edge computing

Background: Neuromorphic computing is a newly developed technology that is based on data-flow architectures similar to the brain, which has the potential to power energy-constrained, latency-sensitive, and large-scale applications. The lack of flexibility in energy consumption and response time of traditional systems is a problem where neuromorphic platforms shine in real-time applications like robotics, IoT and autonomous systems. Objective: The article aims to assess the capabilities of neuromorphic computing platforms with respect to conventional schemes, both quantitatively and qualitatively, in terms of energy consumption, response time, modularity, and application-dependent adaptability, and to determine the drawbacks and application prospects for its further development. Methods: The study uses a comparative analysis approach to compare the identified factors and make statistical comparisons of the performance measures. The performance of the neuromorphic platforms as compared to non-neuromorphic platforms like Intel Loihi, IBM TrueNorth, NVIDIA Tesla V100, and Google TPU is compared based on its applications in robotics, IoT, and especially in healthcare. Data is derived from the experimental assessments of knowledge and theoretical paradigms encountered in prior research studies. Results: Neuromorphic systems showed better energy consumption, system size, and delay characteristics. Nevertheless, that the algorithm so excellently solves particular tasks does not mean that it can successfully be used regardless of its purpose, or can be adapted freely to new, further-reaching trends, such as quantum computing. Regression results demonstrate a high degree of dependency between these measures as well as their potential for real time data processing. Conclusion: Neuromorphic computing can be regarded as a new paradigm of energy-efficient and scalable AI and is especially promising for latency-sensitive deployment. Their shortcomings have been discussed earlier, yet it is worth stating that extension of these approaches by hybrid systems and more sophisticated integration frameworks might open new opportunities and eventually promote them as a foundation for new-generation computation models.

Background: The growth of the number of connected devices and the extent of Internet of Things (IoT) integration has led to new and emerging needs such as the management of big data, real-time reaction, efficient bandwidth utilization, and security considerations. Due to the intrinsic latency, network load and argue of scalability, standard cloud computing models do not suffice these requirements. In response to this, edge computing the function of analyzing data closer to its source hence leading to performance gains. Objective: This article explores the impact of incorporating edge computing in the optimization of IoT systems specifically in aspects like latency minimization, bandwidth utilization, security, processing capability, flexibility in expansion, and data reliability. Methods: A combined computational model was used to mimic edge and cloud platforms. Performance metrics were evaluated under three primary IoT scenarios: traffic management of smart cities, industrial applications, and health care management applications. Regression models and confidence intervals also provided general support to the findings. Results: The findings showed edge computing to be a more effective substitute for cloud-based systems; proving that latency can be reduced by 82%, and data bandwidth by 65-68%. Perennial threats including interception of data were cut by 50-66% while processing was done at 73% higher efficiency. Other criteria such as scalability and data consistency also pointed out the application of edge computing for resilience in more extensive IoT environment. Conclusion: Essentially, edge computing helps overcome limitations of cloud-based IoT systems, and is therefore imperative to real-time, secure, and scalable IoT. Future work should consider the integration of hybrid edge-cloud models, self-healing schemes, and more robust rigorous security solutions in order to fine-tune its applicability.

Background: The telecommunications’ growth, especially with the emergence of 5G, has led to the requirement of low latency solutions. Current cloud computing models possess architectural flaws that prevent real-time service delivery, critical in applications of autonomous vehicles, augmented reality among others. Objective: This article reviews how edge computing can be combined with 5G networks to overcome the latency issues in today’s telecommunication systems. They look at how this combination can cut down latency by processing data closer to the end consumer and its potential to disrupt several industries. Methods: This research uses the literature review of current information in 5G and edge computing systems, architectures, practices, and theoretical frameworks. The result of the work is based on the assessment of the existing solutions in the implementation of edge computing within the 5G environment based on case analysis. Results: The analysis shows that all the applications such as self-driving cars and industrial robotics experienced 40 to 70% reduced latency. Also, edge computing results in better resources management in case of telecommunications since it deems many computing tasks to localized edge nodes from cloud. Conclusion: Combining edge computing with networking also provides a distinctive model for addressing latency problems while enhancing the network and boosting industry development. Concerning the research limitations, the future research should explore ways of improving the efficiency of resource allocation to meet the company’s needs and explore the scalability issues.

Background : The increasing demand for flexible, resilient, and high-performance telecommunication systems—especially in dynamic environments—has led to growing interest in the use of autonomous drones. Their mobility and adaptability make drone swarms a promising solution for enhancing communication networks, particularly in 6G and edge computing applications. Objective : This study explores the application of drone swarms to improve network formation, synchronization, and resilience in both urban and rural telecommunication scenarios, with an emphasis on their feasibility, robustness, and adaptability. Method : A series of simulations were conducted using multi-agent coordination algorithms and network optimization models under varying conditions. Key performance indicators including Packet Delivery Ratio (PDR), latency, energy efficiency, and system reliability were evaluated across different deployment scenarios. Results : The findings indicate that drone swarms achieved a 92% PDR, a significant improvement over the 75% observed in static wireless network (WN) bases. Additionally, average latency decreased by 35%, while energy efficiency increased by 28%. The swarm-based system maintained robust performance even with up to 20% node loss, demonstrating strong fault tolerance and adaptability. Conclusion : The study confirms the potential of drone swarms as a scalable and resilient solution to address critical telecommunication challenges such as disaster response, rural connectivity, and real-time data transmission. Future work should focus on addressing remaining deployment barriers, including regulatory concerns and seamless integration with existing telecommunications infrastructure.

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.