This paper explores the integration of network programming and microservices architecture to build scalable, AI-driven distributed systems for real-time data processing. As artificial intelligence becomes increasingly crucial for real-time decision-making in industries like healthcare, finance, and e-commerce, there is a growing need for systems that can process vast amounts of data efficiently while ensuring scalability and low latency. Network programming techniques are foundational to distributed systems, enabling seamless communication between services. Meanwhile, microservices provide a modular approach that supports scalability and flexibility, essential for AI applications. The paper discusses the role of these technologies in building AI-powered distributed systems, challenges related to network latency, data consistency, and fault tolerance, and real-world applications across industries. Additionally, it delves into future trends such as edge computing and automated scaling in the context of AI-driven distributed systems.
Rahul Mehta, Priya Kapoor· International Journal of Mac...· 0 citations
Wireless Sensor Networks (WSNs) are extensively used in environmental monitoring, healthcare, industrial automation, military surveillance, and smart infrastructure. However, the limited battery life of sensor nodes restricts their long-term operation, particularly in remote areas where battery replacement is difficult and expensive. Energy harvesting offers a sustainable solution by converting ambient energy sources such as solar, thermal, vibration, wind, and radio frequency (RF) energy into electrical power for sensor nodes. This study reviews various energy harvesting techniques, their operating principles, advantages, limitations, and suitability for WSN applications. It also highlights the importance of integrating energy harvesters with energy storage devices and power management systems to ensure reliable operation. A framework involving energy source assessment, harvesting module selection, power conditioning, storage management, and adaptive duty-cycle control is presented to optimize energy utilization. Results indicate that solar energy provides the highest power output under favorable conditions, while vibration and RF harvesting are effective in indoor and industrial environments. Hybrid energy harvesting systems offer greater reliability by combining multiple energy sources. Overall, energy harvesting enhances network lifetime, reduces maintenance costs, and enables sustainable, self-powered WSNs, making it a key technology for future Internet of Things (IoT) and smart environment applications.
Rahul D. Mehta, Priya Kapoor· International Journal of Mod...· 0 citations