There are various mobile edge computing AI technologies that have been put into practice at the industrial edge. For example, facial recognition, autonomous driving, and predictive maintenance are implemented in various industrial applications. To learn more about the impact of Mobile Edge Computing AI on the Industrial Edge, let’s observe how the enhancement of edge AI impacts some current and future industrial applications:
Smart City
Deep learning enables edge AI computers to understand traffic conditions in real time and detect abnormal behavior through smart cameras. Another smart city application that leverages machine reasoning is license plate recognition on roads and in parking lots. AI implementations in various IoT devices allow cities to gain actionable and valuable insights through AI at the edge. Smart cities can analyze and act on vast amounts of data in real time, helping to improve city operations, thereby increasing productivity and efficiency.
car
Mobile edge computing provides control and autonomy to the automotive industry. For example, the integration of edge AI into the fleet telematics industry makes it easier for users to remotely monitor cars through portable devices such as mobile phones, laptops, and tablets. Additionally, fleet vehicle companies can implement edge AI in each vehicle to provide better telematics with more control, insight and safety. Some of the features available from integrating edge AI into vehicles are more efficient map rerouting to save gas on the go. Additionally, edge AI provides automatic alerts to drivers and control stations when the system detects anomalies that could lead to downtime. Additionally, since most of the computation is done locally at the edge, fleet vehicles can process data in real time with lower bandwidth and power. Edge AI being independent of the cloud also improves security by preventing data breaches from computers. In the future, AI at the edge will enable self-driving cars to drive fully autonomously, even in rural and remote areas.
oil and gas
Mobile edge computing improves the use of predictive maintenance in the oil and gas industry. It supports real-time data analysis to monitor whether operations are running smoothly. For example, if a sensor detects a leak in a pipe, the computer will order the flow to stop and alert the maintenance team. Thus, it increases the level of safety for workers and the surrounding environment. Edge AI also helps reduce costs by moving AI computing from the cloud to rugged edge computers located closer to oil and gas assets, minimizing bandwidth requirements and downtime.
agriculture
In agriculture, edge AI helps farmers track soil and weather conditions in real time. After collecting all the data from the sensors, the edge computer can decide the correct schedule for fertilization and harvesting. Mobile edge computing also enables machine vision to identify and differentiate plants, choosing an action to take for each plant species. With the help of AI at the edge, the time spent on day-to-day management and daily harvesting is reduced while increasing harvesting throughput.
manufacturing
Artificial intelligence enables ruggedized edge computers to quickly detect defects and errors in the production chain through high-precision and real-time machine vision that cannot be detected by human eyes. The support of rugged edge computers allows the process to work efficiently and reliably while maintaining high precision and low production costs. In addition, real-time detection analysis also allows computers to filter out damaged goods on the production line without downtime, thereby reducing downtime costs.