What are the Emerging Technologies, and Why the Military are Interested?
- Digitization transferred content from analogue to digital format and improved military administration and office work.
- Digitalisation introduced enterprise-wide systems, like Enterprise Resource Planning, which enabled human, financial, material, and facilities management or battle-space management systems for faster situational awareness.
- Digital transformation has enabled revolutions in military affairs, such as Network-Centric Warfare in the US Department of Defense and network-enabled Capability in the UK Ministry of Defence.
Current waves of transformation enabled by emerging technologies are revolutionising industry (The Fourth Industrial Revolution), commerce (digital biology), facilities (smart homes, cities, and government), and the military (Combat Cloud).
This paper creates an enterprise architecture view of possible digital infrastructure that military affairs may benefit from while planning their second wave of digital transformation for further capabilities. Meanwhile, lethality in battlespace increases, dual-use technology creates tactical advantages, weapon and counter-weapon development takes place in days, arms races raise prizes of armament, and additional defence finances are complicated to gain.
A Systematic Perspective to a Military C5ISTAR Technology Stack Enabled by Emerging Technologies
For a systematic assessment of emerging technologies' impact on Military Affairs, this study divides the technology stack into infrastructure, data, systems, and business models aligned with common enterprise architectures. In this approach, digital modelling or digital twins are the points of interest because they are virtual representations that allow the modelling of the state of a physical entity or system. They are created by digitalising data collected from physical entities through sensors, so various predictions can be made by understanding the behaviour of the physical entity. Virtualising and digitising the physical world seems a beneficial feature for Military Affairs because it enables the military to :
- Create digital models of physical phenomena, run accurate simulations, and gain foresight into possible future.
- Improve the man-machine interface with more immersive ways to interact with machines.
- Maintain the faster OODA loop at the tactical level with less delayed data transfer, optimised computing, and algorithm-accelerated sense-making.
- Bring machine interoperability from recognising the data to sharing the understanding.
The following gives a more detailed view of possible military C5ISTAR technology stack changes.
Infrastructure Layer (networking, transfer and processing)
In this case, the infrastructure layer includes networking, data transfer and processing functions, as illustrated in Figure 1. The wireless 5/6G evolution improves the access network from the edge to terminal capacity and connectivity and lowers the latency if cellular base stations are connected via a high-bandwidth terrestrial network. Non-terrestrial, air- and spaceborne base stations are available, improving accessibility and simplifying the integration. The terrestrial and non-terrestrial 5G base stations compose a three-point access network with standard transfer and networking functions. This multi-domain connectivity will replace legacy tactical data links while improving the availability of access and roaming and extending the range over the horizon, features essential in the Joint All-Domain C2 (JADC2) concept promoted by David Deptula.
Furthermore, with higher frequencies, the cell sizes are smaller, and the Effective Radiated Power (ERP) is less, which means that transceivers' low probability of detection and identification (LPI/LPD) improves. However, with lower frequencies, higher transceiver density, and smaller radiation patterns, deploying dual-use Radio Frequency Identification (RFID), the Internet of Things (IoT), and Operational Technology on the battlefield becomes feasible.
With 5/6 G enhanced wireless communications, the access network becomes more versatile than the legacy Local Area Network (LAN) topology. For example, command posts can be distributed across a wider area without losing seamless collaboration connectivity. Platforms become cell base stations, providing access points to Mobile Adhoc Networks (MANET) within and between platoons, squadrons, teams, and higher organisations. Expendable, swarming sensors and effectors can be connected to a larger tactical unit even in an electromagnetically contested environment.
Furthermore, the new Open Radio Access Network (ORAN) and all-encompassing Internet Protocol (IP) solve the current technical-level interoperability issues. They allow you to create virtual, sliced, or private military network domains parallel to other network users without creating congestion points or bottlenecks.
The flexible network and transport layers support data flows that enable hybrid clouds and hybrid computing, which varies between different clouds, edges, and endpoints. Hybrid computing provides optimal data processing for a task, addressing anything between real-time, big data, or algorithm-crunching requirements.
Data Layer
In Figure 1, the data layer is on top of the infrastructure layer. Enabling technologies may include Data flows with different Quality of Service (QoS), Data warehouses, Data Lakes, Lakehouses, Table formats, Business Intelligence, and Synthetic data.
