Atharv Gyan

Edge computing is a decentralized computing model that processes data closer to the source where it is generated (the "edge" of the network), rather than sending it to centralized data centers or cloud servers. This model is particularly beneficial in scenarios involving the Internet of Things (IoT) , where real-time processing, low latency, and bandwidth efficiency are critical. Key Features of Edge Computing Proximity to Data Source : Edge devices, such as IoT sensors or gateways, process and analyze data locally rather than sending it to the cloud.  Reduced Latency : By processing data near its source, edge computing minimizes delays, which is essential for time-sensitive applications like autonomous vehicles or industrial automation. Bandwidth Optimization : Only critical data is sent to the cloud for further analysis or storage, reducing the load on networks. Enhanced Security and Privacy : Since data is processed locally, it reduces the risk of interception during trans...

Introduction to Edge Computing and IoT Edge computing refers to the practice of processing data near the edge of the network, where the data is generated, rather than relying solely on centralized data-processing warehouses or cloud-based systems. This approach contrasts with traditional cloud computing, where data is transmitted to centralized data centers for processing and analysis. The Internet of Things (IoT) consists of a network of interconnected devices, sensors, and systems that communicate and exchange data to perform various tasks and provide valuable insights. IoT devices are deployed across various sectors, including smart homes, industrial automation, healthcare, transportation, and more. Impact of Edge Computing on IoT Improved Response Times One of the primary benefits of edge computing in IoT is the significant improvement in response times. By processing data closer to the source, edge computing reduces latency, which is the time it takes for data to travel from the...

Java Multithreading: Concurrency and Parallelism are essential concepts in modern software development, particularly in Java, a language known for its robust support for concurrent programming. In this article, we'll explore the fundamentals of multithreading in Java, focusing on concurrency and parallelism. 1. Concurrency vs. Parallelism: - Concurrency: Concurrency is the ability of a program to execute multiple tasks simultaneously, seemingly overlapping in time. In Java, concurrency is achieved through threads. Threads allow different parts of a program to execute independently. - Parallelism: Parallelism, on the other hand, involves executing multiple tasks simultaneously, utilizing multiple CPU cores to speed up the computation. While concurrency deals with managing tasks, parallelism focuses on executing tasks concurrently on multiple processors. 2. Threads in Java: - In Java, threads are represented by instances of the `Thread` class or by implementing the ...