Sensor Fusion for Public Space Utilization Monitoring in a Smart City

By Billy Pik Lik Lau, Nipun Wijerathne, Benny Kai Kiat Ng, Chau Yuen. 2017

97 citations recorded by the source corpus.

Public space utilization is crucial for urban developers to understand how efficient a place is being occupied in order to improve existing or future infrastructures. In a smart cities approach, implementing public space monitoring with Internet-of-Things (IoT) sensors appear to be a viable solution. However, choice of sensors often is a challenging problem and often linked with scalability, coverage, energy consumption, accuracy, and privacy. To get the most from low cost sensor with aforementioned design in mind, we proposed data processing modules for capturing public space utilization with renewable wireless sensor network (RWSN) platform using pyroelectric infrared (PIR) and analog sound sensor. We first proposed a calibration process to remove false alarm of PIR sensor due to the impact of weather and environment. We then demonstrate how the sounds sensor can be processed to provide various insight of a public space. Lastly, we fused both sensors and study a particular public space utilization based on one month data to unveil its usage.

Subfields: Environmental Engineering, Transportation, Water Science and Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

1. Chau Yuen
2. Billy Pik Lik Lau
3. H. Vincent Poor
4. U-Xuan Tan

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “In [81], [95], [119], a bottom up urban planning method is implemented, where sensors are installed in a designated region to capture space utilization.”
• Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “Sensors are becoming increasingly prevalent and abundant within an urban setting, this is due to the reduced production cost of high-quality sensors and the wide spread embedded nature of sensors within smart devices [25].”
• Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach (2019, background): “Since smartphones have various types of in-built sensors, there is potential to collect more data easily through smartphone-based applications, when compared to traditional data collection methods such as custom built sensor nodes [3], [4], surveys, and questionnaires.”
• Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence (2018, methodology): “We adopt a similar unsupervised learning process in our previous work [27] in an outdoor smart city environment.”