A Novel Real-Time Traffic Sensing Model to Improve the Performance of Web-Based Industrial Ecosystems

Abstract

The novel real-time traffic sensing (RTS) model proposed in this paper not only senses traffic patterns but also chaotic traffic conditions, known as the fractal breakdowns, on the fly. If a web-based industrial ecosystem has included RTS as a functional component, it would possess the ability to acquire ambient intelligence of, and act upon, changes in traffic patterns. Its use of the results by the RTS as parameters for self-organization proactively could prevent sudden system failures. Web-based industrial ecosystems consist of distributed processing centers/entities/species. These species have distinctive functional characteristics and collaborate by message passing over the mobile Internet, which supports wireline and wireless communications in a mixed dynamic manner. The unpredictable traffic changes in such an environment could reduce system performance and lead to system instability and even failure. Although brief stints of chaotic operations or system failures followed by quick recoveries may be unnoticeable to human eyes, they can impede the normal operations of industrial systems and inflict huge financial losses. Any industrial ecosystem with RTS support would benefit from the enhanced reliability by detecting possible chaotic operations or fractal breakdowns.