These technologies may be implemented in three main categories of data architectures: Stove-piped, centralised, or data mesh. Stove-piped data architecture is a direct continuum from system-based data architecture. It enables the legacy of functional data owners who use proprietary data models and do not share data unless forced. Centralised data architecture breaks the stove-pipe boundaries and brings data to data warehouses, lakes or Lakehouses. A centralised approach establishes central data functions and provides development and Data as a Service (DaaS) to functions and Forces. However, the central entity may become an administrative bottleneck, isolating data from Forces. Conversely, data mesh prioritises domain-driven design while enabling the teams closest to big data sets to take control of meeting their data preparation and analytics needs. Data mesh enables the democratisation of data so that it’s available to everyone in an enterprise, regardless of their technical expertise, function, or organisation. Each Command of sense and decision-making becomes a citizen data scientist, an officer who can analyse data but doesn’t take on that task as their primary role. Gartner recognises this with the estimation that by 2027, organisations faced with AI and data security requirements will standardise on policy-based access controls to unlock the value from more than 70% of their data.
Data Flow follows uplinks, and downlinks may become bottlenecks if flow management is not prioritised. Since the transfer layer enables Quality of Service prioritisation, military affairs may arrange vertical and horizontal data flows to provide real-time awareness and longitudinal big data for modelling and forecasting.
Data Warehouses are central data repositories integrated from disparate sources, namely operational systems. They enable straightforward business intelligence queries because the data is aligned, cleansed, and structured.
A Data Lake is a system or repository of data stored in its natural/raw format. The repository may be a single data store but includes raw copies of source system data, sensor data, and social data in structured, semi-structured, or unstructured formats. Data from a data lake may be used for reporting, visualisation, advanced analytics, and teaching machine learning.
A Data Lakehouse combines the flexibility of data lakes for working with raw and often unstructured or semistructured data with the reliability and performance of traditional data warehouses that store consolidated sets of structured data.
A Data fabric is a data management design concept for attaining flexible, reusable and augmented data pipelines and services supporting various operational and analytics use cases. Data fabrics support a combination of different data integration styles and utilise active metadata, knowledge graphs, semantics and machine learning to augment data integration design and delivery.
The Data Table Formats provide cross-platform compatibility, transaction support, and schema evolution. Developing the Data Lakehouse ecosystem requires open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Enabling schema evolution is essential for managing data structures over time while maintaining data integrity and backward compatibility. Data Schema management improves interoperability at the upper layers and facilitates establishing a smart machine system of systems.
Synthetic data is created by taking a database, creating an ML model for it, and generating a second set of data. The generated synthetic data has the same patterns and properties as actual data, but it’s not tied to any actual data identifiers. Synthetic data is generated fast, automatically tagged, and provides high-quality data regarding events that rarely happen in the real world, which is very applicable in military affairs.
Available data in both arranged and raw formats enable a variety of data analytics:
- Traditional analytics requires a team of IT analysts to comb through data, theorise potential insights, test those insights, and report on their findings.
- ML-based models can continuously monitor data, pick out anomalies, and alert the appropriate teams in real time without human input.
- Business intelligence tools harness raw data to extract meaningful patterns and actionable insights.
Systems and Services Layer
The next layer enabled by the data layer is the systems and services layer in Figure 1. Emerging technologies opening new options for military affairs include human-machine interface (HMI), immersive technologies, spatial computing, metaverse, algorithms, energy-efficient computing, and classical and quantum computing.
The Human-Machine Interface will evolve using immersive-reality technologies based on the current industrial and office interfaces enabled by multitouch video technologies on tablets and smartphones. Human actors will experience real-time interactions in three-dimensional virtual worlds that eventually incorporate the physical world. The evolution runs from a fully computer-generated space in virtual reality (VR) to mixed reality (MR) and further towards augmented reality (AR), where computer-generated objects are superimposed on the real world.
Spatial computing maps indoor and outdoor physical spaces (including people and furniture). Then, the digital content is anchored within the physical world, enabling users to interact with it realistically.
Furthermore, the metaverse interconnects digital spaces where users can interact, socialise, and create. Spatial computing ensures users' accurate positioning and synchronises their actions. The human-machine interface allows people to have lifelike personal and business experiences online.
Virtualisation and decentralisation of the processing layer enable the distribution of computing workloads across different sites, such as hyperscale remote data centres, regional centres, on-premises centres, and edge points. This ability to distribute workloads supports optimising latency, data transfer costs, adherence to data sovereignty regulations, autonomy over data, and security considerations. Gartner recognises the trend as follows:
‘By 2025, Gartner predicts more than 50% of critical data will be created and processed outside the enterprise’s data centre and cloud.’
