An efficient architecture for the integration of sensor and actuator networks into the future internet

In the future, sensors will enable a large variety of new services in different domains. Important application areas are service adaptations in fixed and mobile environments, ambient assisted living, home automation, traffic management, as well as management of smart grids. All these applications will share a common property, the usage of networked sensors and actuators. To ensure an efficient deployment of such sensor-actuator networks, concepts and frameworks for managing and distributing sensor data as well as for triggering actuators need to be developed. In this paper, we present an architecture for integrating sensors and actuators into the future Internet. In our concept, all sensors and actuators are connected via gateways to the Internet, that will be used as comprehensive transport medium. Additionally, an entity is needed for registering all sensors and actuators, and managing sensor data requests. We decided to use a hierarchical structure, comparable to the Domain Name Service. This approach realizes a cost-efficient architecture disposing of "plug and play" capabilities and accounting for privacy issues

Context-based user grouping for multi-casting in heterogeneous radio networks

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented

Сложность алгоритмов криптографической системы Эль–Гамаля и ихэффективность

Objective. - Electrical remodeling as well as atrial contractile dysfunction after the conversion of atrial fibrillation (AF) to sinus rhythm (SR) are mainly caused by a reduction of the inward L-type Ca2+ current (ICaL). We investigated whether the expression of L-type Ca2+-channel subunits was reduced in atrial myocardium of AF patients. Methods. - Right atrial appendages were obtained from patients undergoing coronary artery bypass graft surgery (CAD, n = 35) or mitral valve surgery (MVD, n = 37). Seventeen of the CAD patients and 18 of the MVD patients were in chronic (>3 months) AF, whereas the others were in SR. The protein expression of the L-type Ca2+-channel subunits {alpha}1C and {beta}2 was quantified by western blot analysis. Furthermore, we measured the density of dihydropyridine (DHP)-binding sites of the L-type Ca2+ channel using 3H-PN220-100 as radioligand. Results. - Surprisingly, the {alpha}1C and the {beta}2-subunit expression was not altered in atrial myocardium of AF patients. Also, the DHP-binding site density was unchanged. Conclusion. - The protein expression of the L-type Ca2+-channel subunits {alpha}1C or {beta}2 is not reduced in atrial myocardium of AF patients. Therefore, the reduced ICaL might be due to downregulation of other accessory subunits ({alpha}2{delta}), expression of aberrant subunits, changes in channel trafficking or alterations in channel function

Current controversies in determining the main mechanisms of atrial fibrillation

Despite considerable basic research into the mechanisms of atrial fibrillation (AF), not much progress has been made in the prognosis of patients with AF. With the exception of anticoagulant therapy, current treatments for AF still do not improve major cardiovascular outcomes. This may be due partly to the diverse aetiology of AF with increasingly more factors found to contribute to the arrhythmia. In addition, a strong increase has been seen in the technological complexity of the methods used to quantify the main pathophysiological alterations underlying the initiation and progression of AF. Because of the lack of standardization of the technological approaches currently used, the perception of basic mechanisms of AF varies widely in the scientific community. Areas of debate include the role of Ca2+-handling alterations associated with AF, the contribution and noninvasive assessment of the degree of atrial fibrosis, and the best techniques to identify electrophysiological drivers of AF. In this review, we will summarize the state of the art of these controversial topics and describe the diverse approaches to investigating and the scientific opinions on leading AF mechanisms. Finally, we will highlight the need for transparency in scientific reporting and standardization of terminology, assumptions, algorithms and experimental conditions used for the development of better AF therapies. Content List - Read more articles from the symposium: Atrial fibrillation - from atrial extrasystoles to atrial cardiomyopathy. What have we learned from basic science and interventional procedures

Reference Network and Localization Architecture for Smart Manufacturing based on 5G

5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix low-volume application, in particular at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases.Comment: 10 pages; submitted to 6th International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production, 7-9 September Genova, Ital

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

The prognosis of allocentric and egocentric neglect : evidence from clinical scans

We contrasted the neuroanatomical substrates of sub-acute and chronic visuospatial deficits associated with different aspects of unilateral neglect using computed tomography scans acquired as part of routine clinical diagnosis. Voxel-wise statistical analyses were conducted on a group of 160 stroke patients scanned at a sub-acute stage. Lesion-deficit relationships were assessed across the whole brain, separately for grey and white matter. We assessed lesions that were associated with behavioural performance (i) at a sub-acute stage (within 3 months of the stroke) and (ii) at a chronic stage (after 9 months post stroke). Allocentric and egocentric neglect symptoms at the sub-acute stage were associated with lesions to dissociated regions within the frontal lobe, amongst other regions. However the frontal lesions were not associated with neglect at the chronic stage. On the other hand, lesions in the angular gyrus were associated with persistent allocentric neglect. In contrast, lesions within the superior temporal gyrus extending into the supramarginal gyrus, as well as lesions within the basal ganglia and insula, were associated with persistent egocentric neglect. Damage within the temporo-parietal junction was associated with both types of neglect at the sub-acute stage and 9 months later. Furthermore, white matter disconnections resulting from damage along the superior longitudinal fasciculus were associated with both types of neglect and critically related to both sub-acute and chronic deficits. Finally, there was a significant difference in the lesion volume between patients who recovered from neglect and patients with chronic deficits. The findings presented provide evidence that (i) the lesion location and lesion size can be used to successfully predict the outcome of neglect based on clinical CT scans, (ii) lesion location alone can serve as a critical predictor for persistent neglect symptoms, (iii) wide spread lesions are associated with neglect symptoms at the sub-acute stage but only some of these are critical for predicting whether neglect will become a chronic disorder and (iv) the severity of behavioural symptoms can be a useful predictor of recovery in the absence of neuroimaging findings on clinical scans. We discuss the implications for understanding the symptoms of the neglect syndrome, the recovery of function and the use of clinical scans to predict outcome