Current communication and network systems are not built for delay-sensitive applications. The most obvious fact is that the communication capacity is only achievable in theory with infinitely long codes, which means infinitely long delays. One remedy for this is to use shorter codes. Conceptually, there is a deeper reason for the difficulties in such solutions: in Shannon's original 1948 paper, he started out by stating that the "semantic aspects" of information is "irrelevant" to communications. Hence, in Shannon's communication system, as well as every network built after him, we put all information into a uniform bit-stream, regardless what meanings they carry, and we transmit these bits over the network as a single type of commodity. Consequently, the network system can only provide a uniform level of error protection and latency control to all these bits. We argue that such a single measure of latency, or Age of Information (AoI), is insufficient for military Internet of Things (IoT) applications that inherently connect the communication network with a cyber-physical system. For example, a self-driving military vehicle might send to the controller a front-view image. Clearly, not everything in the image is equally important for the purpose of steering the vehicle: an approaching vehicle is a much more urgent piece of information than a tree in the background. Similar examples can be seen for other military IoT devices, such as drones and sensors. In this work, we present a new approach that inherently extracts the most critical information in a Military Battlefield IoT scenario by using a metric - called H-Score. This ensures the neural network to only concentrate on the most important information and ignore all background information. We then carry out extensive evaluation of this a by testing it against various inputs, ranging from a vector of numbers to a 1000x1000 pixel image. Next, we introduce the concept of Manual Marginalization, which helps us to make independent decisions for each object in the image. We also develop a video game that captures the essence of a military battlefield scenario and test our developed algorithm here. Finally, we apply our approach on a simple Atari Space Invaders video game to shoot down enemies before they fire at us. / Master of Science / The IoT is transforming military and civilian environments into truly integrated cyberphysical systems (CPS), in which the dynamic physical world is tightly embedded with communication capabilities. This CPS nature of the military IoT will enable it to integrate a plethora of devices, ranging from small sensors to autonomous aerial, ground, and naval vehicles. This results in huge amount of information being transferred between the devices. However, not all the information is equally important. Broadly we can categorize information into two types: Critical and Non-Critical. For example in a military battlefield, the information about enemies is critical and information abouut the background trees is not so important. Therefore, it is essential to isolate the critical information from non-critical informaiton. This is the focus of our work. We use neural networks and some domain knowledge about the enemies to extract the critical information and use the extracted information to take control decisions. We then evalue the performance of this approach by testing it against various kinds of synthetic data sets. Finally we use an Atari Space Invaders video game to demonstrate how the extracted information can be used to make crucial decisions about enemies.