Abstract-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 Internetof-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.
This is a no brainer. Using bicycles to commute is the most sustainable form of transport, is the least expensive to use and are pollution-free. Towns and cities have to be made bicycle-friendly to encourage their wide usage. Therefore, cycling paths should be more convenient, comfortable, and safe to ride. This paper investigates a smartphone application, which passively monitors the road conditions during cyclists ride. To overcome the problems of monitoring roads, we present novel algorithms that sense the rough cycling paths and locate road bumps. Each event is detected in real time to improve the user friendliness of the application. Cyclists may keep their smartphones at any random orientation and placement. Moreover, different smartphones sense the same incident dissimilarly and hence report discrepant sensor values. We further address the aforementioned difficulties that limit such crowd-sourcing application. We evaluate our sensing application on cycling paths in Singapore, and show that it can successfully detect such bad road conditions.
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