A review of relay network on UAVS for enhanced connectivity

One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented

An iot-based smart building solution for indoor environment management and occupants prediction

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939)

Swarms of Unmanned Aerial Vehicles – A Survey

The purpose of this study is to focus on the analysis of the core characteristics of swarms of drones or Unmanned Aerial Vehicles and to present them in a way that facilitates analysis of public awareness on such swarms. Furthermore, the functionality, problems, and importance of drones are highlighted. Lastly, the experimental survey from a bunch of academic population demonstrates that the swarms of drones are fundamental future agendas and will be adapted by the time.</p

Hybrid satellite–terrestrial networks toward 6G : key technologies and open issues

Future wireless networks will be required to provide more wireless services at higher data rates and with global coverage. However, existing homogeneous wireless networks, such as cellular and satellite networks, may not be able to meet such requirements individually, especially in remote terrain, including seas and mountains. One possible solution is to use diversified wireless networks that can exploit the inter-connectivity between satellites, aerial base stations (BSs), and terrestrial BSs over inter-connected space, ground, and aerial networks. Hence, enabling wireless communication in one integrated network has attracted both the industry and the research fraternities. In this work, we provide a comprehensive survey of the most recent work on hybrid satellite–terrestrial networks (HSTNs), focusing on system architecture, performance analysis, design optimization, and secure communication schemes for different cooperative and cognitive HSTN network architectures. Different key technologies are compared. Based on this comparison, several open issues for future research are discussed

A scientometric analysis and critical review of gas turbine aero-engines control: From Whittle engine to more-electric propulsion

The gas turbine aero-engine control systems over the past eight decades have been thoroughly investigated. This review purposes are to present a comprehensive reference for aero-engine control design and development based on a systematic scientometric analysis and to categorize different methods, algorithms, and approaches taken into account to improve the performance and operability of aircraft engines from the first days to present to enable this challenging technology to be adopted by aero-engine manufacturers. Initially, the benefits of the control systems are restated in terms of improved engine efficiency, reduced carbon dioxide emissions, and improved fuel economy. This is followed by a historical coverage of the proposed concepts dating back to 1936. A comprehensive scientometric analysis is then presented to introduce the main milestones in aero-engines control. Possible control strategies and concepts are classified into four distinct phases, including Single input- single output control algorithms, MIN-MAX or Cascade control algorithms, advanced control algorithms, More-electric and electronic control algorithms and critically reviewed. The advantages and disadvantages of milestones are discussed to cover all practical aspects of the review to enable the researchers to identify the current challenges in aircraft engine control systems

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

5G embraces satellites for 6G ubiquitous IoT : basic models for integrated satellite terrestrial networks

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation (5G) networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied, to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose a HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions towards establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation (6G) ubiquitous Internet of Things (IoT)

An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization

Recent advancements in embedded systems, computing, networking, WS and SOA have opened the door for seamless integration of plant floor devices to higher enterprise level applications. Semantic web technologies, knowledge-based systems, context-sensitive computing and associated application development are widely explored in this regard. Ubiquitous and pervasive computing are the main domains of interest among many researchers so far. However, context-sensitive computing in manufacturing, particularly, relevant research and development in a production environment like FMS is relatively new and growing.Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime

An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization

Recent advancements in embedded systems, computing, networking, WS and SOA have opened the door for seamless integration of plant floor devices to higher enterprise level applications. Semantic web technologies, knowledge-based systems, context-sensitive computing and associated application development are widely explored in this regard. Ubiquitous and pervasive computing are the main domains of interest among many researchers so far. However, context-sensitive computing in manufacturing, particularly, relevant research and development in a production environment like FMS is relatively new and growing.Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime