New | Article Five | 2min read
Optimising undersea connectivity with artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) have transformed numerous sectors, including subsea communication. These technologies are becoming increasingly significant in optimising underwater communication and boosting the speed and efficiency of long-distance data transfer. In this article, we will look at how AI and machine learning may be used to improve submarine communication. One of the most important advantages of AI and ML in subsea communication is their capacity to improve data routing and switching. AI and machine learning algorithms can evaluate network traffic patterns and dynamically alter data routing and switching to ensure the most effective path is selected while lowering latency. This improves the speed and efficiency of underwater communications by shortening the time it takes data to go from one end of the planet to the other.
The enhancement of network maintenance and administration is another essential function of AI and ML in subsea communication. Artificial intelligence and machine learning algorithms may be used to monitor the health and performance of subsea networks, finding and anticipating problems before they become serious. This can help to decrease downtime and enhance the dependability of subsea communications, ensuring that organisations and individuals remain connected at all times. Furthermore, AI and machine learning are being utilised to forecast and avoid cable problems in subsea networks. AI and ML systems can anticipate the possibility of a fault developing by evaluating data on cable consumption, allowing preventative steps to be done before a failure occurs. This can assist to decrease downtime and increase the dependability of underwater communications.
Another use of AI and ML in underwater connection is network capacity optimisation. AI and machine learning techniques may be used to optimise network resource use, ensuring that bandwidth is utilised efficiently and effectively. This can help to minimise latency and boost bandwidth, resulting in faster and more efficient subsea communications. AI and machine learning are also being used to increase the security of subsea communications. AI and machine learning algorithms may be used to identify and prevent cyber-attacks, as well as to ensure the security and integrity of data carried through subsea networks. This can assist to ensure the security of subsea communications by preventing critical information from being intercepted or stolen.
In addition to this, artificial intelligence and machine learning are being utilised to drive the creation of new subsea networks. Algorithms for each may be used to simulate and optimise the architecture of subsea networks, decreasing the risk of technical faults and assuring the efficiency and dependability of underwater communications.
It is undeniable that AI and ML are becoming increasingly crucial in maximising subsea connectivity. Their ability to enhance network maintenance and administration, forecast and avoid cable problems, optimise network capacity, increase security, and promote the creation of new subsea networks has a substantial influence on the speed and efficiency of underwater communications. The ongoing advancement of AI and ML technologies will almost certainly have an even bigger influence on future subsea communication.
Found this article interesting? Stay tuned for the next article in SUBCO’s ten-part series on submarine optimisation and deployment, where we present strategies for reducing latency and increasing bandwidth in undersea communications.