Mosaic Warfare
Therefore, individual components can learn, adapt, and interact with each other locally as well as update behaviors and characteristics automatically and dynamically on a global scale to suit the operation as the landscape of warfare constantly evolves. In the context of IoT, the incorporation of artificial intelligence into the sheer volume of data and entities involved in the network will provide an almost infinite number of possibilities for behavior and technological capability in the real world.
In a tactical environment, the IoBT must be able to perform various types of learning behaviors to adapt to the rapidly changing conditions. One area that received considerable attention is the concept of meta-learning, which strives to determine how machines can learn how to learn. Having such a skill would allow the system to avoid fixating on pretrained absolute notions on how it should perceive and act whenever it enters a new environment. Uncertainty quantification models have also generated interest in IoBT research since the system's ability to determine its level of confidence in its own predictions based on its machine learning algorithms may provide some much needed context whenever important tactical decisions need to be made.
The IoBT should also demonstrate a sophisticated level of situation awareness and artificial intelligence that will allow the system to autonomously perform work based on limited information. A primary goal is to teach the network how to correctly infer the complete picture of a situation while measuring relatively few variables. As a result, the system must be capable of integrating the vast amount and variety of data that it regularly collects into its collective intelligence while functioning in a continuous state of learning at multiple time scales, simultaneously learning from past actions while acting in the present and anticipating future events.
The network must also account for unforeseen circumstances, errors, or breakdowns and be able to reconfigure its resources to recover at least a limited level of functionality. However, some components must be prioritized and structured to be more resilient to failure than others. For instance, networks that carry important information such as medical data must never be at risk of shutdown.

Cognitive Accessibility: For semi-autonomous components, the human cognitive bandwidth serves as a notable constraint for the IoBT due to its limitations in processing and deciphering the flood of information generated by the other entities in the network. In order to obtain truly useful information in a tactical environment, semi-autonomous IoBT technologies must collect an unprecedented volume of data of immense complexity in levels of abstraction, trustworthiness, value, and other attributes. Due to serious limitations in human mental capacity, attention, and time, the network must be able to easily reduce and transform large flows of information produced and delivered by the IoBT into reasonably-sized packets of essential information that is significantly relevant to army personnel, such as signals or warnings that pertain to their current situation and mission.
A key risk of IoBT is the possibility that devices could communicate negligibly useful information that eats up the human's valuable time and attention or even propagate inappropriate information that misleads human individuals into performing actions that lead to adverse or unfavorable outcomes. At the same time, the system will stagnate if the human entities doubt the accuracy of the information provided by the IoBT technology. As a result, the IoBT must operate in a manner that is extremely convenient and easy to understand to the humans without compromising the quality of the information it provides them.

Mosaic Warfare: Mosaic Warfare is a term coined by former DARPA Strategic Technology Office director Tom Burns and former deputy director Dan Patt to describe a “systems of systems” approach to military warfare that focuses on re-configuring defense systems and technologies so that they can be fielded rapidly in a variety of different combinations for different tasks. Designed to emulate the adaptable nature of the lego blocks and mosaic art form, Mosaic Warfare was promoted as a strategy to confuse and overwhelm adversary forces by deploying low-cost adaptable technological expendable weapon systems that can play multiple roles and coordinate actions with one another, complicating the decision-making process for the enemy. This method of warfare arose as a response to the current monolithic system in the military, which relies on a centralized command-and-control structure fraught with vulnerable single-point communications and the development of a few highly capable systems that are too important to risk losing in combat.
The concept of Mosaic Warfare existed within DARPA since 2017 and contributed to the development of various technology programs such as the System of Systems Integration Technology and Experimentation (SoSIT), which led to the development of a network system that allows previously disjointed ground stations and platforms to transmit and translate data between one another.

