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Lamppost-as-a-Platform (LaaP)

What is LaaP?

The Lamppost-as-a-Platform (LaaP) project trials the effectiveness of lampposts as a key sensor infrastructure. If proven successful, the platform would augment the operational and planning capabilities of government agencies, including yours.

As a commonplace feature of our urban environment, lampposts are reliable power sources that provide comprehensive coverage of the city. LaaP offers a common infrastructure and services for the various government agencies to collect, share and analyse real-time sensors and video data. 

LaaP uses cloud-based infrastructure, wired and wireless technologies (e.g. low-bandwidth, low-powered wide area network connectivity). It is also fitted with intelligent sensors - including video, environmental and geolocation sensors.

These capabilities help your government agency to translate sensor data – both within your domain of expertise and across multiple domains – providing you with greater situational awareness and actionable insights for operations and planning.

What are the features available?

  • Centralised device management: LaaP provides a centralised device management platform to monitor and manage the health status and configuration of devices at deployment sites.

  • Video/sensor data acquisition: LaaP supports various messaging protocols and connectivity to securely acquire sensor and video data from deployment sites.

  • Video/sensor data analytics: Sensor nodes at the edge fuse multiple sources of sensor data, with support for both front-end edge analytics and back-end video analytics.

  • Support for different IoT protocols: LaaP supports protocols such as DDS and MQTT.

  • End-to-end security

What are some helpful use cases of LaaP?

  • Classification and Speed Detection of Personal Mobility Devices (PMD) and Bicycles

Our cameras are integrated with video analytic capabilities and fitted onto smart lampposts to:

    • Classify PMDs and bicycles

    • Detect their speed along footpaths, shared paths and cycling paths

    • Detect if there are any PMDs travelling on roads.

Accuracy rates: At least 75% in the classification of PMDs and bicycles, and at least 80% in speed measurements.

  • Information Sharing with Vehicles (V2I – Vehicle to Infrastructure)

LaaP tests the use of environmental and geolocation sensors to enhance situational awareness of AVs and manned vehicles. On-board units (OBU) in vehicles will receive data from these sensors within 1.3 seconds, without any data loss.

  • Crowd Analytics

LaaP has a video analytics feature to understand crowd behaviour - counting the size of waiting crowds at pedestrian crossings and bus stops, the number and direction of crossings, and the waiting time between crossings.

Government agencies can use these data to inform the design of public infrastructure, improve the urban living experience and provide more efficient transportation services.

Operators can also set occupancy thresholds to trigger automated alerts when thresholds are breached.

Accuracy rates: At least 75% in footfall count and occupancy, and at least 75% accuracy rate in crossing count and wait time calculation. 

  • Verifying Global Navigation Satellite System (GNSS) signal interference and integrity

LaaP can verify any interference to GNSS signals and maintain their integrity by collecting GNSS information from sensors mounted on lampposts or roadside infrastructure. The four types of GNSS signals commonly used in detection and for collection are: GPS L1, GLONASS L1, Galileo E1 and BeiDou B1

Success rates: At least 95% of the time. Transmission of the 4 types of GNSS signals - from the GNSS receiver to the backend cloud application, and getting published within 2 seconds. 

It also simulates and publishes the spoofed and/or jammed signals within 2 minutes from the start of the simulation to the backend cloud application. It will continuously publish the data until the end of the simulation. 

  • Localised Environment Monitoring

LaaP uses environmental sensors to collect localised environmental data for agencies’ use in urban planning. These localised data enable agencies to design better living and workspaces around environmental factors, e.g. direction, the flow of wind, the direction of sun’s rays, rainfall and air quality.

The operating conditions, accuracy rates and success criteria of the respective sensor types are represented in the following table:

Sensor Type

Operating Conditions

Success Criteria

Temperature

Operating temperature of -40oC to 65oC 

Operating humidity of 0% to 99%RH

Accuracy of +/- 0.5oC

Humidity

Operating temperature of -40oC to 65oC 

Operating humidity of 0% to 99%RH

Accuracy of 3%

Gas

Operating temperature of -40oC to 50oC

  • Typical detection range 1~30ppm of H2 with a sensitivity of 0.3 to 0.6 (change ratio of sensor resistance - 10k ohm to 90k ohm in air)

  • Typical detection range 30~1000ppm of carbon monoxide in the air with a sensitivity of 0.13 to 0.31 (change ratio of sensor resistance – 13.3k ohm to 133k ohm in 100ppm of carbon monoxide)

  • For other gas sensors, the accuracy must be equivalent to or better than the above.

Air Quality, e.g. PM2.5

Operating temperature of -10oC to 55oC 

Operating humidity of 10% to 98% RH range between 0 – 1000 micro gram / m3

  • Accuracy of +/- 10 micro gram / m3 when <300 micro gram / m3

  • Accuracy of 10% of measured value when beyond 100 micro gram / m3

Rain Sensor

Operating temperature of -40oC to 65oC 

Operating humidity of 0% to 99%RH

100% detection rate

What are some upcoming plans for LaaP?

LaaP will next be deployed in Punggol.

How can I get started?

LaaP is currently in the Proof-of-Concept (POC) stage. If you wish to work with us or find out more about the sensors deployed and data available, contact sensors@tech.gov.sg.

 

Last updated 1 July 2020.