AI-Powered Traffic Routing System and Method Based on Context and Renewable Energy Awareness

Abstract

This invention relates to a system and method for energy-efficient and adaptable traffic routing in 5G, 5G-Advanced, and 6G communication networks. Within the scope of this invention, the energy consumption of network nodes, renewable energy production status, traffic type, network load, quality of service requirements, and network risk status are evaluated together, and a combined suitability score is calculated for each candidate path using an artificial intelligence-powered decision engine. Based on the calculated suitability score, the most suitable routing path is selected, and traffic is routed along this path. Thanks to this invention, total network energy consumption is reduced, the use of renewable energy is increased, quality of service is maintained, and an intelligent traffic routing system capable of adapting to changing network conditions is achieved.

Description

1 TARIFF AI-Powered Traffic Context-Based and Renewable Energy Awareness Navigation System Technical Area 5 The invention enables traffic routing in next-generation communication networks such as 5G, 5G-Advanced, 6G, and others. It relates to methods. More specifically, the invention concerns the energy consumption of network nodes, renewable energy. energy production status, traffic type, network load, quality of service requirements, and network risk statuses. AI-powered dynamic traffic routing using contextual information such as... It is related to a system and method of implementation. 10 The invention relates particularly to MPLS, SDN, IP-based transport networks, core networks, and access to increase energy efficiency in network (RAN) and hybrid communication architectures It is available for use. State of the Art 15 Nowadays, traffic routing in communication networks is mostly done using the shortest path. based on the principles of path), fixed cost (static cost) or load balancing This is how it is implemented. However, these methods increase the energy consumption of network nodes. taking into account the renewable energy production status or the immediate contextual conditions of the network It does not receive. 20 Increased data traffic, particularly on 5G and 6G networks, leads to high energy consumption, and This increases the operating costs of the operators. Also, nowadays some network nodes are solar-powered. or powered by renewable energy sources such as wind energy. Existing traffic Orientation methods, production quantities and availability of these energy sources Because their conditions are not taken into account, energy resources cannot be used efficiently and 25 Energy efficiency is decreasing across the grid. In addition, traffic type, time of day, quality of service (QoS) requirements, network load, and potential Contextual information such as risks of failures or interruptions is also included in the current routing algorithms. It is not used to a sufficient extent. In conclusion, due to the drawbacks described above and the current solutions regarding issue 30 Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. It has arrived. Purpose of the Invention The invention was created by drawing inspiration from existing techniques and overcoming the aforementioned drawbacks. 35 It aims to eliminate it. 2 The main purpose of the invention is to analyze the energy consumption of network nodes, the status of renewable energy production, traffic type, network load, quality of service requirements, and network risk status all together. a system that performs AI-powered dynamic traffic routing by evaluating and The goal is to offer a method. Another aim of the invention is to improve the traditional shortest path, with its static cost. or unlike load balancing-based traffic routing methods, the network A multi-criteria and adaptable decision-making mechanism that takes into account current working conditions. to create. Another aim of the invention is to provide a path for each candidate on the network;  Energy efficiency score, 10  renewable energy eligibility score,  Quality of Service (QoS) score,  context appropriateness score and  risk score by calculating a combined suitability score and directing traffic according to this score. to accomplish. Another aim of the invention is to enable network nodes to be powered by solar energy, wind energy, and battery systems. or the amounts of energy obtained from similar renewable energy sources and using energy resources more efficiently by taking their availability into account to provide. 20 Another purpose of the invention is to analyze traffic density on the network, time of day, type of service, Adapting to changing network conditions by considering user requirements and network load together. The goal is to create an intelligent traffic routing mechanism that can provide this. Another purpose of the invention is to prevent malfunctions and overloads that may occur in network nodes, to assess risk situations such as connection interruptions or power outages in advance and 25 The goal is to direct traffic in a way that minimizes these risks. Another aim of the invention is to analyze different aspects of the network through an artificial intelligence decision engine. evaluating alternative orientation options, energy consumption for each alternative, renewables to analyze energy usage, service quality and risk parameters together and to find the most suitable The goal is to make the decision regarding direction. 30 Another objective of the invention is a feedback mechanism that continuously monitors network performance. With its help, system parameters are updated, thus adapting to changing network conditions. The aim is to increase the adaptability of the system. Another aim of the invention is to reduce total grid energy consumption and increase the use of renewable energy. The goal is to increase the rate, maintain service quality, and reduce operators' operating costs. 35 3 Another objective of the invention is to enable the system in question to be 5G, 5G-Advanced, 6G, MPLS, SDN, and IP-based. transport networks, core networks, access networks (RAN), and those to be developed in the future The aim is to ensure easy integration into new generation communication architectures. The invention, context, and renewable energy are used to achieve the objectives described above. It relates to an AI-powered traffic routing system and method based on awareness. 5 Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. Description of Part References 10 1. Network data acquisition module 2. Context analysis module 3. Renewable energy assessment module 4. Risk assessment module 5. Artificial intelligence decision engine 15 6. Path selection module 7. Traffic routing module 8. Performance monitoring and feedback module Detailed Description of the Invention 20 In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. This information is provided for the purpose of understanding and will not have any limiting effect. The invention relates to the energy consumption of network nodes, renewable energy production status, traffic type, and network load. artificial intelligence by evaluating service quality requirements and network risk situations together. Context and renewable energy 25 that implements supported dynamic traffic routing It relates to an awareness-based traffic management system and method. In an example application of the system that is the subject of the invention shown in Figure 1, network data collection module (1) provides information about network topology, node and link loads, traffic demands, latency, It collects parameters such as packet loss, energy consumption, and quality of service. Context analysis module (2), traffic 30 with data provided by network data collection module (1). contextual factors such as type, time of day, network load, user requirements, and network uptime. It creates context parameters for each traffic request by analyzing the information. Renewable energy assessment module (3) allows network nodes to assess solar energy, wind energy, amounts of energy obtained from battery systems or other renewable energy sources and by evaluating the availability status, renewable energy suitability for each node 35 It produces information. 4 Risk assessment module (4) assesses the failure probabilities of network nodes, overload conditions, By analyzing risk parameters such as connection interruptions, power shortages, and similar issues, each candidate It creates risk indicators for the road. Artificial intelligence decision engine (5), context analysis module (2), renewable energy assessment Using the information generated by module (3) and risk assessment module (4), network 5 Each candidate on the route calculates a combined eligibility score. In a sample application of the invention, the combined fitness score is as follows: is being calculated: 𝑆 = 𝑤 𝑄 + 𝑤 𝐸 + 𝑤 𝑅 + 𝑤 𝐶 + 𝑤 𝐾 10 Here;  S: combined suitability score of the candidate pathway,  𝑄: Quality of Service (QoS) score,  E: energy fitness score,  R: renewable energy eligibility score, 15  C: context appropriateness score,  𝐾: risk score,  𝑤 ,𝑤 ,𝑤 ,𝑤 ,𝑤 : weighting coefficients of the relevant parameters It expresses. The path selection module (6) calculates the combined fitness 20 by the artificial intelligence decision engine (5). By evaluating their scores, the most suitable among the candidate pathways that meet service quality requirements is selected. It chooses the appropriate navigation route. The traffic routing module (7) implements the selected routing path on the network and traffic The flow occurs via the designated path. Performance monitoring and feedback module (8), network latency, energy consumption, packet 25 We continuously monitor performance metrics such as loss, service quality, and renewable energy utilization rate. It monitors as follows. The information obtained is fed back to the artificial intelligence decision engine (5) and the system It allows the parameters to be updated. This invention thus provides access to the shortest path or fixed-cost based routing methods currently available. According to the service, it increases the use of renewable energy while reducing the network's energy consumption. It maintains its quality and can adapt to changing network conditions. 35

