IoT for Defense and National Security E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
IoT for Defense and National Security Practical case-based guide illustrating the challenges and solutions of adopting IoT in both secure and hostile environments IoT for Defense and National Security covers topics on IoT security, architecture, robotics, sensing, policy, operations, and more, including the latest results from the premier IoT research initiative of the U.S. Defense Department, the Internet of Battle Things. The text also discusses challenges in converting defense industrial operations to IoT and summarizes policy recommendations for regulating government use of IoT in free societies. As a modern reference, this book covers multiple technologies in IoT including survivable tactical IoT using content-based routing, mobile ad-hoc networks, and electronically formed beams. Examples of IoT architectures include using KepServerEX for edge connectivity and AWS IoT Core and Amazon S3 for IoT data. To aid in reader comprehension, the text uses case studies illustrating the challenges and solutions for using robotic devices in defense applications, plus case studies on using IoT for a defense industrial base. Written by leading researchers and practitioners of IoT technology for defense and national security, IoT for Defense and National Security also includes information on: * Changes in warfare driven by IoT weapons, logistics, and systems * IoT resource allocation (monitoring existing resources and reallocating them in response to adversarial actions) * Principles of AI-enabled processing for Internet of Battlefield Things, including machine learning and inference * Vulnerabilities in tactical IoT communications, networks, servers and architectures, and strategies for securing them * Adapting rapidly expanding commercial IoT to power IoT for defense For application engineers from defense-related companies as well as managers, policy makers, and academics, IoT for Defense and National Security is a one-of-a-kind resource, providing expansive coverage of an important yet sensitive topic that is often shielded from the public due to classified or restricted distributions.
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Introduction: IoT for Defense and National Security
1 An Introduction to the Topic of IoT for Defense and National Security
2 What Is IoT?
3 Why Is IoT of Great Importance Now?
4 IoT Will Change How War Is Waged and How Peace Is Maintained
5 A Short History of IoT for National Defense
6 Special Challenges for IoT Used for Defense and National Security
7 Commercial Vs. Defense IoT
8 A Guide to This Book
9 This Book Is a Beginning
Section 1: Introduction: Vision, Applications, and Opportunities
1 Internet of Battlefield Things: Challenges, Opportunities, and Emerging Directions
1.1 IoBT Vision
1.2 IoBT vs. IoT
1.3 IoBT Operational Requirements
1.4 An Organizing Concept
1.5 Performant and Resilient IoBTs
1.6 Future Directions
1.7 Conclusion
References
2 Sensorized Warfighter Weapon Platforms: IoT Making the Fog of War Obsolete
2.1 Introduction
2.2 IoT
for Firearms
2.3 New Insights into the Battlefield Provided by IoT
2.4 Challenges for IoT
in Soldier Weapons
2.5 Battlefield Challenges to Aggregating and Exfiltrating Data
2.6 Protection and Security for IoT
Data Communication
2.7 State of the Art
2.8 Conclusion
References
3 IoBT Resource Allocation via Mixed Discrete and Continuous Optimization
3.1 Introduction
3.2 Lattices and Submodular Functions
3.3 Problem Formulation
3.4 An Equivalent Parameterization
3.5 Returning to Constraints
3.6 Computational Examples
3.7 Conclusions
References
Note
4 Operationalizing IoT Data for Defense and National Security
4.1 Introduction
4.2 Problem Statement
4.3 Challenges
4.4 Security Considerations
4.5 Developing a Strategy for Operationalizing Data
4.6 Precedence
4.7 End State
4.8 Conclusion
References
5 Real Time Monitoring of Industrial Machines using AWS IoT
5.1 Problem Statement
5.2 Solution Statement – Overview
5.3 Solution Statement – Edge Computing
5.4 Solution Statement – Cloud Connectivity
5.5 Solution Statement – Streaming Analytics and Data Storage
5.6 Solution Statement – Data Visualization
5.7 Solution Statement – Example Data Visualizations
5.8 Results
5.9 Next Steps
References
6 Challenges and Opportunities of IoT for Defense and National Security Logistics
6.1 Introduction
6.2 Linking Industry and DoD Uses of IoT
6.3 Situational Awareness
6.4 Applications for DoD
6.5 Observations on the Future
Acknowledgement
References
Notes
7 Digital Twins for Warship Systems: Technologies, Applications and Challenges
7.1 Introduction
7.2 A Digital Twin Architecture for Implementation
7.3 Ship Digital Twin Implementation
References
Section 2: Introduction: Artificial Intelligence and IoT for Defense and National Security
8 Principles of Robust Learning and Inference for IoBTs
8.1 Internet of Battlefield Things and Intelligence
8.2 Dimensions of Responsible AI
8.3 Detecting Surprise: Adversarial Defense and Outlier Detection
8.4 Novel Deep Learning Representation: Dynamical System
8.5 Robust Secure State Estimation
8.6 Distributionally Robust Learning
8.7 Future Directions
8.8 Conclusion
References
Note
9 AI at the Edge: Challenges, Applications, and Directions
9.1 Introduction
9.2 IoT Applications
9.3 Distributed AI Architecture
9.4 Technology
9.5 Research Directions
9.6 Conclusions
References
10 AI Enabled Processing of Environmental Sounds in Commercial and Defense Environments
10.1 Introduction
10.2 Use Cases
10.3 System Architecture
10.4 Technology
10.5 Summary
References
Section 3: Introduction: Security, Resiliency, and Technology for Adversarial Environments
11 Assurance by Design for Cyber‐physical Data‐driven Systems
11.1 Introduction
11.2 Methods for Assurance
11.3 Discussion and Conclusion
References
12 Vulnerabilities in IoT Systems
12.1 Introduction
12.2 Firmware
12.3 Communication Protocols
12.4 IoT Apps
12.5 Physical Dependencies
12.6 Companion Mobile Apps
