Future of Edge Computing with the Fusion of AI + IoT

Today the concept of edge computing is gaining popularity: data processing is gradually moving to the edge, to IoT devices that directly collect this data. The next step to the more efficient analysis of information without delay is artificial intelligence (AI). Therefore, the emergence of a hybrid of the Internet of Things and AI called AIoT, i.e., AI + IoT, became quite logical. The Article when implementing such an approach. 

Although the concept of the Internet of Things (IoT) has been around for a long time, it is constantly evolving, especially in light of our rapid technology development. It can be said that IoT is the epitome of the gradual merging of the physical and digital worlds, as data is collected from an ever-increasing number of devices and then combined into "Big data". The number of such IoT devices keeps increasing at a high pace.

However, when trying to transfer the data collected by IoT devices to centralized storage, such as the cloud, there is a problem with the delay in their transfer. In many ways, even though the connection speed is constantly increasing, the characteristics of this process do not match the data growth. If you transfer unprocessed data that is "raw" all in the crowd, then the delay will increase, and, therefore, the system's overall performance will suffer. 

Data processing is one area where AI can make a significant contribution. It also paves the way for technological innovation in areas ranging from streamlining urban transport to improving public safety and financial services.

Pure IoT devices collect data with only small or specific amounts of computation. For further analysis, the data is sent to the cloud. However, in such packages, not all data have the same value. For example, video materials for a security system: the system needs frames in which people or certain objects are moving, while pictures of an unchanging background are not of particular interest. Sending all survey data to the cloud for analysis will consume bandwidth that could be put to better use.

The transfer or implementation of AI to the edge can require many computing resources. Standard storage and memory devices will help provide the performance you need, but the problem is that commercially available components of this type tend to be ill-suited to the harsh environments typically found in edge applications. For example, when monitoring traffic at the location of IoT devices, cyclic temperature changes are possible from day to night and from summer to winter. In addition, automotive systems must withstand shock and vibration, while industrial systems must withstand increased pollution levels, etc.

When we talk about the symbiosis called AIoT, we usually mean an AI platform located at the network's edge. Typically, this solution takes the form of a small industrial computer (IPC) with an embedded industrial-grade processor. However, for real-time data analysis, such a processor needs adequate support in flash memory and disk storage. 

To solve the problems of implementing AI in edge applications, as mentioned above, industrial-grade storage and memory devices are needed. The first step is to study and identify the risks present in each specific data collection location. This will allow the components to be executed according to the exact requirements of a particular application. Let's consider several examples of implementation of the proposed solutions.

Our cities are growing in three dimensions, expanding in width and length and striving upward due to the increase in the height of buildings. However, roads are still limited mainly to two dimensions, which, as cities grow, entails an increase in traffic jams and, as a result, can lead to a transport collapse.

Monitoring and changing the flow of traffic based on real-time data can significantly improve the efficiency of the transport system and reduce congestion. This can be done with the help of connected surveillance systems placed in a certain way throughout the city. 

In such a project, the first stage of analysis is performed by local AI platforms at the edge of such a network. It includes vehicle recognition and traffic saturation estimation. In this way, each installation can independently determine how to process data to find out if the number of vehicles is increasing and if there is a risk of congestion. All critical data is then sent to a centralized platform, where actions such as traffic redirection, speed limit changes, and traffic light adjustments can be taken based on it.

Supervising a large fleet of vehicles can be a very complex and time-consuming task, but there are many ways AI can optimize operations, such as reducing fuel costs, vehicle maintenance, mitigating the risk of unsafe driving, etc. 

Modern positioning systems are mostly dependent on GPS, leading to specific problems. So, for example, entering a tunnel or underground parking makes GPS almost useless, and the tracking system will not know where such a car is. Similar disturbances occur in cities when driving inside buildings or other places with poor satellite coverage. It can also be difficult for a GPS-based system to determine the vehicle's height.

However, other data sources can provide vehicle position information. In particular, the speed of the vehicle and all turns can be constantly monitored and recorded. In addition, the AI-based on-board platform can calculate the vehicle's position at any given time: in this way, these parameters compensate for incomplete data from GPS. This technology is called Automotive Dead Reckoning (ADR). Finally, data can be transmitted via wireless networks back to the operator.

When we remove the human factor from vehicles, the main problem we face is the ever-changing traffic pattern, fraught with surprises. Because of this, an autonomous vehicle must be able to make decisions in just a fraction of a second to react to any sudden change in its path. If we rely only on our senses, then the robot has a lot of sensors that collect all sorts of data, which, in turn, must be processed appropriately and added to a consistent picture that displays the overall situation at any given time along the route. In this case, relying on the cloud is hopeless since the delay will surely mean that it will be too late by the time the data is ready and the decision is made.

However, we must not forget that the embedded AI platform that performs all these complex calculations depends on components that work in weather and physical conditions without any performance degradation. Furthermore, to avoid accidents involving autonomous vehicles, the equipment should operate with a minimum probability of failure and sufficient redundancy for a particular application.

Artificial intelligence has already become the norm in our world, and as its role in the Internet of Things becomes more critical, we need to look for "smart" solutions that make it easier to merge them. In addition, AI will soon be poised to supplant the human operator in many areas of activity, further highlighting the need for robust systems that can handle any given ecosystem challenge.

Leveraging AI platforms along with industrial-grade storage and memory solutions is a way to ensure the hardware is ready to perform and is one of the keys to building the IoT of the future.