# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Challenges in ITS Sensing  
#### 4.3.1 Challenge 1: Heterogeneity  
Developing advanced ITS applications requires the adoption of different sensors and sensing methods. On a large scale, heterogeneity resides in many aspects, e.g., hardware, software, power supply, and data. Sensor hardware has a large variety of different ITS tasks. Magnetic sensor, radar sensor, infrared sensor, LiDAR, camera, etc., are common sensor types that each poses unique advantage in certain scenarios. These sensors are different regarding cost, size, material, reliability, working environment, sensing capability,and so on. Not only is there a large variety of sensors themselves, the hardware supporting the sensing functions for storage and protection is also diverse. The associated hardware may limit the applicability of sensors, as well. A sensor with local storage is able to store data onsite for later use; a sensor with a waterproof shell is able to work outdoors, while those without may solely be available for indoor monitoring. Even within the same type of sensors, there can be significant variance with respect to detailed configurations and will influence the effectiveness and applicability of the sensors. Cameras with different resolution is an example, and those with high resolution are suitable for some tasks that are not possible for low-resolution cameras, such as small object detection; the low-resolution cameras may support less complicated tasks and have a better efficiency and lower cost. The installation locations of the same sensors also vary. As aforementioned, sensors onboard a vehicle or carried by a pedestrian have different functions from those installed on infrastructures. Some sensors can only be installed on the infrastructures, and some are appropriate for onboard sensing. For example, loop detectors and magnetic nodes are most often on or underneath the road surface, while sensors for collision avoidance need to be onboard cars, buses, or trucks. Software is another aspect that poses heterogeneity in ITS sensing. There is open-source software and proprietary software. Open-source software is free and flexible and can be customized for specific tasks; however, there is a relatively high risk that some open-source software is unreliable and may solely work in specific settings. There are many open-source codes on platforms such as GitHub. A good open-source tool can generate massive influence on the research community, such as open codes for Mask R-CNN [237], which has been widely applied for traffic object instance segmentation. Proprietary software is generally more reliable, and some software comes with customer services from the company who develop the software. These software tools are usually not free and have less flexibility in being customized. It is also hard to know the internal sensing algorithms or design. When an ITS system is composed of multiple software tools, which is likely the case most of the time, and these tools lack transparency or flexibility regarding communication, there will be hurdles in developing efficient and advanced ITS applications. Heterogeneous settings in ITS sensing inevitably collect a heterogeneous mix of data, such as vehicle dynamics, traffic flow, driver behavior, safety measurements, and environmental features. There are uncertainties, noises, and missing patches in ITS data. Modern ITS applications would require data to be of high quality, integrated, and sometimes in real-time. Despite improving sensing functionality for individual sensors at a single location, new challenges arise in the integration of heterogeneous data. New technologies also pose challenges in data collection as some data under traditional settings will be redundant, and at the same time, new data will be required for some tasks, e.g., CAV safety and mixed autonomy. Edge computing is promising in terms of improving data integration of different data sources. For example, [PERSON] et al.