12.7 Hardware
12.8 IoT Platforms
12.9 Countermeasures
12.10 Conclusions
References
13 Intrusion Detection Systems for IoT
13.1 Introduction
13.2 Background
13.3 IoT Attack Scenarios
13.4 Proposed IDSes for IoT
13.5 Research Directions
Acknowledgement
References
Note
14 Bringing Intelligence at the Network Data Plane for Internet of Things Security
14.1 Introduction
14.2 Related Work
14.3 System Design
14.4 Problem Modeling
14.5 Algorithms and Learning Models
14.6 Evaluation Results
14.7 Conclusions and Future Challenges
Acknowledgment
References
Note
15 Distributed Computing for Internet of Things Under Adversarial Environments
15.1 Introduction
15.2 Distributed Computing for IoT in Defense Applications
15.3 Threat Model
15.4 Frameworks for Distributed Computing
15.5 Establishing Trust in Adversarial Environments: Solutions and Open Opportunities
15.6 Summary
Acknowledgment
References
Note
16 Ensuring the Security of Defense IoT Through Automatic Code Generation
16.1 The Challenge of IoT in Defense and National Security Applications: The Challenge
16.2 Solutions
16.3 Automatic Code Generation
16.4 IoT Interface‐code Issuing Authority
16.5 Conclusions
References
Notes
Section 4: Introduction: Communications and Networking
17 Leveraging Commercial Communications for Defense IoT
17.1 Introduction
17.2 Key Differences Between Defense and Commercial Communications Requirements
17.3 Key Differences Between Defense and Commercial Technology Development
17.4 Commercial Communications for Use in Defense and Homeland Security
17.5 Conclusion
References
18 Military IoT: Tactical Edge Clouds for Content Sharing Across Heterogeneous Networks
18.1 Introduction
18.2 The Need for Tactical Edge Clouds
18.3 Two Architectures
18.4 Tactical Edge Cloud Architectural Insights
18.5 Summary
Acknowledgment
References
19 Spectrum Challenges in the Internet of Things: State of the Art and Next Steps
19.1 Introduction
19.2 Spectrum Bands of Interest in the Internet of Things
19.3 Spectrum Management in the Internet of Things: Requirements and Existing Work
19.4 Spectrum Management in the Internet of Things: The Way Ahead
19.5 Conclusions
References
Note
20 Tactical Edge IoT in Defense and National Security
20.1 Introduction
20.2 Background
20.3 Compelling COTS Edge IoT Applications
20.4 Target Scenarios for Tactical Edge IoT
20.5 Communications Architecture
20.6 Main Challenges and Recommendations
20.7 Conclusions
Acknowledgments
References
21 Use and Abuse of IoT: Challenges and Recommendations
21.1 The Elements of IoT and Their Nature
21.2 Preventing the Abuse of IoT While Enabling Its Benefits
21.3 Types of Abuse and Misuse, and Prevention Through Regulation
21.4 Concluding Remarks: A Call to Action
References
Index
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Resume of main communication technologies capacities at physical ...
Chapter 12
Table 12.1 IoT components and typical vulnerabilities/threats.
Table 12.2 Specs of common wireless protocols for IoT devices.
Chapter 13
Table 13.1 IDS/IPS systems proposed in between 2016 and 2021.
Chapter 14
Table 14.1 We evaluate our solution using four different datasets, represen...
Table 14.2 Performance metrics of the Dilated CNN on ISCX dataset.
Table 14.3 Performance metrics of the Dilated CNN on CICAAFM dataset.
Table 14.4 Performance metrics of the Dilated CNN on other datasets.
Table 14.5 Comparisons between the proposed algorithm and random selected h...
Table 14.6 Performance metrics of BNN on CICIDS2017 dataset.
Table 14.7 Performance metrics of BNN on ISCX dataset.
Chapter 21
Table 21.1 Types of potential misuse and abuse of IoT are constrained by va...
List of Illustrations
Chapter 1
Figure 1.1 Examples of resilient and performant capabilities that are desira...
Figure 1.2 A depiction of the seven stages represented in the multi‐domain o...
Chapter 2
Figure 2.1 ARC concept of the connected battlefield.
Figure 2.2 Gas overcharging of weapons can lead to degradation of weapon per...
Figure 2.3 ARC IoT enabling top‐down battlefield insights and targeting for ...
Chapter 3
Figure 3.1 Unconstrained optimization problem results. The true signal and i...
Figure 3.2 Results of the portfolio optimization problem. (a) The values of ...
Figure 3.3 Results of the portfolio optimization problems. (a) The values of...
Chapter 4
Figure 4.1 Current state: supply chain and military campaign.
Figure 4.2 Current state: supply chain and military campaign (OODA included)...
Figure 4.3 Standards based digital threads.
Figure 4.4 Future state: supply chain and military campaign (OODA included)....
Chapter 5
Figure 5.1 Edge computing is a distributed computing paradigm that brings co...
Figure 5.2 Cloud connectivity from edge computing to cloud services for on‐d...
Figure 5.3 Processing of IoT data for streaming event‐based analytics and st...
Figure 5.4 Data visualization that graphically represents the data (usually ...
Figure 5.5 Provides an example data visualization using Tableau to show the ...
Figure 5.6 Developing a digital twin or virtual representation of industrial...
Chapter 6
Figure 6.1 IoT in the battlefield configuration to demonstrate the diversity...
Chapter 7
Figure 7.1 General schema of a sensor node main components.
Figure 7.2 Schema of communication protocols for digital twin.
Figure 7.3 Description of the main components in a digital twin architecture...
Figure 7.4 Scheme of information sources and data flow to the data lake.
Figure 7.5 Diagram with the phases of the ETL process.
Figure 7.6 Use of communication protocols for sensors based on distance rang...
Chapter 9
Figure 9.1 Examples of roaming IoT devices (RIDs).
Figure 9.2 Examples of analog pressure gauges.
Figure 9.3 Internet of things at the edge.
Figure 9.4 Centralized AI paradigm, centralized application and centralized ...
Figure 9.5 Edge AI paradigm, distributed application and centralized data.
Figure 9.6 Distributed AI paradigm, distributed application and distributed ...
Figure 9.7 Data summarization techniques.
