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Internet of Things (IoT) systems may be deployed to monitor spatiallydistributed quantities of interests (QoIs), such as noise or pollution levels.This paper considers a fog-based IoT network, in which active IoT devicestransmit measurements of the monitored QoIs to the local edge node (EN), whilethe ENs are connected to a cloud processor via limited-capacity fronthaullinks. While the conventional approach uses preambles as metadata for reservingcommunication resources, here we consider assigning preambles directly tomeasurement levels across all devices. The resulting Type-Based Multiple Access(TBMA) protocol enables the efficient remote detection of the QoIs, rather thanof the individual payloads. The performance of both edge and cloud-baseddetection or hypothesis testing is evaluated in terms of error exponents.Cloud-based hypothesis testing is shown theoretically and via numerical resultsto be advantageous when the inter-cell interference power and the fronthaulcapacity are sufficiently large.

Fog-Based Detection for Random-Access IoT Networks with Per-Measurement Preambles