Our Technology. Innovation Perfected.

This technology operates under an AI driven architecture. To understand this, I’ll explain RF theory, and apply it to the technology, and demonstrate how it’s useful.

Wi-Fi is RF (radio frequency) signal. When RF encounters an object, the majority of that signal is passed through the object, depending on its composition. The rest is reflected back. RF signal is a sine wave that travels nearly at the speed of light. 300,000 kilometers per second, roughly.

When it encounters thick enough metal, concrete, or the flesh of a living being, the vast majority of it will be reflected back. These materials can be split into two categories. Dielectric, like wood, plastic and other like materials, and non-dielectric materials, like metals. Dielectric materials generally do not strongly reflect Wi-Fi signals. Instead, they absorb some of the signal and let the rest pass through. The same cannot be said for humans or animals. While a human is technically dielectric, our flesh acts sort of like a sponge, and soaks the signal in, while allowing very little to pass through. This is due to humans being filled with liquids, and the thickness of our bodies. Even babies and toddlers have this same effect. Non-dielectric materials, like metals, will generally reflect the signal due to their density and composition.

RF signal is a form of light that is in a range that we cannot see, so it behaves similarly to light. For example, if someone were to shine a light through a piece of paper, the light would be able to shine through to the other side, while dimmed quite a bit. The same can be said for RF signal, but with different materials.

Think about a house. In some rooms, the wireless signal is weaker than others, regardless of the distance from the wireless access point (WAP, Wi-Fi Router). This is due to the ability of the RF signal to “shine through.”

When this signal is reflected off, even in small parts, that information is given to us in the form of Channel State Information (CSI data). That is data about the objects that a signal encounters.

AI, or in this case, machine learning, is able to sort through and parse amounts of data that a human could spend their whole life on, in a matter of moments. Using this logic, and this understanding of things, we can infer two things:

1. Proper data parsing will be able to tell us whether or not the signal is encountering something, given the correct architecture of the technology.

2. We can use the data to create an image of anything the RF signal encounters in real time.

Using these two assumptions, we are able to create a real life video feed of anything the signal encounters, provided we have correct information, and the architecture is properly built.

This relies on a few things to ensure its success.

1. The WAPs must number at least three for any given area. This is to provide proper triangulation, and avoid any issues that may arise due to interference from specific absorbing forces in a space. (Metal, humans, others.)

2. An AI that has been fed enough information to interpret the CSI data it will be processing.

3. A computer with sufficient resources is able to process the data.

4. The environment is not hampered by additional reflective or absorbing forces. (We can work around 99% of issues. There is likely not a situation we cannot work with.)

Once the above conditions are met, the assumptions rely only on having the CSI data, and having an AI that can interpret that data.

The above conditions being met ensures that we are able to see through walls, under desks, behind closed doors, and in all manner of obscured environments. (Darkness, smoke, dust, airborne debris.) The next step is crucial in ensuring that it is able to function as CCTV or other surveillance offerings, as this only guarantees a wire frame or heat map image of the environment.

The next step is simple, yet difficult to ascertain the amount of resources needed to provide such a robust solution. Using a technology like stable diffusion, we will utilize exterior cameras to pull data from people and objects coming into a building, and provide that information in real time, and uninterrupted. This will ensure that the people who are going in and out are able to be monitored in real time, real color, and in real life capacity, far better than traditional surveillance is capable, as we will be able to track anything and anyone behind walls, in certain rooms, or anywhere that is required to ensure that surveillance is perfect.

There is one more step to ensuring perfection. The inside of a building. Up until this point, we’ve been able to see people through any object as wire frame models, and then assign their image data to them to ensure that they’re surveilled perfectly in any environment. The last step that remains is ensuring the environment around them is captured.

This is by far the easiest part. LiDAR (Light Detection and Ranging) is a remote sensing technology that uses laser light to measure distances and create 3D models of the environment by emitting pulses and measuring the time it takes for the reflected light to return. This will ensure that the entire environment is captured perfectly in 3D.

The final piece of the puzzle is putting all of the pieces together. The software used to display all of this will need to be capable of using the LiDAR image to be the constraints of what is required to surveil, and also be capable of assigning the 3D models of people that we will make to their proper size, and relative location within the environment. This will ensure that the surveillance is done correctly, perfectly, and with 100% accuracy.

Combine all of this into one single application, and then all that’s needed is the hardware, and to have an understanding of that, and how it operates to be able to set this all in motion.

The hardware commonly used for surveillance is cameras, cabling, a network switch, NVR, UPS, and a computer. Cameras capture the data, sending it to the network switch via the cabling, and then it is stored on the NVR (Network video recorder) for however long is required by the organization. (Usually 30 days or less.) Depending on the state the business is located in, they will need to have a UPS (uninterrupted power supply) device that is capable of powering the surveillance system in the event of a power outage for x amounts of hours. (x being the legal variable that depends on the state. For example, Utah is 3 hours.) All of that is tied to a computer that will be able display the information for the assigned person to see.

For Sentinel AI Security, we will require cameras on the exterior covering entrances and exits to a building, WAPs, cabling, NVR, UPS, network switch, and a computer. The average business can vary in the amount of entrances and exits, as well as the capacity for a camera to view. Let’s look at a hospital, for example. Many wings, many cameras, few entrances or exits (relative to size.) Cameras will be placed around the exterior doors, and those will feed into the network switch. That camera data will be fed into the NVR device for storage, and into the computer for processing on the AI. Once a person enters, the WAPs will be able to see them, and will assign the image data from the cameras to them. The person will be able to be seen in 3D from any angle, and in any room that they go into. This will allow for functionality in any and every space at a fraction of the cost.

The portion of this that everyone will wonder will be simple. “What about the cost?”

For such a high tech solution, we will provide the hardware and the software at a reasonable cost that will greatly improve cybersecurity infrastructure. Typically, enterprise grade hardware for surveillance is expensive. A single camera is between 300 and 600 dollars. The NVR is upwards of 1000, the UPS is generally about 3000, a single network switch is over 1000, and then the final costs come from cabling and installation, which is an absolute mess, and takes dozens of hours to complete.

Sentinel AI Security can install in less than half of the time, our access points cost under half of what a camera does, we require far fewer of them, we are able to use half of the network switches or less than is required for a building, our UPS doesn’t have to be nearly as expansive, and we use less power on average. Combine that with the ability to reuse your exterior cameras, and we will come in at roughly 50% or less of what a company is currently paying, and we’ll be able to do far more.

This technology is capable of doing far more than just surveillance. For more, look at “Triton Malware and Sentinel AI Security” to find out how we’re able to do at least double what our competition can, and see exactly how many industries we’re capable of dominating.

Our technology. Better than anything else on the market.