Urban Risk Monitoring Enabled by IoT

OVERVIEW

Environmental risk factors can be managed with early detection of increased risks, such as weather conditions or water levels. IoT provides an ideal solution with the integration of real-time sensor data to decision making data visualisation. Examples include the monitoring of wind speed (and gusts) that increase the risk of limb failure if the tree is already at risk, monitoring of transport links impacted by flooded waterways, or road conditions that deteriorate when weather conditions cause black ice and snow.

PRODUCTS

IoT Water Quality Monitoring Station ATH Temperature and Relative Humidity Sensors AWS Multiparameter Weather Sensor Apogee Silicon-cell Pyranometers Apogee Infrared Radiometers

LOCATION

Monitoring of weather conditions for alpine road safety

Increased risks occur on winter roads such as black ice. For drivers heading into the Alpine resort areas of Australia, an early warning system using LoRaWAN was developed in conjunction with Meshed (network provider) and Transport for NSW.

Using infra-red radiometers to detect road surface temperatures, connected to the ICT International MFR-Node, the system was capable of providing continuous monitoring of road surface temperatures. With this key measurement combined with other weather parameters, the system provided a real time alert that can be connected to the Transport for NSW alert system.

This information enables road users to be aware of the potential for snow and ice to be on the road, as well as the the likelihood that drivers will need to fit snow chains.

Wind Alerts

Using an anemometer and microclimate weather station, a system was implemented to alert the school to an elevated risk of tree limb failure. Using wireless connections the data was supplied to both a dashboard for decision making and also to a database for students to access for study opportunities.

Wind Alarm

The Presbyterian Ladies College (Sydney) required a wind speed alarm to ensure students did not use outside spaces when the wind is strong enough to induce tree limb failure and potentially injure someone. To achieve this, a weather station was installed on the roof of one of the PLC buildings, continuously monitoring wind speed.

Software was written to receive the sensor data wirelessly, and automated processes were implemented to:

Accept alarms coming from the weather station and e-mail them to the appropriate person, and
regularly download data from the weather station, and other sensors.

Weather Station Data

The weather station continuously gathers data (wind speed, wind direction, temperature, humidity and barometric pressure). Weather station data can be accessed by school staff for use in teaching and science experiments.

Connected wirelessly across the school campus, e-mail alarms are generated when wind speed exceeds a threshold, alerting the health and safety team to increased wind speed conditions.

Flood Alerts

Urban areas are frequently at risk of flooding; hard, impermeable surfaces accelerate run-off, whilst low-lying causeways and bridges are susceptible to flooding. Armidale is separated by the Dumaresq Creek that flows through the town. The geography of the town was found to be ideal for such a solution to be implemented.

Why is it necessary to monitor waterway crossings?

During periods of heavy rainfall, the many low lying roads that cross the creek are flooded and therefore require closing to the public. Presently, there is no early alert for these required closures, which a LoRaWAN sensor network could provide. The geography of Armidale provides a valley bottom, surrounded by hills to the North and South that would be ideal locations to site a LoRaWAN gateway to cover the creek crossings.

Upon testing the range of the North and South LoRaWAN gateways, the range was up to 17 km on the northern gateway and 12 km on the southern gateway. This range would be more than adequate, with the potential of back-up redundancy, to cover the entire urban centre of Armidale.

The combination of nodes and water level sensors would provide real-time information to the council and local State Emergency Service to understand the risks to the urban region.

Conclusions

The examples demonstrate the role that appropriate sensors and decision making platforms, as well as IoT connectivity, enable informed decisions to be taken around risk minimisation. The use of a LoRaWAN network in an urban setting is ideal for the reduction of communication costs, requiring only the cost of the gateway and sensors, as well as the dashboard visualisation connection. ICT International can work with customers to develop a dashboard solution, or can integrate with an existing system.