Advances in Aeronautical Informatics

This chapter draws the readers into a comprehensive discussion about the advances in Information and Communication Technologies (ICT) and their influence on the technology landscape of aeronautics. It gives a rough overview of the advances in technical systems from the industrial revolution up until Industry 4.0 and elaborates the reflection of these advancements in aeronautics from the pioneers era toward Flight 4.0. It briefly describes various recent fields of research in ICT such as Cyber-Physical Systems (CPS), Internet of Things (IoT), wireless networks, multi-core architectures, Service-Oriented Architecture (SOA), cloud computing, big data, and modern software engineering methodologies as the parts of future aeronautical engineering body of knowledge. Thereafter, it describes aeronautical informatics as an establishing interdisciplinary field of study of applied informatics and aeronautics.

Embedded systems play an ever increasing role in almost any field of daily life, including the mobility domain taking massive benefits from using software in their products. The intense use of software leads to a situation, where processing platforms have to be introduced in many different fields of applications. However, well-known platforms will not be able to satisfy the ever increasing requirements on processing performance. Thus, for new functionality, higher performant systems have to be implemented using alternative and emerging architectures. Multi-core technology, being state of the art in standard ICT for a couple of years now, seems to be the most promising way and will also find its way into avionics systems. However, the characteristics of the target platforms—as will be outlined in Sect. 2.2—changed over the years. Coming from more simple and more easy to use single-core processors to distributed multiprocessor systems toward multi-core processors, the development shows huge differences as discussed in Sect. 2.3. Especially the use of multi-core based systems in the mobility domains introduces challenges that are by far more complex than primarily expected. These challenges, resulting from the basic architecture of the processors, are identified and will be presented in Sect. 2.4. Resulting failure modes and their sources are identified in Sect. 2.5. Finally, the trends and conclusions regarding the emerging multi-core technology are discussed in Sect. 2.6.

Embedded sensing systems are widely deployed aboard aircraft to capture flight parameters and cater to their processing, logging, and visualization. However, it is their interconnection to form avionics networks that facilitates the provision of a large range of additional functionalities. Most prevalently, the fusion of sensor data collected at different points within aircraft enables the collection of a holistic and comprehensive situational picture. Several key design decisions must be made to set up avionics networks in practice: Besides the identification of suitable hardware platforms, decisions must be made regarding the selection of communication technologies to use, the desired network topologies, and the choice of networking protocols. Across all these dimensions of the parameter space, application-specific requirements must also be adequately catered for, e.g., to meet latency, performance, or reliability constraints. In this chapter, we will discuss requirements to avionics networks as well as highlighting design options to meet them. At last, we present selected promising avenues for future research.

Flight 4.0 represents a rapidly expanding research domain that brings IoT (Internet of Things) technology in the aviation domain. Based on various engineering domains such as Wireless Sensor Networks (WSNs) and embedded systems, Flight 4.0 systems are characterized by high degree of heterogeneity regarding various perspectives, such as communication, hardware, and software solutions. Additionally, in order to be well accepted by the end users, it is of paramount importance to exhibit high degree of configurability and flexibility so as to be applicable in diverse application scenarios. Aiming to address such objectives, this chapter attempts to identify the main aspects and tendencies toward a holistic end-to-end communication infrastructure for Flight 4.0 systems. In this context, and serving as a roadmap, the respective architectures should offer a homogeneous support to a wide range of WSN communication technologies and protocols, while being able to support time-constrained monitor, control, and configuration of critical Flight 4.0 infrastructure. In addition, such architectures must emphasize on the use of distributed components that are able to offer enhanced fault tolerance performance, a critical aspect for most modern aviation systems.

Big Data technology in the field of aviation has emerged in recent years. Continuously growing amounts of data sources such as sensors, radars, cameras, weather stations, airports, etc. produce terabytes of high dynamic data each second. The future aviation concepts require modern data storing, data processing, and data analyzing technologies. The extraction of meaningful knowledge from the given data is a major challenge, trends, cross-connection, correlations, etc. have to be identified. Real-time critical tasks increase additionally the technology requirements and need innovative solutions. The application of Big Data technology in aviation context optimizes safety aspects, fuel consumption, maintenance processes, flight scheduling, etc. This chapter describes a process of Big Data application and summarizes relevant actual Big Data methods in the aviation domain.