Claims

REQUESTS 1. Network node energy consumption, renewable energy production status, traffic type, network load, artificial intelligence by evaluating service quality requirements and network risk situations together. context and renewable energy that implements supported dynamic traffic routing It is a traffic management system and method based on awareness, and its characteristic is; 5  Network topology, traffic load, connection status, energy consumption, and quality of service. Network data acquisition module (1) which collects parameters,  Context that analyzes traffic type, time of day, network load, and user requirements analysis module (2),  renewable energy production amounts and availability status of network nodes 10 Evaluating renewable energy assessment module (3),  Risk situations of network nodes such as failure, overload and power shortage Risk assessment module (4),  using context, renewable energy, energy consumption, service quality and risk information AI decision engine that calculates a combined suitability score for each candidate path (5), 15  Path selection module which selects the routing path according to combined suitability scores (6),  traffic routing module (7) which implements the selected routing path on the network,  Performance monitoring, which updates system parameters by monitoring network performance. feedback module (8) It includes. 20 2. Traffic routing system and method according to Claim 1, characterized by artificial intelligence decision-making. energy suitability score, renewable energy suitability score for each candidate road of its engine (5), It calculates the service quality score, context appropriateness score, and risk score.

3. Traffic routing system and method according to claim 1 or 2, characterized by; combined 25 The fitness score is calculated using a weighted decision function.

4. A traffic routing system and method according to any of the requirements 1-3, and its characteristics are: renewable energy assessment module (3) solar energy, wind energy, energy It is the use of data obtained from storage systems or similar energy sources. 30 5. A traffic routing system and method according to any of the requirements 1-4, and its characteristics are: performance monitoring and feedback module (8) delay, packet loss, energy consumption, AI makes decisions by monitoring service quality and renewable energy usage rates. It is updating the parameters of the engine (5). 35

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