Managing hazardous slopes using resilient IoT sensors and real-time processing (slopeRIoT)

Posted on

< Project Overview >

Landslides can pose a significant risk to life and economic disruption when close to road and rail infrastructure. High risk, high value sites can be monitored with labour inputs, or mitigated with expensive engineering costs, which can cost millions. Real-time and real-time IoT sensor architectures offer a promising way to widen monitoring of network scales and allow data informed decision making to keep cities linked and minimise risk to life.

The project aims to develop IoT integrated sensors with smart front and back processing using broadband satellite resilient connectivity, and incorporate data into existing business and decision making processes, as well as influence new processes. The project will fund an existing PDRA working on key sites threatened by landslides where already successful reengineered low cost seismometers and dSLR based deformation tracking are present.

The project will integrate resilient IoT into proven sensors to live and threshold-triggered live stream key data. Two test sites are being considered critical for their links to major population business centres and previously impacted by natural disasters. The project will also test dense rainfall networks using open-source IoT technologies that can be self-built by communities.


  • Install and test streaming sensors at selected sites in collaboration with stakeholders
  • Develop a MATLAB based portal to ingest streaming data into processing algorithms to derive critical slope health data
  • Use real and near real-time processed data to identify and characterise slope failures and precursors to failure
  • Hold stakeholder workshops to evaluate streaming sensors, processing portals and resultant changes to slope management with a focus on the cost benefits of interventions triggered by data.

Project lead:

Dr Stuart Dunning – Newcastle University