Figure 9.8 The distribution of reconstruction errors for MNIST testing data ...
Figure 9.9 The framework of NeuralFP.
Figure 9.10 AUCs of detecting various OOD records.
Figure 9.11 Pruning Inception V3 and Yolo V3 for Spot; Gen. Intel Core i5...
Figure 9.12 Example of a federated learning system.
Figure 9.13 Training pipeline for context‐based multi‐modal sensing.
Figure 9.14 Inference pipeline for context‐based multi‐modal sensing.
Figure 9.15 Pipeline for adaptive navigation for optimized sensing.
Chapter 10
Figure 10.1 System overview – a system for edge data inferencing and actuati...
Figure 10.2 System architecture – edge‐ and cloud‐based services to enable a...
Figure 10.3 Model development – an iterative process to create a robust mode...
Figure 10.4 Assisted labeling – using existing labeled data to guide labelin...
Figure 10.5 Data visualization techniques – ontology browsing.
Figure 10.6 Feature extraction and processing pipeline – processing of audio...
Figure 10.7 Example waveform, its spectrogram and labels.
Figure 10.8 Illustration of basic recipe for machine learning.
Figure 10.9 Illustration of Gaussian mixture models (GMMs).
Figure 10.10 Illustration of typical CNN model.
Figure 10.11 Execution time of inference on RaspberryPi 3B and 4B for 1 seco...
Figure 10.12 Illustration of anomaly detector advantage. (a) Training and te...
Figure 10.13 Reverse testing accuracy on training data – using training data...
Figure 10.14 Model adaptation using a second tier for labeling.
Figure 10.15 Environment transfer for acoustic model adaptation: the environ...
Chapter 11
Figure 11.1 An overview of a Network‐of‐networks CPDDS.
Figure 11.2 Evolution of the Age metric. Here
X
is the inter‐arrival time,
Y
Figure 11.3 High Consequence Scenario Generation Modeling Framework for CPDD...
Figure 11.4 CPDDS Resilience Framing Illustration. Four phases of system res...
Chapter 12
Figure 12.1 Typical IoT system and its components. IoT devices can sense env...
Figure 12.2 Mirai botnet formation and DDoS attack.
Figure 12.3 Zigbee protocol stack. The blue boxes are defined by Zigbee stan...
Figure 12.4 App1: If it's 11 p.m., and the hallway light is off, then turn o...
Chapter 14
Figure 14.1 Network‐layer security approaches such as firewalls deployed at ...
Figure 14.2 A learning model based on P4 can be more flexible to handle hete...
Figure 14.3 P4 language has protocol independence and reconfigurability.
Figure 14.4 Both the control and data planes are programmable in the propose...
Figure 14.5 Compared with the FRG framework, the BNN approach further offloa...
Figure 14.6 Illustration of the proposed two‐stage learning approach. Packet...
Figure 14.7 Structure of the dilated convolutional neural network (Dilated C...
Figure 14.8 A tradeoff between precision and recall rates can be achieved by...
Figure 14.9 Importance scores have distinct distributions in different proto...
Figure 14.10 Selecting longer or more header fields increases the accuracy a...
Figure 14.11 With only a small number of header fields used, the throughput ...
Figure 14.12 To reduce false negatives of BNN inference, a tradeoff can be a...
Chapter 15
Figure 15.1 Internet of Battlefield and first responder applications operate...
Figure 15.2 IoBT Applications are Opportunistic. Devices come in contact wit...
Figure 15.3 The ownership modalities for edge computing. Different colors he...
Figure 15.4 The architecture of Jupiter, an orchestration system for dispers...
Figure 15.5 Overview of gathering resources in adversarial environments. Res...
Figure 15.6 A Functional View of the proposed Byzantine Fault Tolerant Jupit...
Chapter 16
Figure 16.1 The number of weaknesses is two orders of magnitude less than th...
Figure 16.2: Automatic code generation conceptual architecture.
Figure 16.3 Manual vs. automatic generation of network element software.
Chapter 17
Figure 17.1 The cellular industry has gone through five generations in the 3...
Figure 17.2 DoD 5000 was updated in 2020 to accelerate the acquisition proce...
Figure 17.3 Opportunities for leveraging commercial technologies are depende...
Chapter 18
Figure 18.1 The OODA Loop is shown as a coil progressing through time, where...
Figure 18.2 CBMEN Program Insight: the conventional approach requires reach‐...
Figure 18.3 The CBMEN Node Architecture supports legacy and new application,...
Figure 18.4 DARPA DyNAMO information overlay cloud.
Figure 18.5 DyNAMO's Information Gateway and Network Optimizer work together...
Figure 18.6 Three purposes of a knowledge fabric.
Chapter 19
Figure 19.1 Spectrum “holes” during an LTE transmission may be leveraged to ...
Figure 19.2 Radio fingerprinting in wideband spectrum portions.
Figure 19.3 Spectrum sharing research in Colosseum.
Figure 19.4 Adversarial Machine Learning (AML) in a Wireless Context.
Figure 19.5 Federated Machine Learning (FML) for IoT ML Robustness Sensing....
Figure 19.6 Test set accuracy of a CNN bootstrapped with noise‐based data au...
Chapter 20
Figure 20.1 Overview of the main PS organizations, their responsibilities, a...
Figure 20.2 Main target scenarios for Tactical Edge IoT in defense and publi...
Figure 20.3 Communications architecture of a Tactical Edge IoT system.
Figure 20.4 Ongoing Tactical Edge IoT research.
Chapter 21
Figure 21.1 IoT represents a revolution in automated control of our world. I...
Figure 21.2 A piece of the Berlin Wall inscribed in Spanish: “Happy 1984,” a...
Figure 21.3 China reportedly has the greatest number of networked video surv...
Figure 21.4 Radars can see through forest canopies (a) and inside buildings ...
Figure 21.5 By 1997 DARPA, an agency of the US Defense Department, demonstra...
Figure 21.6 Self‐driving cars are potent IoT systems that map and characteri...
Figure 21.7 Snapshot of densities of mobile phones detected at the headquart...
