AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks

The ubiquitous deployment of various kinds of sensors in smart cities requires a new computing paradigm to support Internet of Things (IoT) services and applications, and big data analysis. Fog Computing, which extends Cloud Computing to the edge of network, fits this need. In this paper, we present a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to build large-scale, geospatial sensing networks, perform big data analysis, identify anomalous and hazardous events, and offer optimal responses in real-time. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.