Multi-Agent Systems (MAS) have become a formidable paradigm in the solution to complex problems which demand distributed decision-making, autonomous systems, scalability and resilience. The conventional centralized decision-making strategies are not the most suitable in a situation where there exist a decentralized environment, uncertainty, and dynamic interactions, and therefore they tend to fail to increasingly achieve the requirements of performance and reliability. The concept of multi-agent systems to solve the issues associated with the deployment of several agents that interact in an intelligent manner is good since it allows agents to cooperate together or to compete with each other or they can make their decisions collaboratively and competitively. The paper provides an in-depth discussion of multi-agent systems in distributed decision-making already based on their theoretical basis, architectural models, coordination, and applications. The research examines the main concepts, which include agent autonomy, communication protocols, negotiation strategies, consensus algorithms, and the learning based coordination. An in-depth literature review of classical and contemporary research articles has been done, showcasing how MAS has transformed over the years to be rule-based systems, learning-based and self-organizing systems. This methodology proposes a generic architecture of MAS that deals with distributed decision-making that includes agent perception, local reasoning, coordination and global optimization. Decision functions and mathematical models are discussed in order to formalize the interactions between agents and collective action. The success of MAS in references to the dimensionality of scalability, fault tolerance, and accuracy of decisions is proven by the experimental outcomes of real-life application scenarios. The paper will also conclude with a summary of the main findings, limitations, and future directions of research like explainable multi-agent learning and real-world deployment on a large scale.
Fatou Diop· International Journal of App...· 0 citations
The Internet of Things (IoT) technologies and their high growth rate in the context of data science approaches have led to the significant shift in paradigm of smart home automation. Conventional home automation systems are mainly dependent on the rule-based measures of control and do not offer flexibility and intelligence. Modern smart homes are expected to be more automated and energy efficiency, comfortable to the user, and secure through the combination of data-driven solutions like machine learning, predictive analytics, and real-time data processing. This essay is a detailed research of the use of IoT and data science in the automation of smart homes. The suggested interface focuses on sensor-based acquisition of data, cloud storage, and the smart decision-making process based on machine learning models. Modular architecture: It is a form of architecture that is created to support interoperability and scalability among heterogeneous devices. The most common challenges include privacy of data, system consistency and efficiency. The experimental performance proves that data science-based automation is much more efficient than the conventional approaches to automation concerning energy optimization and prediction of user behavior. The results validate the fact that the combination of IoT and data science forms a strong venue of the future smart houses.
Fatou Diop· International Journal of App...· 0 citations
This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability.
Fatou Diop· International Journal of Art...· 0 citations
The distributed databases now become a building block of modern computing infrastructures under the influence of the dramatic increase in data volumes, the evolution of cloud computing, and the need to manage large-scale, fault-tolerant, and performance-intensive data warehouses. The conventional centralized database architecture is no longer adequate to support the needs of the modern applications including cloud services, Internet of Things (IoT), big data analytics, real-time processing and globally distributed web applications. Distributed database system handles these issues by partitioning, replicating and controlling data in more geographically dispersed nodes whilst ensuring consistency, availability and reliability. This paper entails a detailed analysis of distributed database in the contemporary computer use. It discusses the architectural concepts, design techniques, data dispersion strategies, and consistency models and techniques of dealing with transaction in a distributed database system. An in-depth literature review reflects the development of distributed databases, the primitive models of client-servers to the new cloud-native and NoSQL databases. The given methodology analyses the system design issues as part of it, such as data partitioning, replication scheme, concurrency control, and fault resistance. The analysis of the performance evaluation is done through experimental assessment and discussion of performance measures which include the latency, throughput, scalability, and availability. The paper has summarized by determining major issues, new trends and subsequent research agenda in the distributed database systems.
Fatou Diop· International Journal of App...· 0 citations
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