As a commonplace feature of our urban environment, lampposts are reliable power sources that provide lighting in our city. The LaaP project uses lampposts as a key sensor infrastructure to deploy different kinds of intelligent sensors and network connectivity technologies for a smart city.
LaaP offers a common infrastructure and services for the various Government agencies to collect, share and analyse real-time sensor and video data. Sensor data from the LaaP platform can provide situational awareness to augment the operational and planning capabilities of the agencies.
LaaP is part of the Smart Nation Sensor Platform (SNSP), a whole-of-government (WOG) technology platform that transforms sensor data into a 360° situational awareness of smart city spaces in Singapore. LaaP is a component of the Sensor Data layer of the SNSP Framework (SNSP’s tech stack) and functions as a smart infrastructure, creating smarter city spaces across Singapore with real-time sensor data.
What is LaaP?
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 agencies translate sensor data, both within their domain of expertise and across multiple domains, by providing them with greater situational awareness and actionable insights for operations and planning.
Key LaaP features are:
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 Internet of Things (IoT) protocols: LaaP supports protocols such as DDS and MQTT.
End-to-end security: LaaP uses secure end-to-end encryption to prevent data from being read or modified, other than by the true sender(s) and recipient(s).
Why Should LaaP be Adopted?
LaaP can be used as a common sensor infrastructure to provide better situational awareness about the physical environment in which each lamppost is located.Localised Environment Monitoring
LaaP uses environmental sensors to collect localised environmental data such as temperature, humidity, the concentration of different gases, air quality, and rainfall.
These localised environmental data enable agencies to design better living and workspaces around environmental factors and promote public health and environmental sustainability.
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 -40°C to 65°C
Operating humidity of 0% to 99%RH
|Accuracy of +/- 0.5°C|
|Humidity||Operating temperature of -40°C to 65°C
Operating humidity of 0% to 99%RH
|Accuracy of +/- 3%|
|Gas||Operating temperature of -40°C to 50°C||
|Air Quality, e.g. PM2.5||Operating temperature of -10°C to 55°C
Operating humidity of 10% to 98% RH range between 0 – 1000 micro gram / m3
|Rain Sensor||Operating temperature of -40°C to 65°C
Operating humidity of 0% to 99%RH
|100% detection rate|
Cameras fitted onto smart lampposts are integrated with video analytic capabilities.
LaaP has a video analytics feature to understand different aspects of crowd behaviour. This includes tabulating:
The size of waiting crowds at pedestrian crossings and bus stops.
The number and direction of crossings.
The waiting time between crossings.
Crowd data help agencies with crowd threshold analysis and offer insights into footfall patterns for the mapping of transportation infrastructure. Agencies can use these data to inform public infrastructure design, improve the urban living experience, and provide more efficient transportation services.
Furthermore, operators can also set occupancy thresholds to trigger automated alerts when thresholds are breached.
Accuracy: At least 75% in footfall count and occupancy, and at least 75% accuracy rate in crossing count and wait time calculation.Classification and Speed Detection of Personal Mobility Devices (PMD) and Bicycles
LaaP also has a video analytics feature to perform PMD and bicycle traffic analysis. LaaP can thus perform the following functions:
Classify PMDs and bicycles.
Detect their speed along footpaths, shared paths and cycling paths.
Detect any PMDs travelling on roads.
This data help agencies plan and manage safer footpaths and roads for pedestrians, riders, cyclists, and motorists alike.
Accuracy: 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 uses environmental and geolocation sensors to guide and enhance situational awareness of autonomous vehicles (AVs) and manned vehicles for safer roads and smoother commutes.
On-board units (OBU) in vehicles can receive data from these sensors within 1.3 seconds, without any data loss.Verifying Global Navigation Satellite System (GNSS) Signal Interference and Integrity
LaaP helps ensure that geospatial information provided from satellites to vehicles is reliable and accurate. By collecting GNSS information from sensors mounted on lampposts or roadside infrastructure, LaaP can verify any interference to GNSS signals and maintain their integrity.
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. Transmissions of the four types of GNSS signals from the GNSS receiver to the back-end cloud application are published within two seconds.
It also simulates and publishes the spoofed or jammed signals within two minutes from the start of the simulation to the back-end cloud application. It will continuously publish the data until the end of the simulation.
How Do You Use LaaP?
