Artificial Intelligence for the Internet of Everything

William Lawless, Ranjeev Mittu, Donald Sofge, Ira S S Moskowitz, Stephen Russell

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128176368
• eBook ISBN: 9780128176375
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Artificial Intelligence for the Internet of Everything considers the foundations, metrics and applications of IoE systems. It covers whether devices and IoE systems should speak only to each other, to humans or to both. Further, the book explores how IoE systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems. It examines the meaning, value and effect that IoT has had and may have on ordinary life, in business, on the battlefield, and with the rise of intelligent and autonomous systems. Based on an artificial intelligence (AI) perspective, this book addresses how IoE affects sensing, perception, cognition and behavior. Each chapter addresses practical, measurement, theoretical and research questions about how these "things may affect individuals, teams, society or each other. Of particular focus is what may happen when these "things begin to reason, communicate and act autonomously on their own, whether independently or interdependently with other "things. - Considers the foundations, metrics and applications of IoE systems - Debates whether IoE systems should speak to humans and each other - Explores how IoE systems affect targeted audiences and society - Discusses theoretical IoT ecosystem models

Chapter 1

Introduction

W.F. Lawless^⁎; Ranjeev Mittu^†; Donald A. Sofge^‡; Ira S. Moskowitz^†; Stephen Russell^§ * Departments of Math & Psychology, Paine College, Augusta, GA, United States
^† Information Management & Decision Architectures Branch (CODE 5580), Information Technology Division, U.S. Naval Research Laboratory, Washington, DC, United States
^‡ Distributed Autonomous Systems Group, Code 5514, Navy Center for Applied Research in Artificial Intelligence, U.S. Naval Research Laboratory, Washington, DC, United States
^§ Battlefield Information Processing Branch, Computational Information Sciences Directorate, Army Research Laboratory, Adelphi, MD, United States

Abstract

At our AAAI-2016 symposium on the Internet of Everything (IoE),¹ from an artificial intelligence (AI) perspective, we had participants who discussed the meaning, value, and effect that the Internet of Things (IoT) has and will have in ordinary life, on the battlefield—Internet of Battlefield Things (IoBT), in the medical field—Internet of Medical Things (IoMT), and other fields, and with intelligent-agent feedback in the form of constructive and destructive interference (i.e., interdependence). Many of the invited and regular speakers at our Symposium have revised and expanded their papers for this book; other researchers who were not formally a part of the Symposium also provided chapters. At the Symposium, and for this book, we purposively left the topic open-ended. We were then, and are now, interested in research with an AI perspective that addressed how the IoE affects sensing, perception, cognition, and behavior, or causal relations, whether the context for an interaction was clear or uncertain for mundane decisions, complex decisions on the battlefield, life and death decisions in the medical arena, or decisions affected by intelligent agents and machines. We were especially interested in theoretical perspectives for how these “things” may affect individuals, teams and society, and in turn how they may affect these “things.” We were especially interested in what may happen when these “things” begin to think (Gershenfeld, 1999). Our ultimate goal was, and remains, to use AI to advance autonomy and autonomic characteristics to improve the performance of individual agents and hybrid teams of humans, machines, and robots for the betterment of society. In the introduction that follows we review the background along with an overview of IoE; afterwards, we introduce the chapters that follow in order of where they are listed in the Table of Contents.

Keywords

Internet of Things (IoT); Internet of Everything (IoE); Autonomy; Artificial intelligence (AI); Compositional models; Analytics; Agents; Web; Humans; Machine; Robots

1.1 Introduction: IoE: IoT, IoBT, and IoIT—Background and Overview

The Internet of Everything (IoE) generalizes machine-to-machine (M2M) communications for the Internet of Things (IoT) to form an even more complex system that also encompasses people, robots, and machines. From Chambers (2014), IoE connects:

people, data, process and things. It is revolutionizing the way we do business, transforming communication, job creation, education and healthcare across the globe. … by 2020, more than 5 billion people will be connected, not to mention 50 billion things. … [With IoE] [p]eople get better access to education, healthcare and other opportunities to improve their lives and have better experiences. Governments can improve how they serve their citizens and businesses can use the information they get from all these new connections to make better decisions, be more productive and innovate faster.

