The military planners have envisioned a future battlefield defined by the internet of things, one during which smart devices, unmanned aircraft produce an endless torrent of actionable data and soldier-worn sensors. The Internet of Military Things (IoMT) may be a class of Internet of things for combat operations and warfare. It’s a posh network of interconnected entities, or “things”, within the military domain that continually communicate with one another to coordinate, learn, and interact with the physical environment to accomplish a broad range of activities in a more efficient and informed manner. The concept of IoMT is essentially driven by the thought that future military battles are going to be dominated by machine intelligence and cyber warfare and can likely happen in urban environments. The IoMT is conceptually designed to dump much of the physical and mental burden that warfighters encounter during a combat setting by creating directly a miniature ecosystem of smart technology capable of distilling sensory information and autonomously governing multiple tasks. Several different terms overtimes are introduced to explain the utilization of IoT technology for reconnaissance, environment surveillance, unmanned warfare, and other combat purposes. The terms are included the Military Internet of Things (MIoT), the internet of Battle Things, and now the Internet of Battlefield Things (IoBT).
Internet of Battlefield Things (IoBT)
The U.S. Army lab (ARL) created in 2016 the internet of Battlefield Things (IoBT) project in response to the U.S. Army’s operational outline for 2020 to 2040, titled “Winning during a Complex World.” within the outline, the Department of Defense announced its goals to stay up with the technological advances of potential adversaries by turning its attention faraway from low-tech wars and instead that specialize in combat in additional urban areas. Acting as an in-depth blueprint for what ARL suspected future warfare may entail, the IoBT project pushed for better integration of IoT technology in military operations so as to raised steel oneself against techniques like EW which will lie ahead.
The ARL established the internet of Battlefield Things in 2017 Collaborative Research Alliance (IoBT-CRA) to compile industry, university, and government researchers to advance the theoretical foundations of IoBT systems.
According to ARL, the IoBT was primarily designed to interact with the encompassing environment by acquiring information about the environment, acting upon it, and continually learning from these interactions. The research efforts as a consequence focused on sensing, actuation, and learning challenges. so as for the IoBT to function as intended, the subsequent prerequisite conditions must first be met in reference to technological capability, structural organization, and military implementation.
Relations and Communication
All entities within the IoBT must be ready to properly communicate information to at least one another even with differences in architectural design and makeup. words, all equipment, technology, or other commercial offerings accessed by military personnel must share an equivalent language or a minimum of have “translators” that make the transfer and processing of various sorts of information possible. The IoBT must be capable of temporarily incorporating available networked devices and channels that it doesn’t own for its own use, especially if doing so is advantageous to the system (e.g. making use of existing civilian networking infrastructure in military operations during a megacity). The IoBT at an equivalent time must take into consideration the varying degree of trustworthiness of all the networks it leverages.
Timing is going to be critical to the success of IoBT. The speed of computation, communication, interface, machine learning, and actuation between entities are vital to several mission tasks because the system must know which sort of data to prioritize. Scalability also will function as a crucial thing about the operation since the network must be flexible enough to function at any size.
Training and research
The success of the IoBT framework often hinges on the effectiveness of the mutual collaboration between the human agents and therefore the electronic entities within the network. The electronic entities during a tactical environment are going to be tasked with a good range of objectives from collecting information to executing cyber actions against enemy systems. so as for these technologies to perform those functions effectively, they need to be ready to not only ascertain the goals of the human agents as they modify but also demonstrate a big level of autonomous self-organization to regulate the rapidly changing environment. Unlike commercial network infrastructures, the adoption of IoT within the military domain must take into consideration the acute likelihood that the environment could also be intentionally hostile or unstable, which can require a high degree of intelligence to navigate.
The IoBT technology as a result must be capable of incorporating predictive intelligence, machine learning, and neural network so as to know the intent of the human users and determine the way to fulfill that intent without the method of micromanaging each and each component of the system.
According to ARL, maintaining information dominance will believe the event of autonomous systems which will operate outside its current state of total dependence on human control. A key focus of IoBT research is that the advancement of machine learning algorithms to supply the network with decision-making autonomy. Instead of having one system at the core of the network functioning because the central intelligence component dictating the actions of the network, the IoBT will have intelligence distributed throughout the network. Therefore, individual components can learn, adapt, and interact with one another locally also update behaviors and characteristics automatically and dynamically on a worldwide scale to suit the operation because the landscape of warfare constantly evolves. Within the context of IoT, the incorporation of AI into the sheer volume of knowledge and entities involved within the network will provide an almost infinite number of possibilities for behavior and technological capability within the world .