‘By 2027, approximately 5% of large enterprises will deploy a hyperscaler distributed cloud solution for edge computing workloads outside data centres.’
Edge computing involves processor-intensive, often repetitive, mission-critical data analytics within devices on the outer edge of a network. With supporting networking and data layers, edge computing enables more real-time intelligence and faster sense-making from tactical to operational levels. Furthermore, edge processing supports machine-to-machine cooperation within the Intranet of Military Things (IoMT) sensors and actors.
The decentralising layer hosts a variety of algorithms, including AI, optimised to specific functions in support of the business layer. Gartner forecasts this in the business as follows:
‘By 2028, 50% of enterprise platforms will leverage specialised infrastructures to support AI infusion, a significant increase from less than 10% in 2023.’
Next-generation systems and services are developed with tools and technologies that enable modern code deployment pipelines and automated code generation, testing, refactoring, and translation. These can improve application quality and development processes. The Gartner sees this emerging trend as follows:
‘By 2027, 80% of AI-generated SaaS applications will be up to 80% composite for efficiency of human-AI digital engineering.’
‘By 2026, 40% of development organisations will use the AI-based auto-remediation of unsecured code from application security testing (AST) vendors as a default, up from less than 5% in 2023.’
Digital Business Modelling Layer
The last layer enabled by the technology layers is the digital business layer in Figure 1. The next-generation technology layers enable features like digital twin, artificial intelligence-based image recognition, optimisation, expert functions, robotic process automation (RPA), AI agents, autonomic systems, synthetic media, ambient, invisible intelligence, polyfunctional robots, and data-driven military.
A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It is built on big data, spans the object's lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help make decisions. Military Affairs may benefit from digital twin features in the maintenance and repair of platforms, developing system of systems, capability life-cycle management, force generation, and strategic modelling.
Applied AI technologies use models trained through machine learning to solve classification, prediction, and control problems, automate activities, add or augment capabilities and offerings, and improve decision-making. These features may benefit military affairs, for example, in financial optimisation, personnel promotion, facilities management, supply chain management, and learning management.
Robotic Process Automation and AI Agents refer to a system or program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilising available tools. Beyond natural language processing, AI agents can encompass various functionalities in military affairs, including decision-making within processes, problem-solving in real-time situations, interacting with external environments, and executing actions. The Gartner foresees emerging features in business as follows:
‘By 2027, GenAI tools will be used to explain legacy business applications and create appropriate replacements, reducing modernisation costs by 70%.’
‘By 2027, more than 40% of digital workplace operational activities will be performed using management tools enhanced by GenAI, dramatically reducing the labour required.’
‘By 2028, 60% of IT services will be powered by the trifecta of GenAI, hyper-automation and metaverse, radically changing the services buyer landscape.’
Data-driven military affairs may witness changes among supporting entities like Intelligence, Military Survey, Logistics, and Operation Centres that provide continuously improved data products to their supported entities. Secondly, the data-driven approach may change military supply chain management as products and support become more cyber-physical, and data outside the military will become more valuable assets with emerging commercial space and cyber operators. Thirdly, the military may be able to execute so-called ‘information-driven operations. The defence organisation should not only be capable of obtaining an authoritative information position (or information dominance), but it must also use information as a ‘weapon’, i.e. as a means or instrument of influence. Fourthly, the quantitatively thinking commanders may be able to mitigate the analysis paralysis usual with current risk-avoiding sense-making supported with less machine-based analysis.
Altogether, the digitalisation illustrated in Figure 1 supports the Fourth Industrial Revolution (4IR) and provides potential for Military Affairs to benefit from. The second wave of military digital transformation may create strategic advantages for the Operate, Generate, and Support functions. The UK Army’s digital transformation program, THEIA, has three headline outputs: out-compete the adversary, partner better and integrate with partners, and improve efficiency. The US Army aims to improve and leverage innovative and transformative technologies: modernisation and readiness, optimised digital investments, and a technically savvy, operationally effective digital workforce. NATO is talking about using these “emerging and disruptive technologies efficiently.” NATO could improve its operations with military, industry, and civilian partners in every warfighting domain, including sea, land, air, space, and cyber operations.
Figure 1: An illustration of a possible technology stack on top of more efficient communications
