Durable Material: Rust-proof metal or engineering plastics for outdoor durability. Auto-Sensing: Vehicle detection sensors enable automatic lifting/lowering. Anti-Theft: Tamper-proof alarms, reinforced locks, and optional GPS tracking. Power Options: Solar, rechargeable batteries, or wired power supply.
is a signal quality tester based on the standard LoRaWAN protocol, equipped with GPS positioning capabilities. It is primarily used to test the LoRa network signal strength at construction sites. It can test the packet loss rate for both uplink and downlink, as well as the Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR), thereby facilitating the analysis and assessment of the LoRa network signal coverage at construction sites
Up to 5Km communication range in an open environment; LCD Screen indicates reading, power percentage, allowance, valve, and signal status. Long-lasting battery life of up to 3 years; Capable of saving over 10,000+ historical operation records locally; Compatible with standard LoRaWAN® gateways and third-party network server platforms; Fast connection with HKT LoRaWAN® gateway and cloud platform without additional configuration.
Ø Supports LoRaWAN protocol; Ø Supports tamper-proof alarms and low-voltage detection; ØBuilt-in 4000mAh battery; ØMeasurement Range: 0~65535LUX; ØAccuracy: ±7%.
Our LoRaWAN parking sensor uses dual detection (geomagnetic + 24GHz radar) with 99% accuracy. It checks space occupancy periodically and sends data to nearby LoRaWAN gateways, which forward it to the cloud. Facility managers can then view real-time space status from anywhere — no need to send staff for regular patrols. Used in underground garages, commercial complexes, and high-end residential projects.
Our LoRaWAN parking sensor features an NRC 0.88 (ISO354‑certified) sound absorption rating, which effectively reduces vehicle noise in underground garages and improves the acoustic comfort of enclosed parking areas.
Our parking space sensors help improve the efficiency of parking management through collcting and transforming the data of parking space.
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