The Advantage of the 5G Network for Enhancing the Internet of Things and the Evolution of the 6G Network.

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network.

Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles.

Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents an unmanned aerial vehicle (UAV)-aided network architecture. It is considered that the connected vehicles in a IoV ecosystem should fully offload latency-critical computation-intensive tasks to road side units (RSUs) that integrate MEC functionalities. In this regard, a UAV is deployed to serve as an aerial RSU (ARSU) and also operate as an aerial relay to offload part of the tasks to a ground RSU (GRSU). In order to further enhance the end-to-end communication during data offloading, the proposed architecture relies on reconfigurable intelligent surface (RIS) units consisting of arrays of reflecting elements. In particular, a dual-RIS configuration is presented, where each RIS unit serves its nearby network nodes. Since perfect phase estimation or high-precision configuration of the reflection phases is impractical in highly mobile IoV environments, data offloading via RIS units with phase errors is considered. As the efficient energy management of resource-constrained electric vehicles and battery-enabled RSUs is of outmost importance, this paper proposes an optimization approach that intends to minimize the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power constraints, timeslot scheduling, and task allocation. Extensive numerical calculations are carried out to verify the efficacy of the optimized dual-RIS-assisted wireless transmission.

FLEXGRID - A novel smart grid architecture that facilitates high-RES penetration through innovative flexibility markets towards efficient stakeholder interaction.

The FLEXGRID 2 project develops a digital platform designed to offer Digital Energy Services (DESs) that facilitate energy sector stakeholders (i.e. DSOs, TSOs, market operators, RES producers, retailers, flexibility aggregators) towards: i) automating and optimizing their investments and operation/management of their systems/assets, and ii) interacting in a dynamic and efficient way with their environment (electricity system) and the rest of the stakeholders. In this way, FLEXGRID envisages secure, sustainable, competitive, and affordable smart grids. A key objective is the incentivization of large-scale bottom-up investments in Distributed Energy Resources (DERs) through innovative smart grid management. Towards this goal, FLEXGRID develops innovative data models and energy market architectures (with high liquidity and efficiency) that effectively manage smart grids through an advanced TSO-DSO interaction as well as interaction between Transmission Network and Distribution Network level energy markets. Consequently, and through intelligence that exploits the innovation of the proposed market architecture, FLEXGRID develops investment tools able to examine in depth the emerging energy ecosystem and allow in this way: i) the financial sustainability of DER investors, and ii) the market liquidity/efficiency through advanced exploitation of DERs and intelligent network upgrades.

Total dermoscopy score calculation using quantitative measurements in digital dermoscopy.

The main contributions of this paper are an automated approach for applying the ABCDE rules in a digital dermoscopy platform with fixed settings and a new registration method specially designed for aligning and comparing follow-up digital dermoscopy images in order to evaluate the evolution over time parameter E. Experimental evaluations of the registration method are reported for image pairs acquired during follow-up examinations.

Power Conservation through Energy Efficient Routing in Wireless Sensor Networks.

The power awareness issue is the primary concern within the domain of Wireless Sensor Networks (WSNs). Most power dissipation ocurrs during communication, thus routing protocols in WSNs mainly aim at power conservation. Moreover, a routing protocol should be scalable, so that its effectiveness does not degrade as the network size increases. In response to these issues, this work describes the development of an efficient routing protocol, named SHPER (Scaling Hierarchical Power Efficient Routing).

An assisted-living home architecture with integrated healthcare services for elderly people.

Since the population of elderly people grows absolutely and in relation to the overall population in the world, the improvement of the quality of life of elderly people at home is of a great importance. This can be achieved through the development of generic technologies for managing their domestic ambient environment consisting of medical sensors, entertainment equipment, home automation systems and white goods, increasing their autonomy and safety. In this context, the provision intelligent interactive healthcare services will improve their daily life and allowing at the same time the continuous monitoring of their health and their effective treatment. This work is supported by the INHOME Project EU IST-045061-STP, http://www.ist-inhome.eu.

The Wisconsin breast cancer problem: Diagnosis and TTR/DFS time prognosis using probabilistic and generalised regression information classifiers.

This study addresses the breast cancer diagnosis and prognosis problem by employing two neural network architectures with the Wisconsin diagnostic and prognostic breast cancer (WDBC/WPBC) datasets. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among cases (instances) as derived from fine needle aspirate (FNA) tests, while the second architecture estimates the time interval that possibly contains the right endpoint of disease-free survival (DFS) of the patient. The accuracy of the neural classifiers reaches nearly 98% for the diagnosis and 93% for the prognosis problem, while the prognostic recurrence predictions were evaluated using survival analysis through the Kaplan-Meier approximation method. Both architectures were compared with other similar approaches. The robustness and real-time response of the proposed classifiers were further tested over the web as a potential integrated web-based decision support system.