From a recent view of IoE, IoT is “all about connecting objects to the network and enabling them to collect and share data” (Munro, 2017). With the approach of IoT in everyday life (Gasior & Yang, 2013), on battlefields—Internet of Battlefield Things (IoBT), in the medical arena—Internet of Medical Things (IoMT), distributed with sensory networks and cyber-physical systems, and even with device-level intelligence—Internet of Intelligent Things (IoIT) comes a number of issues identified by Moskowitz (2017), which are the explosion of data (e.g., cross-compatible systems; storage locations); security challenges (e.g., adversarial resilience, data exfiltration, covert channels; enterprise protection; privacy); and self-⁎ and autonomic behaviors, and the risks to users, teams, enterprises, and institutions. As presently conceived, “Humans will often be the integral parts of the IoT system” (Stankovic, 2014, p. 4). For IoE, IoT, IoBT, IoMT, IoIT, and so on, and so on, will manifest as heterogeneous and potentially self-organizing complex-systems that define human processes, requiring interoperability, just-in-time (JIT) human interactions, and the orchestration of local-adaptation functionalities as these “things” attempt to achieve human objectives and goals (Suri et al., 2016). IoE is already impacting industry, too: the Industrial Internet of Things (IIoT).²

Presently, there are numerous practical considerations: whatever the systems used for the benefits afforded, each one must be robust to interruption and failure, and resilient to every possible perturbation from wear and tear in daily use. For system-wide failures, a system must have manual control backups; user-friendly methods for joining and leaving networks; autonomous updates and backups; and autonomous hardware updates (e.g., the list is similar to re-ordering inventory or goods automatically after a sale event or in anticipation of scheduled events by a large retailer like Amazon, Wal-Mart, or Target). A system must also provide forensic evidence in the event of mishaps, not only with onboard backups, but also with automatic backups to the cloud.

For new and future systems, there are several other questions that must be addressed: Will systems communicate with each other or be independent actors? Will humans always need to be in the loop? Will systems communicate only with human users, or also with robot and machine users?

For future systems, we are also interested in what may happen when these “things” begin to “think.” Foreseeing something like the arrival of the IoE, Gershenfeld (1999, pp. 8, 10), the former Director of the MIT Media Lab,³ predicted that when a digital system:

has an identity, knowing something about our environment, and being able to communicate … [we will need] components that can work together and change … [to produce a] digital evolution so that the digital world merges with the physical world.

Gershenfeld helped us to link our AAAI symposium with our past symposia on using artificial intelligence (AI) to reduce human errors.⁴ Intelligence is perceived to be a critical factor in overcoming barriers in order to direct maximum entropy production (MEP) to solve difficult problems (Martyushev, 2013; Wissner-Gross & Freer, 2013). But intelligence may also save lives. For example, a fighter plane can already take control and save itself if its fighter pilot loses consciousness during a high-g maneuver. We had proposed in 2016 that with existing technology, the passengers aboard Germanwings Flight 9525 might have been saved had the airliner safely secured itself by isolating the copilot who committed murder and suicide to kill all aboard (Lawless, 2016). Similarly, when the Amtrak train derailed in 2015 from the loss of awareness of its head engineer loss of life could have been avoided had the train taken control until it or its central authority could affect a safe termination (NTSB, 2016); similarly for the memory lapse experienced by the well-trained and experienced engineer who simply failed to heed the speed limit approaching a curve, killing three and injuring multiple others (NTSB, 2018).

Gershenfeld’s evolution may arrive when intelligent “things” and humans team together as part of a “collective intelligence” to solve problems and to save lives (Goldberg, 2017). But autonomy is turning out to be more difficult than expected based strictly on engineering principles alone (e.g., for driverless cars, see Niedermeyer, 2018). Researchers involved with the IoE must not only advance the present state of these “things,” but also address how they think that the science of “collective intelligence” may afford the next evolution of society.

