CVD risk factors associated with LVH at follow-up, including sex, age, arterial blood pressure, and body mass index (BMI). We also evaluated inflammatory markers, autoimmunity, disease activity, and the use of RA medications as predictors of LVH.Results: We recruited 145 RA patients (121 females, 83%) and reassessed them after a median (interquartile range) of 36 months (24–50). At baseline, women were more dyslipidemic but otherwise had fewer CVD risk factors than men, including less prevalent smoking habit and hypertension, and smaller waist circumference. At follow-up, we detected LVH in 42/145 (44%) RA patients. LV mass significantly increased only in women. In multiple Cox regression analysis, women with RA had the strongest association with LVH, independently from the presence of CVD risk factors (OR, 6.56; 95% CI, 1.34–30.96) or RA-specific characteristics (OR, 5.14; 95% CI, 1.24–21.34). BMI was also significantly and independently associated with LVH.Conclusion: Among established RA patients, women carry the highest predisposition for LVH

A Flexible IoT Stream Processing Architecture Based on Microservices

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework

OFDM recognition based on cyclostationary analysis in an open spectrum scenario

Abstract—In this paper the problem of detecting the presence of similar OFDM signals, i.e. WLAN and WiMAX signals, in an Open Spectrum scenario is faced. The identification of the channel occupancy and the signal classification are performed by using a fast detector based on a single spectral correlation function estimator and a multi-class support vector machine classifier which are designed and tested in a multipath environment. Finally, the obtained numerical results and the amount of processing necessary to perform the considered operations are reported and discussed. I

A comparison between stand-alone and distributed architectures for spectrum hole detection

Abstract—In this paper two different cognitive radio architectures, i.e. stand-alone and distributed, are proposed for spectrum sensing purposes. In particular, both architectures implement a fast and reliable algorithm based on cyclic features extraction which allows to identify spectrum holes. The performances of such systems are compared in detecting primary users ’ presence in a monitored area classifying the used transmission standards, IEEE 802.11a and IEEE 802.16e. The considered scenario is challenging since both standards use the OFDM transmission technique, are designed to have the same bandwidth and use the same frequency band. A set of numerical simulations have been carried out to compare the performances of the proposed systems in a heavy multipath scenario and their advantages and disadvantages are discussed. I