In a tactical environment, the IoBT must be ready to perform various sorts of learning behaviors to adapt to rapidly changing conditions. One area that received considerable attention is that the concept of meta-learning, which strives to work out how machines can find out how to find out. By taking the skill would permit the system to avoid fixating on pretrained absolute notions on how it should perceive and act whenever it enters a replacement environment. The IoBT should also demonstrate a classy level of situation awareness and AI which will allow the system to autonomously perform work supported by limited information. A primary goal is to show the network the way to correctly infer the entire picture of a situation while measuring relatively few variables. The system must be capable as a result of integrating the vast amount and sort of data that it regularly collects into its collective intelligence while functioning during a continuous state of learning at multiple time scales, simultaneously learning from past actions while acting within the present and anticipating future events.
The network must also account for unforeseen circumstances, errors, or breakdowns and be ready to reconfigure its resources to recover a minimum of a limited level of functionality. However, some components are structured to be more resilient to failure than others. As an example, networks that carry important information like medical data must not ever be in danger of shutdown.
For semi-autonomous components, the human cognitive bandwidth is a notable constraint for the IoBT thanks to its limitations in processing and deciphering the flood of data generated by the opposite entities within the network. Truly useful information during a tactical environment, semi-autonomous IoBT technologies must collect an unprecedented volume of knowledge of immense complexity in levels of abstraction, trustworthiness, value, and other attributes.
A key risk of IoBT is that the possibility that devices could communicate negligibly useful information that eats up the human’s valuable time and a spotlight or maybe propagate inappropriate information that misleads human individuals into performing actions that cause adverse or unfavorable outcomes. At an equivalent time, the system will stagnate if the human entities doubt the accuracy of the knowledge provided by the IoBT technology. The IoBT as a result must operate in a manner that’s extremely convenient and straightforward to know to the humans without compromising the standard of the knowledge it provides them.
Mosaic Warfare may be a term coined by former DARPA Strategic Technology Office director Tom Burns and former deputy director Dan Patt to explain a “systems of systems” approach to military warfare that focuses on re-configuring defense systems and technologies in order that they will be fielded rapidly during a sort of different combinations for various tasks. The Mosaic Warfare was promoted as a technique to confuse and overwhelm adversary forces by deploying low-cost adaptable technological expendable weapon systems which will play multiple roles and coordinate actions with each other, complicating the decision-making process for the enemy. This method of warfare arose as a response to the present monolithic system within the military, which relies on a centralized command-and-control structure fraught with vulnerable single-point communications and therefore the development of a couple of highly capable systems that are too important to risk losing in combat.
The idea of Mosaic Warfare existed within DARPA since 2017 and contributed to the event of varied technology programs like the System of Systems Integration Technology and Experimentation (SoSIT), which led to the event of a network system that permits previously disjointed ground stations and platforms to transmit and translate data between each other.
Ocean of Things program
The DARPA announced in 2017 the creation of a replacement program called the Ocean of Things, which planned to use IoT technology on a grand scale so as to determine a persistent maritime situational awareness over large ocean areas consistent with the announcement, the project would involve the deployment of thousands of small, commercially available floats. Each float would contain a set of sensors that collect environmental data—like ocean temperature and sea state—and activity data, like the movement of economic vessels and aircraft. All the information gathered from these floats would then be transmitted periodically to a cloud network for storage and real-time analysis. The DARPA aimed through this approach to make an in-depth sensor network that will autonomously detect, track, and identify both military, commercial, and civilian vessels also as indicators of other maritime activity.
The Ocean of Things project focused totally on the planning of the sensor floats and therefore the analytic techniques that might be involved in organizing and interpreting the incoming data as its two main objectives. For the float design, the vessel had to be ready to withstand the tough ocean conditions for a minimum of a year while being made out of commercially available components that cost but $500 each in total. additionally, the floats couldn’t pose any danger to passing vessels and had to be made out of environmentally safe materials in order that it could safely eliminate itself within the ocean after completing its mission. with regard to the info analytics, the project targeting developing cloud-based software that would collect, process, and transmit data about the environment and their own condition employing a dynamic display.