Scholarlink

<p>The increasing volume and heterogeneity of real-time data generated by Internet of Things (IoT) devices in intelligent environments poses significant challenges for efficient data processing. Traditional centralized Complex Event Processing (CEP) deployments can become bottlenecks when geographically distributed sensors continuously generate heterogeneous events. To address this issue, this paper proposes an edge-fog-cloud Distributed Complex Event Processing (DCEP) system for scalable event detection in intelligent environments. The proposed system assigns raw event generation to the edge layer, local event abstraction to fog-layer CEP engines, and higher-level alert composition to a cloud-layer CEP engine. The implementation combines Apache Kafka, Esper, lightweight ARM devices, and a server, and is evaluated through functional tests, integration tests, a distributed stress test, and a controlled comparison between centralized and distributed DCEP deployments. The stress test shows that the distributed deployment reaches 68,027 processed events/s before saturation in the evaluated setup. The controlled comparison executes the same event patterns as a single-node DCEP deployment on an Orange Pi and as a distributed DCEP deployment across Raspberry Pi, Orange Pi, and server nodes. Across all tested rates, both deployments produce the same number of high-level alerts, while the distributed deployment achieves lower end-to-end latency and higher high-level-alert output rate. These findings show that the proposed system is beneficial when event patterns can be decomposed by domain and placed across heterogeneous nodes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A distributed complex event processing system for scalable efficient event detection in intelligent environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Jesús Rosa-Bilbao,
  • Alejandro Guitarte-Fernández,
  • Juan Boubeta-Puig

摘要

The increasing volume and heterogeneity of real-time data generated by Internet of Things (IoT) devices in intelligent environments poses significant challenges for efficient data processing. Traditional centralized Complex Event Processing (CEP) deployments can become bottlenecks when geographically distributed sensors continuously generate heterogeneous events. To address this issue, this paper proposes an edge-fog-cloud Distributed Complex Event Processing (DCEP) system for scalable event detection in intelligent environments. The proposed system assigns raw event generation to the edge layer, local event abstraction to fog-layer CEP engines, and higher-level alert composition to a cloud-layer CEP engine. The implementation combines Apache Kafka, Esper, lightweight ARM devices, and a server, and is evaluated through functional tests, integration tests, a distributed stress test, and a controlled comparison between centralized and distributed DCEP deployments. The stress test shows that the distributed deployment reaches 68,027 processed events/s before saturation in the evaluated setup. The controlled comparison executes the same event patterns as a single-node DCEP deployment on an Orange Pi and as a distributed DCEP deployment across Raspberry Pi, Orange Pi, and server nodes. Across all tested rates, both deployments produce the same number of high-level alerts, while the distributed deployment achieves lower end-to-end latency and higher high-level-alert output rate. These findings show that the proposed system is beneficial when event patterns can be decomposed by domain and placed across heterogeneous nodes.