Figure 21.8 The New York Times compiled a “diary” of the US President's move...
Figure 21.9 The Global Hawk (US RQ‐4) flies 30‐h surveillance missions. IoT ...
Figure 21.10 Soldier launching a Switchblade 300 loitering attack drone. The...
Figure 21.11 An Israeli human‐on‐the‐loop IoT system, like this Samson Remot...
Figure 21.12 A framework for channeling IoT for the greater good while preve...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright
List of Contributors
Introduction: IoT for Defense and National Security
Begin Reading
Index
Wiley End User License Agreement
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IoT for Defense and National Security
Edited by
Robert DouglassAlta Montes, Inc., USA
Keith GrembanUniversity of Colorado School of Law, Boulder, CO, USA
Ananthram SwamiArmy Research Laboratory, Adelphi, MD, USA
Stephan GeraliLockheed Martin, Inc., Bethesda, MD, USA
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List of Contributors
Tarek Abdelzaher
Department of Electrical and Computer Engineering
University of Illinois at Urbana‐Champaign
Urbana
IL
USA
Jae‐wook Ahn
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Jonathan Ashdown
Air Force Research Laboratory
Rome
NY
USA
Michael Atighetchi
Raytheon BBN
Cambridge
MA
USA
Nathaniel D. Bastian
Army Cyber Institute
United States Military Academy
West Point
NY
USA
Luis Bathen
IBM Research – Almaden
IBM
San Jose
CA
USA
Christina Baxter
Emergency Response TIPS
LLC
Melbourne Beach
FL
USA
Gisele Bennett
MEPSS LLC
Indian Harbour Beach
FL
USA
Elisa Bertino
Department of Computer Science
Purdue University
West Lafayette
IN
USA
Ramesh Bharadwaj
Information Technology Division
U.S. Naval Research Laboratory
Washington
DC
USA
Stephane Blais
Raytheon BBN
Cambridge
MA
USA
Kyle Broadway
Chief Technology Officer
Armaments Research Company
University of Missouri
Columbia
MO
USA
and
Johns Hopkins University
Baltimore
USA
Jonathan Bunton
Department of Electrical and Computer Engineering
University of California
Los Angeles
CA
USA
Seraphin Calo
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Armando Caro
Raytheon BBN
Cambridge
MA
USA
Samrat Chatterjee
Data Sciences & Machine Intelligence Group
Pacific Northwest National Laboratory
Richland
WA
USA
Satish Chikkagoudar
Information Technology Division
U.S. Naval Research Laboratory
Washington
DC
USA
Shyama Prosad Chowdhury
IBM GBS
IBM India
Kolkata
WB
India
Dan Coffin
Raytheon BBN
Cambridge
MA
USA
William Crowder
Logistics Management Institute
Tyson
VA
USA
Nirmit Desai
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Vicente Diaz‐Casas
Grupo Integrado de IngenierÍa
CITENI
Campus Industrial de Ferrol
Universidade da Coruña
Ferrol
Spain
Robert Douglass
Alta Montes
Sandy
UT
USA
M. Douglas Williams
Seed Innovations
Colorado Springs
CO
USA
Zheng Fang
Department of Computer Science
University of California
Davis
CA
USA
Tiago M. Fernández‐Caramés
Department of Computer Engineering
CITIC Research Center
Universidade da Coruña
A Coruña
Spain
Paula Fraga‐Lamas
Department of Computer Engineering
CITIC Research Center
Universidade da Coruña
A Coruña
Spain
Auroop Ganguly
Department of Civil & Environmental Engineering
Northeastern University
Boston
MA
USA
Luis A. Garcia
Information Sciences Institute
University of Southern California
Marina Del Rey
CA
USA
Stephan Gerali
Enterprise Operations
Lockheed Martin Corporation
Bethesda
MD
USA
Keith Gremban
Ann and H.J. Smead Aerospace Engineering Sciences and Silicon Flatirons Center
University of Colorado Boulder
Boulder
CO
USA
Keith Grueneberg
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Marcos Míguez González
Grupo Integrado de IngenierÍa
CITENI
Campus Industrial de Ferrol
Universidade da Coruña
Ferrol
Spain
Sara Ferreno‐Gonzalez
Grupo Integrado de IngenierÍa
CITENI
Campus Industrial de Ferrol
Universidade da Coruña
Ferrol
Spain
Nancy Greco
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Tadanobu Inoue
IBM Research
IBM Japan
Chuo‐ku
Tokyo
Japan
Susmit Jha
Neuro‐symbolic Computing and Intelligence
CSL
SRI International
Menlo Park
CA
USA
Dhiraj Joshi
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Joud Khoury
Raytheon BBN
Cambridge
MA
USA
Paul J. Kolodzy
Kolodzy Consulting, LLC
Falls Church
VA
USA
Sastry Kompella
Information Technology Division
U.S. Naval Research Laboratory
Washington
DC
USA
Bhaskar Krishnamachari
Department of Electrical and Computer Engineering
University of Southern California
Los Angeles
CA
USA
Hyunwoo Lee
Department of Computer Science
Purdue University
West Lafayette
IN
USA
Wei‐Han Lee
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Ninghui Li
Department of Computer Science
Purdue University
West Lafayette
IN
USA
Tommaso Melodia
Department of Electrical and Computer Engineering
Northeastern University
Boston
MA
USA
Steve Morgan
Chief Technology Office
Raft LLC
Herndon
VA
USA
Prasant Mohapatra
Department of Computer Science
University of California
Davis
CA
USA
Anand Mudgerikar
Department of Computer Science
Purdue University
West Lafayette
IN
USA
Alicia Munin‐Doce
Grupo Integrado de IngenierÍa
CITENI
Campus Industrial de Ferrol
Universidade da Coruña
Ferrol
Spain
Sam Nelson
Raytheon BBN
Cambridge
MA
USA
Konstantinos Poularakis
Department of Electrical Engineering & Institute for Network Science
Yale University
New Haven
CT
USA
Francesco Restuccia
Department of Electrical and Computer Engineering
Northeastern University
Boston
MA
USA
Stephen Russell
Department of Research Opportunities and Innovation
Jackson Health System
Miami
FL
USA
Qiaofeng Qin
Department of Electrical Engineering & Institute for Network Science
Yale University
New Haven
CT
USA
Gowri Sankar Ramachandran
School of Computer Science
Queensland University of Technology
Brisbane
Queensland
Australia
Lucía Santiago Caamaño
Grupo Integrado de IngenierÍa
CITENI
Campus Industrial de Ferrol
Universidade da Coruña
Ferrol
Spain
Tim Strayer
Raytheon BBN
Cambridge
MA
USA
Ananthram Swami
U.S. Army DEVCOM Army Research Laboratory
U.S. Army Futures Command
Adelphi
MD
USA