1.2 Introductions to the Technical Chapters

The first research chapter, Chapter 2, titled “Uncertainty Quantification in the Internet of Battlefield Things,” was authored by Brian Jalaian⁵ and Stephen Russell. The authors are scientists at the U.S. Army Research Laboratory in Adelphi, MD. Their focus in this chapter is on mixed technologies that must be fully integrated for maximum military effect in the field (i.e., technologies built by different businesses at different times for different purposes must somehow become integrated to work seamlessly; e.g., recently the Army was successful in integrating multiple technologies for National Defense (Freedberg, 2018)). They begin their chapter by reviewing a wide range of advances in recent technologies for IoT, not only for commercial applications, but also their present and future use in military applications that are now evolving into IoBT. From their perspective, the authors believe that IoBT must be capable of not only working with mixed commercial and military technologies, but also leveraging them for maximum effect and advantage against opponents in the field. These applications in the field present several operational challenges in tactical environments, which the authors review along with the proposed solutions that they offer. Unlike commercial applications, the IoBT challenges for the army include limitations on bandwidth and interruptions in network connectivity, intermittent or specialized functionality, and network geographies that vary considerably over space and time. In contrast to IoT devices’ common use in commercial and industrial systems, army operational constraints make the use of the cloud impractical for IoBT systems today. However, while cloud use in the field is impractical now, the army’s significant success with an integrated mission command network (e.g., NOC, 2018) is an encouraging sign and motivation for the research proposed by Jalaian and his coauthor. The authors also discuss how machine learning and AI are intrinsic and essential to IoBT for the decision-making problems that arise in underlying control, communication, and networking functions within the IoBT infrastructure, as well as higher-order IoBT applications such as surveillance and tracking. In this chapter they highlight the research challenges on the path towards providing quantitative intelligence services in IoBT networked infrastructures. Specifically, they focus on uncertainty quantification for machine learning and AI within IoBT, which is critically essential to provide accurate predictive output errors and precise solutions. They conclude that uncertainty quantification in IoBT workflows enables risk-aware decision making and control for subsequent intelligent systems and/or humans within the information-analytical pipeline. The authors propose potential solutions to address these challenges (e.g., machine learning, statistical learning, stochastic optimization, generalized linear models, inference, etc.); what they hope is fertile ground to encourage more research for themselves and by others in the mathematical underpinnings of quantitative intelligence for IoBT in resource-constrained tactical networks. The authors provide an excellent technical introduction to the IoT and its evolution into the IoBT for field use by the US army. The authors are working at the cutting edge of technological applications for use in the field under circumstances that combine uncertainty with widely varying conditions, and in a highly dynamic application space.

Chapter 3, titled “Intelligent Autonomous Things on the Battlefield,” was written by Alexander Kott⁶ and Ethan Stump, both with the U.S. Army Research Laboratory in Adelphi, MD. Kott is the Chief Scientist in ARL’s laboratory and Stump is a robotics sc...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Chapter 1: Introduction
• Chapter 2: Uncertainty Quantification in Internet of Battlefield Things
• Chapter 3: Intelligent Autonomous Things on the Battlefield
• Chapter 4: Active Inference in Multiagent Systems: Context-Driven Collaboration and Decentralized Purpose-Driven Team Adaptation
• Chapter 5: Policy Issues Regarding Implementations of Cyber Attack: Resilience Solutions for Cyber Physical Systems
• Chapter 6: Trust and Human-Machine Teaming: A Qualitative Study
• Chapter 7: The Web of Smart Entities—Aspects of a Theory of the Next Generation of the Internet of Things
• Chapter 8: Raising Them Right: AI and the Internet of Big Things
• Chapter 9: The Value of Information and the Internet of Things
• Chapter 10: Would IOET Make Economics More Neoclassical or More Behavioral? Richard Thaler’s Prediction, a Revisit
• Chapter 11: Accessing Validity of Argumentation of Agents of the Internet of Everything
• Chapter 12: Distributed Autonomous Energy Organizations: Next-Generation Blockchain Applications for Energy Infrastructure
• Chapter 13: Compositional Models for Complex Systems
• Chapter 14: Meta-Agents: Using Multi-Agent Networks to Manage Dynamic Changes in the Internet of Things
• Index