Ocean of Things: In 2017, DARPA announced the creation of a new program called the Ocean of Things, which planned to apply IoT technology on a grand scale in order to establish a persistent maritime situational awareness over large ocean areas. According to the announcement, the project would involve the deployment of thousands of small, commercially available floats. Each float would contain a suite of sensors that collect environmental data—like sea surface temperature and sea state—and activity data, such as the movement of commercial vessels and aircraft. All the data collected from these floats would then be transmitted periodically to a cloud network for storage and real-time analysis. Through this approach, DARPA aimed to create an extensive sensor network that can autonomously detect, track, and identify both military, commercial, and civilian vessels as well as indicators of other maritime activity.
The Ocean of Things project focused primarily on the design of the sensor floats and the analytic techniques that would be involved in organizing and interpreting the incoming data as its two main objectives. For the float design, the vessel had to be able to withstand the harsh ocean conditions for at least a year while being made out of commercially available components that cost less than $500 each in total. In addition, the floats could not pose any danger to passing vessels and had to be made out of environmentally safe materials so that it could safely dispose of itself in the ocean after completing its mission. In regards to the data analytics, the project concentrated on developing cloud-based software that could collect, process, and transmit data about the environment and their own condition using a dynamic display.

Security concerns: One of the largest potential dangers of IoMT technology is the risk of both adversarial threats and system failures that could compromise the entire network. Since the crux of the IoMT concept is to have every component of the network—sensors, actuators, software, and other electronic devices—connected together to collect and exchange data, poorly protected IoT devices are vulnerable to attacks which may expose large amounts of confidential information. Furthermore, a compromised IoMT network is capable of causing serious, irreparable damage in the form of corrupted software, disinformation, and leaked intelligence.
According to the U.S. Department of Defense, security remains a top priority in IoT research. The IoMT must be able to foresee, avoid, and recover from attempts by adversary forces to attack, impair, hijack, manipulate, or destroy the network and the information that it holds. The use of jamming devices, electronic eavesdropping, or cyber malware may pose a serious risk to the confidentiality, integrity, and availability of the information within the network. Furthermore, the human entities may also be targeted by disinformation campaigns in order to foster distrust in certain elements of the IoMT. Since IoMT technology may be used in an adversarial setting, researchers must account for the possibility that a large number of sources may become compromised to the point where threat-assessing algorithms may use some of those compromised sources to falsely corroborate the veracity of potentially malicious entities.
Minimizing the risks associated with IoT devices will likely require a large-scale effort by the network to maintain impenetrable cybersecurity defenses as well as employ counterintelligence measures that thwart, subvert, or deter potential threats. Examples of possible strategies include the use of “disposable” security, where devices that are believed to be potentially compromised by the enemy are simply discarded or disconnected from the IoMT, and honeynets that mislead enemy eavesdroppers. Since adversary forces are expected to adapt and evolve their strategies for infiltrating the IoMT, the network must also undergo a continuous learning process that autonomously improves anomaly detection, pattern monitoring, and other defensive mechanisms.
Secure data storage serves as one of the key points of interest for IoMT research. Since the IoMT system is predicted to produce an immense volume of information, attention was directed toward new approaches to maintaining data properly and regulating protected access that don't allow for leaks or other vulnerabilities. One potential solution that was proposed by The Pentagon was Comply to Connect (C2C), a network security platform that autonomously monitored device discovery and access control in order to keep pace with the exponentially-growing network of entities.
In addition to the risks of digital interference and manipulation by hackers, concerns have also been expressed regarding the availability of strong wireless signals in remote combat locations. The lack of a constant internet connection was shown to limit the utility and usability of certain military devices that depend on reliable reception.

See also: Internet of Autonomous Things
Internet of Things
Smart munitions
Edge computing
Biometrics
Cyberwarfare

Further reading: Suri, Niranjan; Tortonesi, Mauro; Michaelis, James; Budulas, Peter; Benincasa, Giacomo; Russell, Stephen; Stefanelli, Cesare; Winkler, Robert (2016). "Analyzing the applicability of Internet of Things to the battlefield environment". 2016 International Conference on Military Communications and Information Systems (ICMCIS). pp. 1–8. doi:10.1109/ICMCIS.2016.7496574. ISBN 978-1-5090-1777-5. S2CID 17390182.
Chudzikiewicz, Jan; Furtak, Janusz; Zielinski, Zbigniew (2015).