A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm.

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user's needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user's needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System.

Implementing intelligent reflecting surfaces (IRSs), in high frequency based beyond 5G networks, has become a necessity to overcome the harsh blockage issues that exist in these bands. IRSs can supply user equipment (UEs) with multi alternative virtual line of sight (LOS) links, hence enhancing the spectral efficiency (SE) of the system. As a result of deploying multi IRSs as communication assistants, the step of IRSs-UEs association is required to optimally assign each UE to its best IRS; consideration of the interference between different links is needed, to maximize the system performance. However, this process will be a time and power consuming problem, if conventional schemes, which exhaustively search all possible association patterns to find the optimum one for communication, is adapted. Although iterative search based schemes can reduce this complexity, they still need feedback signaling in real time. Hence, they will be inefficient in terms of power consumption and delay. Moreover, optimal placement of the multi-IRSs in the network, to enlarge the system performance, is still an open issue and needs to be studied. Consequently, in this paper, to handle the IRSs-UEs association problem, we propose a neural network (NN) based scheme using a multi-IRSs aided multi input multi output (MIMO) system. In this system, the estimated angles of arrival (AoAs) of UEs are used as input features for the NN, which is trained to associate each UE to its best IRS based on this information; then, within each IRS, passive beamforming is performed. Adapting this NN in online mode guarantees obtaining better performance while relaxing the complexity of association and increasing response time, giving a performance comparable to the exhaustive and iterative search based schemes. The proposed NN based scheme determines the association pattern without searching or feedback signals. Moreover, the proposed approach maintains the system SE nearly similar to the optimum performance obtained by the conventional scheme. Secondly, a criterion is suggested for optimal deployment of multi IRSs in the network, depending on maximizing the average summation UEs signal-to-interference-plus-noise ratio (SINR). Numerical results prove that this strategy outperforms a reference one, which aims to guarantee certain performance by maximizing minimum UE SINR. In contrast the proposed strategy achieves better system and per UE spectral efficiency.

Radio Frequency over Fibre Optics Repeater for Mission-Critical Communications: Design, Execution and Test.

This paper presents a technical solution that addresses mission-critical communications by extending the radio frequency coverage area using a flexible and scalable architecture. One of the main objectives is to improve both the reaction time and the coordination between mission-critical practitioners, also called public protection and disaster relief users, that operate in emergency scenarios. Mission-critical services such as voice and data should benefit from reliable communication systems that offer high availability, prioritization and flexible architecture. In this paper, we considered Terrestrial Trunked Radio (TETRA), the mobile radio standard used for mission-critical communications, as it has been designed in this respect and is widely used by first responder organizations. Even if RF coverage is designed before network deployment and continuously updated during the lifetime of the technology, some white areas may exist and should be covered by supplementary base stations or repeaters. The model presented in this paper is an optical repeater for TETRA standard that can offer up to 52.6 dB downlink, 65.6 dB uplink gain and up to 3.71 km coverage distance in a radiating cable installation scenario. The design in not limited, as it can be extended to several different mobile radio standards using the same principle. Flexibility and scalability attributes are taken into consideration, as they can build a cost-effective deployment considering both capital and operational expenditures.

Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles.

Currently, the use of unmanned vehicles, such as drones, boats and ships, in monitoring tasks where human presence is difficult or even impossible raises several issues. Continuous efforts to improve the autonomy of such vehicles have not solved all aspects of this issue. In an Internet of Unmanned Vehicles (IoUV) environment, the idea of replacing the static wireless infrastructure and reusing the mobile monitoring nodes in different conditions would converge to a dynamic solution to assure data collection in areas where there is no infrastructure that ensures Internet access. The current paper fills a significant gap, proposing an algorithm that optimises the positions of unmanned vehicles such that an ad hoc network is deployed to serve specific wireless sensor networks that have no other Internet connectivity (hilly/mountainous areas, Danube Delta) and must be connected to an Internet of Things (IoT) ecosystem. The algorithm determines the optimum positions of UV nodes that decrease the path losses below the link budget threshold with minimum UV node displacement compared to their initial coordinates. The algorithm was tested in a rural scenario and 3rd Generation Partnership Project (3GPP), free space and two-ray propagation models. The paper proposes another type of network, a Flying and Surface Ad Hoc Network (FSANET), a concept which implies collaboration and coexistence between unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) and several use cases that motivate the need for such a network.

Analysis of Compromising Video Disturbances through Power Line.

In this article, we present results on research performed in the TEMPEST domain, which studies the electromagnetic disturbances generated unintentionally by electronic equipment as well as the methods to protect the information processed by this equipment against these electromagnetic phenomena. The highest vulnerability of information leakage is attributed to the display video signal from the TEMPEST domain perspective. Examples of far-range propagation on a power line of this type of disturbance will be illustrated for the first time. Thus, the examples will highlight the possibility of recovering processed information at distances of 1, 10 and 50 m. There are published articles studying electromagnetic disturbances generated by electronic equipment propagating on power cables of such equipment but no studies on their long-distance propagation. Our research aims to raise awareness in the scientific community and the general public of the existence of such vulnerabilities that can compromise confidential or sensitive information that can make the difference between success or failure in the business sector, for example, or can harm personal privacy, which is also important for us all. Countermeasures to reduce or even eliminate these threats will also be presented based on the analysis of the signal-to noise-ratio recorded during our research.

Big Data, Internet of Things and Cloud Convergence--An Architecture for Secure E-Health Applications.

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.