Paulo Tabuada
Department of Electrical and Computer Engineering
University of California
Los Angeles
CA
USA
Leandros Tassiulas
Department of Electrical Engineering & Institute for Network Science
Yale University
New Haven
CT
USA
Bishal Thapa
Raytheon BBN
Cambridge
MA
USA
Darlene Thorsen
Data Sciences & Machine Intelligence Group
Pacific Northwest National Laboratory
Richland
WA
USA
Venugopal Veeravalli
ECE Department
University of Illinois at Urbana‐Champaign
Champaign
IL
USA
Dinesh Verma
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Gunjan Verma
U.S. Army DEVCOM Army Research Laboratory
U.S. Army Futures Command
Austin
TX
USA
Shiqiang Wang
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Jaime Wightman
Chief Data and Analytics Office
Lockheed Martin Corporation
Bethesda
MD
USA
Maggie Wigness
U.S. Army DEVCOM Army Research Laboratory
U.S. Army Futures Command
Adelphi
MD
USA
David Wood
IBM Thomas J. Watson Research Center
Yorktown Heights
New York
USA
Introduction: IoT for Defense and National Security
Robert Douglass
Alta Montes, Inc., Sandy, Utah, USA
1 An Introduction to the Topic of IoT for Defense and National Security
The Internet of Things, IoT, connects physical objects through digital networks. It fuses sensors, processors, data storage, smart algorithms, and actuators to observe and physically alter the world and the people in it. In the winter of 2022, IoT weapons wielded by badly outnumbered but determined and courageous Ukrainian light infantry won the battle of Kyiv, destroying hundreds of Russian tanks and perhaps thousands of military vehicles along with the Russian soldiers in them. Javelin anti‐tank missiles and Switchblade loitering missiles were instrumental in securing Ukraine's victory. Javelins are nascent IoT weapons while Switchblade missiles are quintessential IoT systems. They move their human operators back from the most hazardous combat zones. They operate with a diverse collection of sensors and actuators, wired and wireless tied together. They coordinate and share their attack across a network connecting reconnaissance drones, command nodes, and other soldiers. They function both as weapons receiving off‐board intelligence and as a source of intelligence. They put soldiers in top‐level supervisory control while moving them out of the real‐time control loop, allowing their weapons to find, track, attack, and destroy their targets in a highly autonomous manner. They turbocharge the tempo of combat. They dispel the fog of war. They save the lives of their operators. In the battle of Kyiv, they help win the fight against long odds and an overwhelming weight of armor.
IoT is not new. More than half a century ago, the internet protocols were conceived as the glue of an IoT system, one that would connect early warning sensors detecting incoming intercontinental missiles with command nodes and systems of response. While the first internet was not ultimately used for this application, its rationale was clearly the creation of a network of systems to sense and respond in a nation's defense – it was conceived as an IoT system of systems for war. Realizing the power of IoT required decades of maturation in five core technologies: sensors, wireless networking and communication, cloud or distributed computing, intelligent algorithms, and digitally controlled actuators. Like the Internet itself, defense investment largely invented these technologies and their underlying concepts while the much greater commercial market powered their expanded performance and reduced their size, weight, power, and especially cost. These technologies continue to advance, but they already enable emerging IoT systems to realize a vision anticipated for decades. As described below, IoT will revolutionize warfare. It will revolutionize how nations secure themselves and maintain peace. The technology is already doing so, although it is not often referred to as IoT. Despite their impact to date and their still greater future promise, IoT systems used for military affairs still face many challenges – challenges not faced or faced to the same degree by commercial IoT.
In the commercial sector, hundreds of books have been written on IoT. Arguably IoT technology impacts defense and national security more than any commercial domain; however, not a single book exists on the topic. The subject is shrouded in secrecy and government restrictions, isolating it from the public realm. This Book provides a first look at what can be released. Leading scientists and technologists describe their latest research results. They address a wide range of topics including IoT security in hostile environments, artificial intelligence (AI) in IoT for defense, and tactical networks in a disrupted, intermittent, and limited‐bandwidth battlefield. For example, this Book explains that in such a world IoT can be sustained in the cauldron of combat using content‐based routing, configured with mobile ad‐hoc networks that ride jam‐defiant and intercept‐resistant electronically formed beams.
This book offers solutions to special challenges of IoT for defense that set it apart from the commercial environment. It enumerates some of the outstanding and unsolved technical problems. It looks at several different visions for the future of IoT ranging from IoT‐enabled rifles to entire logistics systems powered by IoT. It provides several case studies by practitioners in the field from defense manufacturing to the design of warships. It provides a roadmap for policy and regulation of government use of IoT. This Book in no way purports to be a comprehensive review of IoT for defense, for two reasons. First, no one volume can span such an extensive domain. Second, governments sequester much of the material. This book presents an introduction to the subject – a sampling of its many aspects. The public remains largely unaware of these issues, but they are critically important to governments and individuals who are protected by IoT systems and in places oppressed by them.
