Emergent behaviors in the Internet of things: The ultimate ultra-large-scale system

To reach its potential, the Internet of Things (IoT) must break down the silos that limit applications' interoperability and hinder their manageability. Doing so leads to the building of ultra-large-scale systems (ULSS) in several areas, including autonomous vehicles, smart cities, and smart grids. The scope of ULSS is both large and complex. Thus, the authors propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly programming all possible decisions in the vast space of ULSS scenarios, HEB relies on the emergent behaviors induced by local rules at each level of the hierarchy. The authors discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. They also illustrate the HEB concepts in reference to autonomous vehicles. This use case paves the way to the discussion of new lines of research.Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Engineering Resilient Space Systems

Several distinct trends will influence space exploration missions in the next decade. Destinations are becoming more remote and mysterious, science questions more sophisticated, and, as mission experience accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult, harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards, such as free-floating active small bodies; multielement missions required to answer more sophisticated questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration, that must survive equipment failures over the span of decades. These missions will need to be successful without a priori knowledge of the most efficient data collection techniques for optimum science return. Science objectives will have to be revised ‘on the fly’, with new data collection and navigation decisions on short timescales. Yet, even as science objectives are becoming more ambitious, several critical resources remain unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking mission budgets for operations support. How can we continue to explore challenging new locations without increasing risk or system complexity? These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as documented in Vision and Voyages for Planetary Science in the Decade 2013–2022 (National Research Council, 2011), but are especially acute for the following mission examples, identified in our recently completed KISS Engineering Resilient Space Systems (ERSS) study: 1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform science operations as components and subsystems degrade and fail; 2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination (essentially hibernating to save on operations costs), then upon arrival, would have to act as its own surveyor, finding new objects and targets of opportunity as it approaches each asteroid, requiring response on short notice; and 3. A MSR campaign would not only be required to perform fast reconnaissance over long distances on the surface of Mars, interact with an unknown physical surface, and handle degradations and faults, but would also contain multiple components (launch vehicle, cruise stage, entry and landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that dramatically increase the need for resilience to failure across the complex system. The concept of resilience and its relevance and application in various domains was a focus during the study, with several definitions of resilience proposed and discussed. While there was substantial variation in the specifics, there was a common conceptual core that emerged—adaptation in the presence of changing circumstances. These changes were couched in various ways—anomalies, disruptions, discoveries—but they all ultimately had to do with changes in underlying assumptions. Invalid assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of interactions within the system, may cause unexpected or unintended system behavior. A system is resilient if it continues to perform the intended functions in the presence of invalid assumptions. Our study focused on areas of resilience that we felt needed additional exploration and integration, namely system and software architectures and capabilities, and autonomy technologies. (While also an important consideration, resilience in hardware is being addressed in multiple other venues, including 2 other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study period. The first workshop (Workshop #1) explored the ‘problem space’ as an organizing theme, and the second workshop (Workshop #2) explored the ‘solution space’. In each workshop, focused discussions and exercises were interspersed with presentations from participants and invited speakers. The study period between the two workshops was organized as part of the synthesis activity during the first workshop. The study participants, after spending the initial days of the first workshop discussing the nature of resilience and its impact on future science missions, decided to split into three focus groups, each with a particular thrust, to explore specific ideas further and develop material needed for the second workshop. The three focus groups and areas of exploration were: 1. Reference missions: address/refine the resilience needs by exploring a set of reference missions 2. Capability survey: collect, document, and assess current efforts to develop capabilities and technology that could be used to address the documented needs, both inside and outside NASA 3. Architecture: analyze the impact of architecture on system resilience, and provide principles and guidance for architecting greater resilience in our future systems The key product of the second workshop was a set of capability roadmaps pertaining to the three reference missions selected for their representative coverage of the types of space missions envisioned for the future. From these three roadmaps, we have extracted several common capability patterns that would be appropriate targets for near-term technical development: one focused on graceful degradation of system functionality, a second focused on data understanding for science and engineering applications, and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending these roadmaps to identify candidate enablers of the capabilities from the following three categories: architecture solutions, technology solutions, and process solutions. The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think deeply about the theory, approaches, and technical issues involved in developing and applying resilience capabilities. The conclusions summarize the varied and disparate discussions that occurred during the study, and include new insights about the nature of the challenge and potential solutions: 1. There is a clear and definitive need for more resilient space systems. During our study period, the key scientists/engineers we engaged to understand potential future missions confirmed the scientific and risk reduction value of greater resilience in the systems used to perform these missions. 2. Resilience can be quantified in measurable terms—project cost, mission risk, and quality of science return. In order to consider resilience properly in the set of engineering trades performed during the design, integration, and operation of space systems, the benefits and costs of resilience need to be quantified. We believe, based on the work done during the study, that appropriate metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional work is required to explicitly tie design decisions to these first-order concerns. 3. There are many existing basic technologies that can be applied to engineering resilient space systems. Through the discussions during the study, we found many varied approaches and research that address the various facets of resilience, some within NASA, and many more beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced Research Projects Agency (DARPA) initiatives, ‘smart’ power grid control, cyber-physical systems, software architecture, and application of formal verification methods for software were identified and discussed. The variety and scope of related efforts is encouraging and presents many opportunities for collaboration and development, and we expect many collaborative proposals and joint research as a result of the study. 4. Use of principled architectural approaches is key to managing complexity and integrating disparate technologies. The main challenge inherent in considering highly resilient space systems is that the increase in capability can result in an increase in complexity with all of the 3 risks and costs associated with more complex systems. What is needed is a better way of conceiving space systems that enables incorporation of capabilities without increasing complexity. We believe principled architecting approaches provide the needed means to convey a unified understanding of the system to primary stakeholders, thereby controlling complexity in the conception and development of resilient systems, and enabling the integration of disparate approaches and technologies. A representative architectural example is included in Appendix F. 5. Developing trusted resilience capabilities will require a diverse yet strategically directed research program. Despite the interest in, and benefits of, deploying resilience space systems, to date, there has been a notable lack of meaningful demonstrated progress in systems capable of working in hazardous uncertain situations. The roadmaps completed during the study, and documented in this report, provide the basis for a real funded plan that considers the required fundamental work and evolution of needed capabilities. Exploring space is a challenging and difficult endeavor. Future space missions will require more resilience in order to perform the desired science in new environments under constraints of development and operations cost, acceptable risk, and communications delays. Development of space systems with resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by enabling as yet unforeseen missions and breakthrough science observations. Our KISS study provided an essential venue for the consideration of these challenges and goals. Additional work and future steps are needed to realize the potential of resilient systems—this study provided the necessary catalyst to begin this process

