A BREAKPOINT MODEL FOR DATA VOLUME AND NETWORK CAPACITY IN INTERNET OF THINGS
DOI:
https://doi.org/10.37943/HLSA6233Keywords:
Massive IoT, 5G measurements, 6G, IMT-2030, Big Data analytics, breakpoint analysis, mMTC, vertical farming, digital twin, Lambda architectureAbstract
The migration from fifth generation (5G) to sixth generation (6G) mobile networks is driven by the Massive Internet of Things (IoT), which targets device densities of up to ten million units per square kilometer. Two scaling limits govern such systems: a data-volume limit, beyond which batch analytics must give way to distributed streaming, and a network-capacity limit, beyond which the radio access network can no longer carry the aggregate uplink. These limits have so far been treated independently, and no empirical anchor exists for Central Asia. This study makes three contributions. First, it derives a closed-form, dimensionless separation ratio that couples the two breakpoints through shared system parameters and determines which limit binds first. Second, the model is instantiated using 145 regulated 5G New Radio measurements from the commercial Tele2 network in Astana, Kazakhstan, across five load scenarios spanning 13 to 100,000 emulated devices. Third, the closed-form structure of the ratio suggests the splitting is a general trait of uplink-bound IoT domains, though this study empirically instantiates only the vertical-farming case. The instantiated model yields a separation ratio of about thirty-two, with breakpoints at twenty-eight nodes for network capacity and eight hundred eighty for data volume. The measured round-trip time of 124.8 milliseconds and the measured uplink of 0.19 megabits per second miss the International Mobile Telecommunications-2030 (IMT-2030) targets by factors of 1248 and about 5.3 × 10⁵, respectively. Because the network-capacity limit binds first, the analytics regime required for dense deployment is unreachable on 5G. For the measured single-operator deployment, 6G is a precondition rather than an enhancement; generalization to other operators and sites requires further measurement.
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