Sections 1 to 4 of this Book assume some prior knowledge of IoT. For IoT neophytes or those who want more background and context on the elements of IoT for defense and national security, the first part of Chapter 21 provides that introduction, and the beginner might want to start there. Section 1 of this Book presents a sampling of challenges, applications, and opportunities for IoT used for defense. Section 2 reviews the role of AI in IoT for defense and addresses selected case studies and key challenges associated with AI‐based IoT. Section 3 discusses security issues and solutions for operating distributed IoT networks in adversarial environments. Section 4 addresses the key challenge of providing IoT systems with reliable communications and networking in mobile, dynamic, and hostile environments. It highlights differing requirements between defense and commercial IoT and suggest how one might build on the other. The final Chapter, 21, addresses issues of regulating IoT to advance its use while blocking its misuse and abuse.
2 What Is IoT?
As a revolution in technology, IoT rivals the Internet on which it depends. It is not just another application riding on the Internet, but a fundamental advance in technology. It can automate our world. IoT senses the world, analyzes the data in the light of mission requirements, and then takes actions that affect the physical world. This is unique – closing the loop automatically in the physical world. The only similar technology consists of control systems. In some sense, IoT is a control system for the world. IoT combines three elements – sensing and information extraction, processing, and action. These elements ride on top of digital communication and networking, an infrastructure that has become cheaper, smaller, and ever greater in capacity, especially with the advent of 5G wireless technology. IoT goes beyond sensing and processing information. It fuses the technology that can take control and modify our physical world. Society‐altering consequences, both good and bad, will flow as IoT advances. IoT can leverage human senses, thought, and power, putting people on top of the control loop rather than in the middle of it. Alternatively, IoT can displace human control. Perceiving, formulating a response, and acting to achieve a desired goal are some of the hallmarks of sentient beings. But IoT capabilities extend beyond human senses and exceed our manual powers. Its ability to plan actions already surpasses human performance in some domains.
IoT uses distributed sensors, databases, digital documents, and other software applications to extract information from multiple sources across time and space. Extracted information can be processed locally or by processors geographically dispersed across a cloud of computing resources. Based on what its sensors perceive, IoT processors make decisions and plan actions. The processing can be as simple as assessing a single sensor value, such as a temperature on a thermostat, or it may consist of sophisticated AI algorithms that recognize people, places, and events. IoT actuators carry out plans immediately or alternately synchronized them over time using remote devices that physically alter the environment. IoT's actuators may be as trivial as turning on a remote smoke alarm or they may aim a weapon to track and kill a person that IoT has identified. The actuators can operate on scales both far smaller and far larger than human actions. Human operators may be in the loop of IoT actions but need not be. We will want to retain supervisory control and oversight.
IoT endows us with countless beneficial advances, many not yet envisioned. But when misused, IoT becomes the ultimate tool of authoritative regimes for pervasive surveillance and automatic control of their citizens – a tool which can easily destroy free societies. Some nations are already using the power of IoT to help suppress terrorism, protect their security, and enforce the rule of law. Other nations are using it to suppress all opposition to the ruling government. The public and policy makers must understand both the potential and the dangers that extensive IoT networks pose for democracies. They must find a way to regulate IoT to advance the benefits for defense while protecting against misuse and abuse.
3 Why Is IoT of Great Importance Now?
All three of IoT's key elements exist today: sensors/info‐extraction, intelligent processing, and network‐enabled actuators. The infrastructure that supports these elements also already exists, such as high‐bandwidth wireless networking and cloud and edge computing. The concept of integrating these elements into a an IoT system is not new, as noted above. What is new now is the maturity of the core components that make up each of these elements. Many of these elements were invented by the US Department of Defense and other governments but matured in the commercial domain. Their maturation creates an inflection point in IoT. The key components now powering IoT networks, can be summarized in the following list. Many of these topics are addressed in the Chapters of this book.
Communications
: Wireless communication provides high bandwidth, low‐latency, and near universal availability via 5G, WiFi, Bluetooth, ZigBee, and other standards. Performance surpasses what was available from wired networks just a decade or two ago.
Networking
: The continuing reductions in cost, size, weight, and power of network interfaces embedded in devices creates an explosion of devices and sensors that can interact with one another. Widely used network standards supports interoperability across different manufactures of IoT devices. For example, China announced plans in 2021 to network together different sensor systems observing all public spaces in the nation. In private residences Amazon's Echo ties together many devices and appliances made by many different companies.
Processing Power and Storage
: The ongoing operation of Moore's law creates processor performance capable of digesting ”big data” and training and executing intelligent algorithms, such as deep neural networks. As an example, a single mobile phone contains as much storage and processing power as any supercomputer just a few decades ago.
Distributed Processing
: Cloud, fog, and edge computing makes massive computing power available to IoT devices without having to embed it at every node of an IoT network.
Security:
The understanding of the digital vulnerabilities of IoT and how to protect against them has expanded in both commercial and defense enterprises. However, this understanding is occasionally ignored in defense practice and routinely ignored in commercial applications. Anti‐tamper techniques developed and applied in both domains of defense (increasingly) and commercial (selectively) add protection from physical attempts to coopt devices and software.
Localization and Common Timing:
Proliferation of geolocating satellite systems and small, low‐cost receivers, and techniques such as Bluetooth beacons make it possible for most devices to locate themselves precisely and continuously. They also allow IoT devices to coordinate on precise timelines by sharing a common timing framework. People who carry devices like cell phones or fitness trackers or who drive a recent model of car can be tracked continuously and precisely. For example, one commercial fitness‐tracker aggregates individuals' locations, revealing the location of individual soldiers as well as secret intelligence facilities.
Big Data and AI Algorithms
: Machine‐learning/AI algorithms demonstrate increasing sophistication and human‐like intelligence in such activities as pattern recognition, tracking, language understanding, and autonomous control. As an example, a simulated aircraft controlled by an AI algorithm recently defeated a top human fighter pilot in an air‐to‐air simulated dog fight, six out of six times.
Science Magazine
, the journal of the American Association for the Advancement of Science, called AI's ability to predict protein folding the most important scientific innovation of 2021.
