The Smart Cities Market across the world is one of the fastest-growing sectors and is predominately based on the Internet of Things (IoT) sensors and advanced analytics, of which our technology is based.
The sector has been valued globally in 2019 between USD $545 Billion and USD $624 Billion and expected to grow to between USD $790 Billion to USD $1.2 Trillion by 2027.
Whichever the end value, the agreed Compound Annual Growth Rate (CAGR) for this sector averages at over 19%.
The main drivers for this market are the increases in population and urbanisation, along with the demands for sustainable infrastructure to support them.
There is a constant need to develop and evolve resilient cities based on balancing the needs to reduce energy consumption, building a sharing economy, and maintaining sustainability for the community and environment.
The traditional methods of urban design and development can no longer support these demands.
Creating a plan that only has 5-year cursory reviews contained within it to achieve a 20-30 year vision does not allow for the necessary micro-adjustments necessary to minimise the delta of change between the current state and the long term vision, whilst reducing the impact on the communities.
Making the micro-adjustments and changes to the urban design from its inception and along the journey can only be managed through the use of accurate, timely, and validated data that can then be processed and analysed through technologies like Artificial Intelligence (AI) and Machine Learning (ML).
These technologies deliver trend and prediction outcomes based on empirical data that allow local governments to make small adjustments and track the results to ensure they remain on target to provide the vision and meet community expectations.
The sensors available on the Hayden Data enclosures can provide data and information from the urban environment that can then be aggregated with other data sets to allow urban planners and authorities to understand the performance of their plans within the community.
Leveraging elements of Machine Learning at our sensor level and combining publicly available data with information held by local authorities and other 3rd party providers will allow us to create algorithms to ingest into our Artificial Intelligence engines to deliver reporting, trending, and predictive insight for Local Governments. This will allow them to make micro-adjustments, monitor the outcomes, adjust as and where required, and review new trends and predictions to ensure compliance with their strategic goals.
The table below provides insight into the capabilities of the sensors and how they can be used to monitor and manage a Regional and a City urban landscape. All are using the same sensors and data with the output of information tailored to suit the environment and customers’ requirements.
|Sensor||Regional Urban Environment||City Urban Environment|
|Accelerometer – X, Y, Z||Streetlight, Power Pole, any fixed structureSeismic activity||Streetlight, Power Pole, any fixed structureSeismic activity|
|Rotation – XZ, YZ||Streetlight, Power Pole, any fixed structure||Streetlight, Power Pole, any fixed structure|
|Smoke – in ppm||Bushfire alertingBushfire monitoringHealth risk detection||Industrial fire alertpollution monitoringHealth risk detectionTraffic build-up|
|Gas – in ppm||Chemical composition of smoke Remote industrial monitoringMethane levels||Industrial incident monitoring Gas leak detectionHealth risk monitoring|
|Ambient Temperature||Granular weather monitoringBushfire riskBushfire monitoringTourism opportunitiesHealth risk monitoring||Granular weather monitoringHealth risk monitoringUrban heat sinksPerformance of green belts and urban forests|
|Pressure||Granular weather monitoringWeather prediction||Granular weather monitoringWeather prediction|
|Humidity||Granular weather monitoringParks and garden water management when combined with Pressure and BoM dataMonitoring risk profile during Bushfire season||Granular weather monitoringParks and garden water management when combined with Pressure and BoM dataAgriculture management|
|UVA & UVB||Health risk detectionChildren’s playgroundsSporting facilities||Health risk detectionChildren’s playgroundsSporting facilities|
|Digital Camera||Visual validation of other readingsIncident reporting if triggered through other sensorsCounting sensorWildlife monitoringWalking track managementBushwalker safety||Visual validation of other readingsIncident reporting if triggered through other sensorsCounting sensorTraffic monitoringCrowd movementLocalised heat (using IR)|
|Wind Speed & Direction||Granular weather monitoringWeather analyticsWhen combined with other sensors for Bushfire alerting and management – predictionHealth risk detection when combined with other sensorsHistorical trend data||Granular weather monitoringWeather analyticsHealth risk detection when combined with other sensorsHistorical trend data|
|Rain gauge||Granular weather monitoringWeather AnalyticsLocation rain for risk analytics for bushfire seasonAgriculture trend analytics||Granular weather monitoringWeather AnalyticsWater collection analytics for urban runoffAgriculture trend analytics|