The Role of the IoT and AI Technologies in Modern Warfare

2022 signifies a revision of the political status quo that had been maintained since World War II and a return to realpolitik with the deployment of large-scale arms conflicts in the European continent. Warfare has always been a sizeable governmental expenditure, generating the North Atlantic Treaty Organization (NATO) 2% of Gross Domestic Product (GDP). The US and China have already increased their defense budgets by 10% and 7%, constituting budgets of US$801 billion and US$230.16 billion, respectively. Interestingly, the technology appears to be one of the large spending buckets (at least for the US), with an estimated US$112 billion allotted for Research, Development, Test, and Evaluation (RDT&E), while 13% of the budget (US$14.7 billion) goes to Science and Technology.

The analysis of the US’s Department of Defense and NATO statements and recommendations showcase the technological advances in the areas of the Internet of Things (IoT), while Artificial Intelligence (AI) is becoming a strategic priority for the defense industry, which enables them to develop more advanced military capabilities and forces. There are various applications of the IoT in the military deployment, starting from the weapon system’s connectivity to the network, which facilitates the commanders to make real-time decisions, to intelligence supply chain management, augmented reality, cyber security, and many others that have become an integral part of the modern warfare capabilities.

• Military asset tracking: This use case is widely adopted in the defense industry. IoT devices enable real-time GPS monitoring of speed, motor function, engine time, fuel consumption, and well-rounded information on vehicle function and positioning. The data collected from multiple military assets deployed in various geographic areas allows strategy and training formation and logistics insights on the further need for fuel and maintenance of the assets. For example, Tapestry Solutions provides the gateway to the IoT with its Enterprise Sensor Integration (ESI) technology. The ESI is a platform with the ability to integrate various Radio Frequency Identification (RFID) position-information tags and sensor devices and enable GPS tags for embedded hardware and complex servers.
• AI-enabled target prediction: This application is a variation of prescriptive analytics, where data from the external environment, weather, and traffic is aggregated to detect anomalies or potential risks in the supply chain. The fundamentals of such application are machine learning-enabled “what-if scenarios”, allowing commanding officers to learn, track, and mitigate risks from existing value chain pipelines.
• Real-time fleet management: Machine learning and AI algorithms can optimize military transport and minimize traffic expenses and human operating efforts. According to the US Department of Defense, fleet management systems account for more than 25% of fuel-saving in operational deployment. Intelligent tracking and fleet management allow operation insight on military fleet movement, delivery, and security. In real-time, extending the visibility would enable military personnel to forecast which products are in demand or excess. It also streamlines logistical control and eliminates losses and robbery.
• Augmented Reality for remote training: The augmented models displayed through Head-Mounted Display (HMD) combined with historical and even near real-time data create an environment for a training simulation that is accurate to the external situation. The Augmented Reality (AR)/Virtual Reality (VR) HMD linked with the developed model allows soldiers to train in the near-real environment and obtain data on personal behavior to create further medical risk assessments. The most common use case of AR/VR is piloting simulation for pilots before flying the aircraft. Pilots must carry out several maneuvers to evade an enemy trail or a tracking missile, which is impossible to do in a peaceful time.
• Predictive maintenance: Similar to manufacturing and industrial verticals, predictive maintenance is among the top use cases of Machine Learning (ML) and IoT deployment in IoMT. The data from ships, places, and armored personnel carriers combined with machine learning models enable the army to maintain and have high readiness capacity for military deployment. The British Ministry of Defense launched project Nelson in 2018 to develop a core maritime platform with advanced data exploration and analytics capabilities. Similarly, in the US, Falkonry was selected by STRATCOM’s Joint Warfare Analysis Center (JWAC) to adapt its AI-enabled software to serve the US Air Force.

Despite the shift in military paradigm and more significant expenditure for technological advancement for military application, it is essential to highlight the role of the private sector. Private companies have been instrumental in democratizing IoMT-related technologies and applications, starting from the sensor and AI chipset development to AI and ML applications enabling understanding of the battlefield in almost real-time. The commercially developed tech makes it possible to handle a wide range of assets and coordinate the building of the connectivity network for military operations in a cost-efficient manner due to the economies of scale. However, in this instance, the technology innovation is still evolving in the space of legacy systems that highlight the challenge of connectivity, interoperability, and legal challenges (especially in geographically dispersed areas). The other challenge of commercial entities in the IoMT space is potential exposure to intellectual property that halters commercial opportunities for vendors beyond its military application contracts. Nevertheless, it is expected that the greater standardization and integration between private and defense institutions would enable a significant capability to increase in military technology deployment.

On the other hand, the significant hyperscalers and technology vendors are constantly battling for primary military contacts. In 2019, the US’s Department of Defense launched a tender for US$10 billion over ten years of communication, which was rewarded to Microsoft. However, the JEDI project was dropped after an extensive AWS legal battle over the argument that Amazon was prevented from winning this contract. Additionally, in 2020, LOGSA (US army logistic section) awarded IBM a US$135 million contract to provide cloud services, software development, and cognitive computing.

If the arms race was about outer space capabilities in the previous warfare paradigm, that arms race 2.0 is about AI and cognitive computing capabilities and who can get there first. Hyperscalers will attempt to be at the forefront of the new technology arms race, primarily through cloud computing services. However, due to the traditionally bureaucratic model of the public service and army structure, adopting new technologies and cloud capabilities will be a long road.