Digital Sensors
: Video cameras, infra‐red cameras, accelerometers, health status monitors, lidars, and other sensors have dropped dramatically in size, weight, and power as well as in cost while at the same time improving sensor quality. Many sensors now have integrated position, timing, and network interfaces. For example, one of the first lidars (a type of 3D imaging sensor) cost the US Defense Department approximately $690,000 in 1985 in inflation‐adjusted dollars and weighed about 50 pounds. A lidar costs less than one 10,000th of that amount today and can fit inside a smart phone. Today's version provides both higher resolution and faster framerates.
Digital Information Extraction
: The past two decades have seen an explosion of information exfiltrated from software applications. As an example, one information aggregator combines the detailed smart phone location data reported from over 80,000 phone apps residing on millions of phones. By combining information from different data bases, supposedly anonymous tracks can easily be associated with named individuals. Purchasing a week's worth of such aggregated data allowed the New York Times to locate and track the US President through his daily movements.
Actuators
: The size, weight, power, and cost of many actuators have declined, sometimes dramatically, while the types of action and control of actuation have expanded. The scale of commercial markets supports this advance by funding the necessary non‐recurring engineering. Specific advances include newly available materials or cost reductions in specialized materials (e.g. carbon‐fiber composites, titanium, printable metals). New possibilities for IoT arise from 3D printers,
micro‐electro‐mechanical systems
(
MEMS
), and actuators with embedded Bluetooth or other wireless network interfaces. MEMS‐based accelerometers tied to micro electro‐mechanical actuators control the flight of many unmanned aerial vehicles as well as enable motion tracking and haptic interaction in smart watches and fitness devices.
Widely Adopted Standards
: Shared standards for communications and networking, widely adopted by commercial product and service providers, expand their markets globally and support the interaction of thousands of devices and applications. Examples of standards driving IoT include 5G and Bluetooth in the communications realm, Amazon's Ring and Alphabet's Nest device‐interface standards, and Apple's IoS and Google's Android operating systems and application environments.
Smart Phones as a Common Human‐machine Interface:
Several billion people use one of two types of smart phones. These smart phones provide a common hardware and software platform hosting thousands of applications. The two platforms provide human‐interface environments and infrastructure, rich in hardware and media for human interaction. They form ubiquitous human‐supervisory control nodes for IoT devices and networks. The use of common human‐interface conventions, although they fall short of standards, helps users move quickly from a known application to a new one. They reduce training and learning time and expand market sizes. When combined with MEMS sensors and new displays, they make virtual‐reality a reality through IoT systems such as Occulus.
Successful IoT Applications:
IoT systems are rapidly expanding in popularity and use across the developed world, demonstrating the power of IoT and increasing trust in new applications and automation that amplifies human potential. For example, Tesla cars and Ring security networks are two such IoT systems gaining popularity and acceptance. Automotive IoT now provides warnings, detects moving obstacles, and in constrained situations even becomes the driver. Digital home assistants, such as Amazon's Alexa, tie microphones to cloud processors running AI algorithms to understand spoken language and control many home devices. In defense and national security, emerging weapons and intelligence systems include IoT technology, such as tracking supplies, semi‐autonomous missiles, drones, and surveillance networks. Existing IoT applications demonstrate their power, gain public acceptance, and attract the notice of defense ministries.
These advances in core technology mean that IoT can today automate more tasks. IoT can now reduce the cost of many operations and increase efficiency, performance, and tempo of actions. Increasingly, IoT networks will begin to alter and control our commercial and private environments. For defense and national security, IoT's promise is just beginning to unfold. However, IoT cannot yet meet its full potential for defense. The special challenges IoT faces in this domain still limit it. This Book addresses many of these challenges, presenting emerging R&D results and case studies. These advances let IoT alter, control, and automate our affairs both for defense and in peaceful society. It is already beginning to do so.
4 IoT Will Change How War Is Waged and How Peace Is Maintained
National defense and security are founded on situation awareness that provides insights into hostile intentions and actions. Accurate situation awareness serves as a basis for planning operations and then controlling them by observing and adjusting their effects. Excellent situation awareness depends on continuous surveillance provided by diverse sensors. To be effective for defense, sensors must be coupled together, coordinated, and controlled along with thousands of other devices and entities. The entities are distributed, mobile and heterogeneous. They include packages, weapons, vehicles, aircraft, soldiers, and thousands of other types of equipment and supplies. They must operate effectively together whether waging war, maintaining peace, assisting in emergency relief, ensuring effective logistics, or protecting the homeland. For many of the world's militaries, IoT is already altering logistics, command and control, tactics, and surveillance. Coordination and control require planning and monitoring a plan's execution. Yet, as Helmuth von Moltke reputedly observed, no plan survives contact with the enemy. The reason is not because parts of the plan fail – good commanders develop contingencies in their plans. Plans fall apart because of what Clausewitz called the “fog of war” – a breakdown of visibility and communication in the battlespace. The fog of war prevents a commander from knowing what parts of his plan are succeeding or failing and from deploying the means to alter it. Lacking insight into a battle's progression, a commander may not know where the enemy is or even the disposition of his own forces. Individual units lose track of the location and status of their supporting units, supplies, and opposing forces. In the future, IoT promises more than just improved efficiency in envisioning the battlefield and coordinating and executing plans. IoT can change the course of battles, wars, and peace. It can do so by physically altering the world by dynamically joining vast numbers of intelligent, heterogenous sensors, processors, and actuators over robust and secure networks. IoT dispels the fog of war. It extends the eyes and ears of the intelligence analyst. It multiplies and amplifies the arms of the warfighter.
Some of the main benefits of IoT for defense and national security can be summarized as follows:
Autonomous/Automated Weapons:
IoT increases automation and enables autonomous weapons and systems.
Increased the Tempo of War:
More autonomy in defense systems moves humans from inside the control loop to supervisory, top‐level control. Actions can go from sensors to actuators in milliseconds instead of seconds, minutes, or hours.
Reducing Your Force's Casualties
: In a supervisory role, humans use IoT remotely for sensing, control, and action. Warfighters move back from the most dangerous environments. Javelin anti‐tank missiles and Switchblade loitering missiles keep soldiers miles back from their targets, for example, by using a Javelin in a “fire‐and‐forget” mode and controlling a Switchblade with remote supervisory oversight.
