Autonomous Systems

Background: The rapid evolution from 4G to 5G has transformed the telecommunications landscape, but as technological demands continue to grow, the shift toward 6G is gaining attention. 6G aims to address the limitations of 5G, such as latency and bandwidth constraints, while introducing new capabilities like terahertz communication and ubiquitous AI integration. Objective: This article explores the development roadmap of 6G, highlighting its applications across industries and addressing key challenges in its deployment. Methods: A comprehensive review of current literature on 5G advancements and emerging 6G technologies was conducted. Comparative analyses were performed on the theoretical frameworks of 6G’s core capabilities, including network architecture, spectrum management, and AI integration. Results: The study identified key applications for 6G, such as smart cities, autonomous transportation, healthcare, and industrial automation. It also highlighted the anticipated improvements in data transmission speed, reliability, and connectivity. Conclusion: 6G represents a pivotal evolution in telecommunications, offering transformation in numerous sectors. However, challenges such as infrastructure development, regulatory frameworks, and energy efficiency must be addressed.

Background: Network slicing has turned out to be one of the key enablers in the 5G networks due to the ability to support the diverse applications such as ultra reliable and low latency communications for the self-driving cars or IoT-like massive machine type communications. Prior expeditions lacked integrated tools for the dynamic assignment and allocation of resources and no possibility for maintaining constant QoS. Objective: In this article, the primary aim is to synthesis and test a reinforcement learning–driven slicing framework in order to orchestrate the resources of the three types of slices – URLLC, mMTC, and eMBB. This is to improve the performance of the sliced resource, ensure high availability, and minimize competition of the resources in multi-tenant scenarios in 5G networks. Methods: The proposed study design includes a focus on the key stakeholders and their needs for requirements gathering and an experimental field for actual implementation. Resource distribution is guided by the reinforcement learning algorithms by trying to minimize a cost function which incorporates the relation between the latency, isolation, throughput and energy expended. Using a number of runs, quality of performance is monitored to enable assessment of stability as well as response rates. Results: Experimental results show that the proposed framework achieves a lower level of latency violations and capacity oversubscription compared to heuristic methods. Furthermore, it consistently achieves nearly 2.5X better throughput for telemedicine slices and guarantees less than 5 ms latency for time-sensitive services during dynamic traffic conditions. Conclusion: The study shows how reinforcement learning can be effective and applied for end-to-end 5G network slicing. This sort of adaptive orchestration can increase service dependability while optimising overhead and herald instantly climbable multi-tenant networks compatible with various industries

Background: Enabling Edge Artificial Intelligence (Edge AI) to be implemented in autonomous systems and telecommunications can offer for improved real-time data, non-recurring latency, enhanced operational proficiency. Some empirical research suggests that Edge AI minimizes latency by 70%, enhances computing speed by 50%, and cuts bandwidth consumption by 30% in the most demanding cases. Objective: The purpose of this article is to investigate how Edge AI can serve as an enabling technology for the future of self-sustaining environments such as autonomous mobility and telecommunications in terms of measured utility and differentiation. Methods: Screening 120 refereed articles and 25 case studies connected to Edge AI application in telecoms and self-governing systems, this systematic looked-for patterns in the proximal research and promising agendas. The review encompassed research works concerned with latency minimization, bandwidth enhancement and enhancement in the processing capacity. Focus was made on application areas like self-driving cars, industrial IoT, and smart city platforms and performance analysis was made in these areas. Results: The current study prove that when employed in autonomous systems, Edge AI enhances decision making reaction time by 40-60%, while enhancing data traffic throughput within telecommunications networks by 35%. Further, Edge AI makes the overall energy consumption lower in IoT-based applications by cutting down the average usage by a quarter thus creating a sustainable network. Conclusion: Edge AI becomes a central tool in the development of self-driving cars and telecommunications, increased performance and ability to handle mass amount of data at a low latency. These developments place Edge AI at the base of the evolution of future intelligent systems as the basis for smarter and more responsive technological landscapes.

Background: With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time. Objective: This article seeks to discuss the role played by AI self-defending mechanisms in autonomous network security given their effectiveness in threat detection, response time, and the overall harm that can be caused to networks by cyber criminals. Methods: Three separate studies were made, including conventional security systems, and analytically compared them with the AI-driven system across 100 different network environments. Machine learning (ML), deep learning (DL), and other forms of AI were applied to identify and counteract distinct threats like viruses, phishing, and even DDoS attacks. Detecting accuracy, response time and ability to mitigate attacks where among some of the other factors that were examined. Results: Automated threat intelligence systems have a 92% accuracy while legacy systems only have 78%. Mean response time was also decreasing by 65% from 45 seconds to 15 seconds. A significant increase to attack mitigation rates was noted with fifty percent effectiveness of the AI programs averting 85 percent of the threats in the first 30 seconds of identification. Conclusion: Autonomous threat response systems substantiate AI, which function as a radically superior replacement to conventional network security structures, minimizing threat response time and boosting the overall threat neutralization outcome. Incorporation of these types of secure mechanisms into contemporary security landscapes is important as a means of counteraction against new forms of cyber threats.