The full implementation and onboarding process is currently being finalised and will be updated here in time. For agencies that are interested to onboard LaaP, you can email the team at firstname.lastname@example.org for assistance with the onboarding process.
Under Tranche 1 of the LaaP project, 50 smart lampposts were installed in one-north and Geylang to test the technical feasibility of using lampposts as a common infrastructure to create situational awareness for different smart city use cases – including crowd analytics, localised environmental monitoring, information sharing with vehicles, and PMD and bicycle traffic analytics.
The LaaP team is exploring new use cases and features for lampposts as part of a smart city’s infrastructure.
LaaP is currently in the trial and initial deployment stage. Agencies who wish to work with us or find out more about the sensors deployed and data available may contact email@example.com.
Meet the Team!
Kwan Wei Pin, Executive Manager, SNPS
Wei Pin is an Executive Manager at the Smart National Platform Solution Office; he leads the LaaP programme. Prior to working in GovTech, Wei Pin worked in Defence Science and Technology Agency (DSTA), where he led the development, acquisition, and integration of training and simulation programmes of varying scale and complexity for the Singapore Armed Forces for 12 years. Wei Pin hopes to improve citizens’ lives and help government agencies to accelerate their digitalisation journeys.
Nelson On, Senior Manager, SNPS
Nelson is an experienced professional with a demonstrated history of working in the information technology domain of Smart Cities Technologies, IoT, ICT Infrastructure, Governance & Security, Data Centres, and Large-Scale System Integration.
Toh Boon Hion, Senior Consultant, SNPS
Boon Hion is a Senior Consultant at GovTech; he is involved in field infrastructure design for the LaaP programme. Before joining GovTech in 2016, Boon Hion held various technical and operational roles in multiple MNCs, local tech start-ups, and telco. He enjoys reading during his free time.
Chew Wei Che, Manager, SNPS
Wei Che graduated from the Nanyang Technological University (NTU) with a degree in Mechanical Engineering. Wei Che is interested in technology that benefits people with disabilities. Before joining GovTech, Wei Che worked on various simulation and large-scale system integration projects for the defence sector.
Goh Kwong Huang, Lead Data Engineer, DSAID
Kwong Huang has a strong industry background in engineering, science, and technology. With a PhD in Electronics & Electrical Engineering specialised in the video technology domain, he has over 25 years of experience in video processing algorithms research & development, video coding & streaming platform solutioning, scalable multimedia platform deployment, video analytics system development, and cloud video exchange architecting. Kwong Huang is excited to help Government agencies leverage advanced technology in Smart Nation building.
Liew Hua Peng, Principal System Engineer, SIoT
Hua Peng is a team lead and systems architect at GovTech, Sensors & IoT. He has over 30 years of experience in electronic engineering, embedded systems, and cloud computing. Prior to working in GovTech, Hua Peng had developed industrial embedded products such as GPS and PDA. He also worked on industrial 4.0 systems. Together with his team in GovTech, Hua Peng designed and built the Smart Office Edge/Cloud software. He is involved in the development of the nation-wide TraceTogether Token project, which achieved best-in-class CSA level 4 security level. Hua Peng is passionate about improving people’s lives through innovation and technology.
Kelvin Wong, Senior Cybersecurity Specialist, CSG
Kelvin is a Senior Cybersecurity Specialist from Cybersecurity Group; he leads the Smart Nation Sensor Platform programme for Cybersecurity. His focus is on Internet-of-Things, Cloud Security, practising security governance, risk assessment, and security by design. He hopes to uplift the security posture of devices for a more secured IoT/OT landscape.
Augustine Tan, Cybersecurity Specialist, CSG
Augustine is a Cybersecurity Specialist from the Cybersecurity Group. His main focus is to support secure development, design, and implementation of Smart Nation Sensor Platform systems at GovTech. Augustine hopes to be able to contribute to GovTech’s efforts in creating a safe and secure Smart Nation.
Thia Kok Soon, Senior Manager, SNPS
Kok Soon graduated from the National University of Singapore (NUS) with a Master of Engineering. Having worked for more than 12 years in a telco environment, he has vast experience in network planning and operations. Kok Soon now works in the Operations Engineering team under the Smart Nation Platform Solutions Group. Aside from his work commitments, Kok Soon enjoys tinkering with hardware. His latest toy is a DIY 3D printed CNC machine that doubles as a laser engraver.
Last updated 24 February 2022
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