Slow-moving landslides are a widespread hazard in coastal and mountainous settings, causing damage to property and infrastructure, and sometimes loss of life. Mechanisms driving rare catastrophic failure events are poorly understood, highlighting the need for effective monitoring systems. Traditional landslide monitoring techniques include remote sensing (e.g. InSAR), geotechnical instrumentation (e.g. piezometers), and geophysical monitoring (e.g. electrical resistivity). Although numerous and varied, traditional methods cannot always provide the high spatiotemporal resolutions required for real-time monitoring. Remote sensing techniques can be spatially and temporally coarse, and ground-based instrumentation is costly and susceptible to damage during ground failure. We have established a novel IoT-based wireless sensor network (WSN) for slow-moving landslide monitoring which has been operational for 4 years. It consists of motion-triggered, low-power, low-cost inertial measurement unit (IMU) sensors that are embedded in artificial boulders (SlideCubes) and distributed across the landslide body. The sensors communicate via LoRaWAN (Long Range Wide Area Network) with a gateway, and data are uploaded to a server in near real-time. This research focuses on the western portion of the Black Ven-Spittles landslide complex at Lyme Regis, Dorset where a small earthflow propagates from a disused landfill site. The site is a suitable ‘field laboratory’ in which to test the WSN and SlideCubes; the earthflow is self-contained and somewhat isolated from the surrounding complex, reaching comparatively high velocities (c. 52.62 m y-1) and retrogressing westward towards the town allotments, car park and other infrastructure. Our SlideCubes are deployed on the landslide surface and ‘go with the flow’ during gradual failure. Two brands of IMU sensor are deployed across the earthflow, allowing comparison between similar sensors and evaluation of their suitability for monitoring landslides. The sensors precisely capture motion onset which is transmitted in near real-time. From this, we examine spatial patterns of SlideCube motion and extract relative trigger magnitudes, producing a holistic picture of earthflow failure events as well as a preliminary assessment of potential catastrophic collapse. The IoT network also comprises an onsite rain gauge, with potential for integration of additional sensors, that supplies information about possible drivers of this motion. We draw on third-party meteorological and wave data to further support our process understanding. We categorise types of motion recorded by the IMU sensors and validate this with trail camera imagery, providing insight into the geomorphological processes occurring on the landslide surface and subsurface. Our WSN is a successful test case of low-cost landslide monitoring which has potential for development into a continuously operational early warning system.