Complexity of Multi-Modal Transportation and Systems of Systems

The multi-modal transportation comprising diverse infrastructures, means & operations, energy resources, rules & regulations and a broad community of stakeholders constitute a complex yet real candidate for formalisation, analysis and optimization. It is shown that the complexity of the system is best described by viewing the challenges of its complexity as a System of Systems (SoS). The objective of this paper is to make an attempt to define and formalise the loose concept of “System of Systems” within the standard framework of Systems Theory and exploit this new theoretical foundation as a basis for understanding, characterisation, evaluation, assessment and management of complexity, relationships and optimal performance in a multi-modal transportation context. The longer term objective is to develop a robust systems framework for scientific treatment of requirements, constraints, risks, resilience, capacity, performance and trade-offs in multi-modal transportation setting

Ontologies as Backbone of Cognitive Systems Engineering

Cognitive systems are starting to be deployed as appliances across the technological landscape of modern societies. The increasing availability of high performance computing platforms has opened an opportunity for statistics-based cognitive systems that perform quite as humans in certain tasks that resisted the symbolic methods of classic artificial intelligence. Cognitive artefacts appear every day in the media, raising a wave of mild fear concerning artificial intelligence and its impact on society. These systems, performance notwithstanding, are quite brittle and their reduced dependability limips their potential for massive deployment in mission-critical applications -e.g. in autonomous driving or medical diagnosis. In this paper we explore the actual possibility of building cognitive systems using engineering-grade methods that can assure the satisfaction of strict requirements for their operation. The final conclusion will be that, besides the potential improvement provided by a rigorous engineering process, we are still in need of a solid theory -possibly the main outcome of cognitive science- that could sustain such endeavour. In this sense, we propose the use of formal ontologies as backbones of cognitive systems engineering processes and workflows