Increasing the Probability of Destroying Targets While Reducing civilian Casualties:
Onboard and off‐board sensors in an IoT network can guide weapons more accurately to their intended target. Automated control loops update flight paths more rapidly, more often, and more accurately than weapons that are directly sighted, aimed, and controlled by soldiers. By tying multiple types of dispersed weapons together, IoT increases the attack surface on the adversary.
Expands the View for Military Operations and Intelligence:
IoT networks can join large numbers of unattended and dispersed sensors covering wide areas. IoT also gleans information from data bases, by sniffing networks, and by monitoring software applications. While traditional standalone sensors can provide rudiments of this sort of information, large numbers of sensors and information sources become infeasible to manage and integrate without the network structure IoT provides. IoT observes an area, event, or activity in a more pervasive, multi‐view, and continuous manner. IoT sensor networks support improved human interpretation of sensor data and deliver training data through the cloud for intelligent processing algorithms.
Dispels the Fog of War:
A fog envelopes a commander's view of an unfolding situation when sensors are destroyed, or data and reports become unavailable. IoT's creates dynamically self‐healing, adaptable, and resilient networks of information sources with multiple secure communications paths. These features allow a commander to continue to observe the situation even in the chaos of combat. The fog is lifted. When information sources are overlapping or closely adjacent, data can be fused into a high‐quality operational picture even when using poor quality sensors or when individual sensors or reports go missing. IoT's ability to flexibly place and replace and move sensors provides further resiliency to uninterrupted situation awareness. When IoT includes specialized sensors in the IoT network, such as radar, thermal imagers, and radio‐frequency imagers, it can penetrate literal atmospheric fog as well as smoke, clothing, and even the walls of buildings. Advances in tactical communications and networking, such as those described in
Section 4
, keep information flowing from and into the battlefield.
Intelligent Processing (AI and Big‐data Techniques
)
to Convert Data to Information, Information to Awareness, Awareness to Plans, and Plans to Action:
IoT provides intelligent processing to expand human ability to observe. It makes sense of vast amounts of data and information. It can automatically develop plans and autonomously control responses. While IoT may dispel the fog of war, it can create a new fog by overloading decision makers with data. Smart processing provides the essential element to digest, filter, prioritize, and abstract information so commanders are not buried by the flow. AI algorithms are beginning to demonstrate human‐level intelligence in aspects of military operations, such as air combat.
Automation of Logistics:
Logistic activities, such as resupply, deployment, and maintenance, become much more efficient and experience an increased tempo. For example, as described in
Chapter 2
, if a soldier's weapon always knows what ammunition is remaining and can automatically order more and can monitor the rifle's use and wear, then both resupply and maintenance become streamlined. Logistics becomes supercharged when IoT can stay abreast in real‐time of what, where, when, and how much is needed. IoT knows where supplies currently reside and what resources are available to move them. Just‐in‐time and just‐in‐case supply chains can add efficiency, lower costs, and improve reliability. When supply chain data is collected and entered manually, advanced supply concepts become infeasible. By networking sensors and actuators to monitoring algorithms, new logistics approaches become possible, such as conditioned‐based maintenance.
Encourage the Use of Standards:
The advantages of having flexible and dynamic networks of sensors, processors, and actuators will encourage the development and adoption of standards allowing future defense systems to interoperate as part of larger IoT systems. Expanding standards also allows common IoT components to be used across stovepipe systems, simplifying logistics.
Breaking and Connecting Existing Stovepipes and Silos:
IoT can join legacy stovepipe systems (and eventually make them obsolete) by using automatically compiled software to create interfaces between legacy systems. This eliminates human fingers, keyboards, and manual delays crossing the gaps between legacy systems.
Rapid Evolution and Turn‐over of Technology
: The nature of IoT supports rapid and low‐cost evolution of technology by allowing new, improved components to be easily inserted as nodes in the network. For example, IoT LTE communication nodes support incremental replacement with 5G as it becomes available. This does not free an IoT systems designer from considering the impact on the entire system from upgrading a node. But it does make it technically easier and therefore quicker and cheaper to effect the upgrade. It also makes it possible to do it incrementally, spreading the cost of upgrades and allowing them to be continuous, keeping closer pace with rapidly advancing commercial technology.
Forcing Function for Advances in Policy, Doctrine, Logistics, and Acquisition:
The power to be gained by adapting commercial IoT technology to defense and continually upgrading defense capability may incentivize or force defense departments and defense industry to develop the policy, doctrine, logistics, and procurement processes necessary to quickly leverage commercial technology. The advantages of adapting commercial IoT to military requirements on an ongoing basis may entice defense ministries to make necessary organizational and process changes.
Advancing and mastering IoT technology and its application is essential for any government attempting to maintain parity with its peers. At present, commercial IoT far outstrips defense IoT. The challenge for defense ministries and industry is to leverage commercial investment in IoT while augmenting it to meet the domain's special requirements. Many of the challenges are technical and many technical solutions are the topic of the Chapters of this Book. But nations must also adapt their processes and policies to accomplish an efficient flow of commercial IoT advances into defense applications. Those who do so will likely dominate military power in the rest of twenty‐first century. To better understand how commercial and defense IoT have and can intertwine, a brief history of the development of IoT for national defense follows.
5 A Short History of IoT for National Defense
As noted, the concept of IoT originated in defense organizations many decades ago. Only with the commercial maturation of component technology has its impact become significant. Defense departments are traditionally conservative in adopting new technology into practice, although, ironically, they are often in the forefront of inventing new technology. Commonly, the much larger commercial marketplace is required to move inventions to innovations in practice. Once mature the technology moves back into defense systems. That is the case with IoT. A brief review of the development and use of IoT in the US Defense Department provides a case study in understanding how IoT is adopted for defense applications. It also illustrates the interplay between defense IoT development and commercial technology maturation. Understanding the history of IoT in the US defense enterprise provides a context for the discussion of the interplay between commercial and defense IoT going forward, as described in part 6 of this Introduction.