Avionics systems are getting increasingly sophisticated, airspaces are densely occupied, and aircraft are desired to fly in more adverse weather conditions. These conditions increase the complexity of Air Traffic Management (ATM) as aviators and airspace controllers struggle to maintain safety while cross-checking multisource information, including information from Unmanned Aerial Systems (UASs). Hence, future ATM decision-support systems are required not only to be autonomous and reliable complex decision-making processes with minimal human intervention, but also must be able to deal with UAS ATM (UTM). This chapter presents the implementation of Ontologies for NextGen Avionics Systems (ONAS) for UTM. The ONAS approach consists of an operation framework and an ontology-based tool, called Avionics Analytics Ontology (AAO), to support decision-making in advanced ATM/UTM systems. The AAO entails a cognitive ATM/UTM architecture for avionics analytics where an ontological database captures information related to weather, flights, and airspace. The AAO-based decision-making process supports human Situation AWareness (SAW) as well as machine Situation Assessment (SA). The ONAS approach presented is intended to be initially used in civil aviation. A use case along with two different scenarios is presented for an ATM/UTM system. The scenarios represent realistic flight situations (based on dataset from a flight tracking service) where the ATM/UTM decisions made are supported by the AAO.

Avionics, like any other safety-critical real-time systems, pose unique challenges on system design, development, and testing. Specifically, the rigorous certification process mandated for avionics software calls for additional attention. The DO-178C Software Considerations in Airborne Systems and Equipment Certification provides detailed guidelines to ensure safety measures. This chapter gives a different angle to avionics development and certification, highlighting model-based approaches for advancing the design, development, testing, and maintenance of airborne software systems. Modern software engineering processes such as agile and scrum are discussed as the new techniques in speeding up the certification hurdle, while achieving higher return on investment.

This chapter describes unmanned aircraft with respect to autonomy and safety aspects of aerospace. The focus will be on unmanned aircraft systems, however most of the principles regarding safety and automation are valid for both, manned and unmanned aviation. As a means to assure safety for aircraft, safety assessments, development processes, and software standards have been established for manned aviation. In this context, design-time assurance of software will be discussed. Another key component of the safety concept for manned aviation is the onboard pilot. The pilot supervises and validates the system behavior and develops a gut feeling if the system is okay, due to his onboard presence. This is not possible for an unmanned aircraft. Human supervision will be remotely located. Therefore, an extensive discussion on runtime assurance and automated supervision will be a part of this work. Furthermore, with the growing degrees of automation and upcoming autonomy of the aircraft, one pilot might have to supervise more than one aircraft at the same time. Unmanned aircraft are expected to be integrated into civil airspace in the near future, possibly in very large quantities. The autonomy of these unmanned aircraft and the absence of a pilot onboard the aircraft is a source of concern. However, the automation and autonomy can also support safety. The interdependence between safety and autonomy will be discussed in this chapter. The challenge regarding unmanned aircraft is that the same level of safety can be maintained. In this context, this chapter will discuss the impact of new and upcoming regulations and standards for unmanned aircraft regarding a holistic approach to the assessment of risk and their impact on autonomy and safety.

This chapter is concerned with timing verification of future avionics software. We critically review a recent CAST position paper, identified as CAST-32A, about certification issues connected to the use of multi-core architectures and show that it leaves several issues unresolved. It introduces robust partitioning as a requirement for the feasibility of timing verification, but fails to precisely define it. We give a precise notion of robust partitioning that guarantees temporal isolation and, therefore, allows for separate timing analysis of tasks running on individual cores. Sometimes, complete temporal isolation is impossible to achieve or will lead to very poor resource allocation. In an ideal setting, one could analyze the timing behavior of a set of applications executed on several cores in a compositional way. We discuss the requirements for a correct analysis of the interference on the shared resources of multi-core processors. Finally, we show how to configure an existing multi-core architecture to enable compositional timing analysis.

This chapter investigates specific approaches to evolve the Aerospace Engineering curricula to increase coverage of the fundamentals of computer science and deepen student experience in programming. First, existing K-12, Aerospace Engineering, and Computer Science and Engineering curricula are examined. Multidisciplinary programs including robotics and Cyber-Physical Systems (CPS) are reviewed to provide insight into potential directions in which an Aerospace-centric program might expand. Student, faculty, and industry interests offer insight into key Computer Science and Engineering (CSE) content to infuse into next-generation Aerospace curricula. The approach being taken at the University of Michigan, the author’s home institution, is described, including plans to increase curricular flexibility and introduce a new course providing students background in key computer science concepts such as data structures and complexity, computational science with application to Aerospace analysis and design, and embedded data management and control. A discussion of potential future curricular extensions into human–machine systems and electromechanical devices concludes the chapter.