A Multi-Perspective Framework for Research on (Sustainable) Autonomous Systems

The ongoing digital transformation is challenging the way in which business is conducted and value is created and captured (Vial 2019). While prior digitalization waves focused on replacing paper as physical carrier of information, leveraging the Internet as global communication infrastructure, and developing reactive, partly automated business processes and systems (e.g. Legner et al. 2017), the next wave will be about transforming these processes/systems into proactive autonomous systems (AS). Such systems represent complex “systems of systems” with different maturities, qualities, reliabilities, and performances, which may develop their own dynamics (Boardman and Sauser 2006; Maier 1999). In the information systems (IS) context, a common characteristic of AS is their reliance on large amounts of data, along with the use of advanced technologies—such as the Internet of Things, Artificial Intelligence (AI), Machine Learning, or Blockchain—that allow for gathering and processing ‘big’ data with limited, or even no, human involvement. Today, AS can be found in various fields of application. Popular examples include driverless cars, smart cities, and smart homes, which often rely on a combination of sensors, algorithms, and self-executable code. Besides these tangible AS that link the physical world to the information world (Barrett 2006), we note a growing number of intangible AS in the form of software systems that operate either entirely in the background or at the interface with humans. Examples are intelligent chatbots, smart contracts, and recommender systems (Murray et al. 2021a; Pfeiffer et al. 2020; Rutschi and Dibbern 2020; Wang et al. 2019a, b), as well as algorithmic management and control systems, such as the ones used by Uber and other gig economy firms to manage their digital workforce (Cram and Wiener 2020; Möhlmann et al. 2021; Wiener et al. 2021). Even though AS are designed, developed, and implemented in a process of socio-technical interaction, once in use, the embedded technology takes on the role of an autonomous agent (or actor) that can make decisions and perform actions independently of humans (Baird and Maruping 2021). In other words, what has been created in a socio-technical way by implementing patterns—including organizational rules, as well as social norms and values—into a technical system, turns into a techno-social system once operating, where social agents in the organizational environment respond to the technical system and where the system may self-adapt to environmental changes. Thus, agency, decision rights, and responsibility are handed over to technology agents, while the ultimate accountability and decision rights to change these systems still reside with the governing entity owning those systems (Kellogg et al. 2020).Footnote1 This asks for a better understanding of AS in a broader context, where the autonomy of technical systems as agents must be analyzed in relation to human agents. In fact, changes in the autonomy of one (human or technology) agent may have consequences for the autonomy of another agent. Accordingly, the notion of “conjoined agency” between human and technology agents has been conceptualized as one way to acknowledge new types of interdependencies that arise in the course of increasing technology autonomy (Murray et al. 2021b). Another way to view AS is by consideration of their temporal dimension, as captured by the notion of sustainability, which generally refers to some long-term existence. This means that, once in use, AS should be able to exist and technology agents embedded in these systems should be able to fulfill their function for a longer period of time without human intervention, as otherwise they cannot be considered being really autonomous. In this sense, sustainable autonomous systems (SAS) may refer to self-learning technical systems that are constantly improving themselves, such as an autonomous vehicle that, on a daily commute, keeps optimizing the route it takes. Put differently, SAS are characterized by their ability to adapt to changing circumstances and be responsive to environmental changes. In doing so, SAS may not only optimize themselves in accordance with some predefined output criteria (e.g., quality or performance), but also with regard to their consumption of resources (e.g., an autonomous vehicle constantly improving its fuel consumption). On a larger scale, this points to another perspective on sustainability directed towards the effects of AS use and operation. As such, sustainability may also concern the long-term economic, social, and environmental effects of using AS (Hart and Milstein 2003), commonly referred to as the “3Ps” (profit, people, and planet) of the triple bottom line (Elkington 1997). This perspective includes the effects of SAS on the efficient use of tangible resources, such as energy (e.g., smart offices), space (e.g., smart cities), food (e.g., smart fridges), or natural resources (e.g., smart agricultures), as well as their effects on intangible resources, such as the longevity of data (e.g., for auditing purposes) or human and social capital in general. While the debate around SAS is not new, the emergence of blockchain has fueled innovative solutions, but also concerns regarding the energy consumption of blockchains based on the so-called “proof of work” consensus mechanism (Sedlmeir et al. 2020). While ecologic sustainability is one important aspect of SAS, there are further aspects that need to be considered. For example, as unintended and unforeseen second-order or spillover effects can result from the deployment of SAS, the question must be answered if we really want to rely on systems that are on ‘autopilot.’ Here, critical ethical questions arise (Tang et al. 2020), including questions of fairness regarding the decision rules according to which AS act (Dolata et al. 2021); for instance, how a driverless car should react to unforeseen circumstances affecting humans (Kirkpatrick 2015). In recent years, IS research has begun to pick up the concept of autonomy and to study it from different perspectives. Thereby it is important to note that the concept of autonomy is by no means new to the IS field. For example, autonomy has been an inherent characteristic of intelligent software agents (Jennings et al. 1998), which have been subject of research in various fields of application, such as supply-chain automation and improvement (Nissen and Sengupta 2006) or electronic auctions (Adomavicius et al. 2008). It is only recently, however, that the concept of autonomy has gained increasing interest with regard to the phenomena described above. Against this backdrop, in this editorial, we seek to synthesize and integrate different autonomy concepts and develop a framework that can serve as a basis for future research on (S)AS in various IS contexts and settings. In particular, drawing on the IS and related literatures, we first identify and review different autonomy concepts and their definitions. On this basis, we then elaborate on the relationships among those concepts and present a multi-perspective framework for studying (S)AS in a broader “systems of systems” context along with promising directions for future research. Our framework has been inspired by the existing literature on autonomy and AS, as well as the experiences we made as editors during the review process for our special issue on SAS in BISE. In total, we received 12 papers out of which two were accepted and published in this issue