# Embracing the Future Internet of Things +
## 4 Realization of Hyper-Connected IoT realm
### Smart Mobility: IoT-Enhanced Autonomous Driving  
We built a Crowd Estimation and Mobility Analytics (CEMA) service which is beneficial for enhancing autonomous driving through IoT. Here, we focus on the _crowd size estimation_ given by the service based on the level of Wi-Fi activities in the surrounding of the deployed Wi-Fi sniffers. Wi-Fi sniffers are statically deployed on the road-side as well as in the autonomous vehicles by including GPS sensor for knowing the positions of the vehicles.  
This subsection describes the use of the proposed CEMA, federation, FogFlow, and SMG technologies in the smart mobility domain for IoT-enhanced autonomous driving. Integration of these technologies with other IoT and/or autonomous driving components makes the use of them possible in the pilot tests, which are conducted at the Technical University of Eindhoven campus in the Netherlands. The purpose is to estimate crowdedness in the university campus and leverage measurements coming from many vehicles to create a dynamic map of the crowd. By using the dynamic map, the autonomous vehicles can make routing decisions such as choosing a less crowded path or other decisions such as speed enforcement during certain events where many pedestrians exist.  
Figure 7 shows the integration of components for the aforementioned purpose. In the \"In-vehicle IoT\" side, we deploy a device including Wi-Fi and GPS sensors in the autonomous driving vehicle for sensing the Wi-Fi activity in its surrounding. The Wi-Fi sensor (constituted of a Wi-Fi module configured in monitor mode, sniffing Wi-Fi probe requests) collects raw data from smartphones of students at the campus (such as MAC address of the requester, relative received signal strength-RSSI, and timestamp) and the device at the vehicle anonymizes the data using hashing and salting mechanisms and pushes to the cloud side where \"Central IoT\" resides. Crowd estimation service (CEMA) [28] creates the analytics results and makes them available through a lightweight broker for the campus. Several pilot sites can be connected through the Federated IoT Platform. The data in the NGSI format is translated to Mca (reference point for M2M communication with application entities, see [13] for details) by SMG and pushed to the shared oneM2M-based interworking platform. The vehicle subscribes to the oneM2M platform through an IoT gateway physically placed in the vehicle. The gateway pushes the data to the robot operating system (ROS), which has subscriber components such as routing decision components. Moreover, ROS is used for the control of the vehicle itself, so that the decisions, such as speed reduction or alignments, are conducted through ROS. In this manner, the data collected by the autonomous vehicle is pushed back either to the vehicle itself or to other autonomous vehicles.  
Instead of performing data analytics on the central IoT, an alternative approach is to migrate whole detection, processing, and analytics tasks toward the front-end devices, only the data analytics results are published to central IoT. In this case, lightweight edge-based crowd mobility analytics modules can be performed in a single device attached inside automated public transport. Similarly, an edge device is integrated with a GPS sensor and an inertial measurement unit (IMU) sensor for performing stop detection, passenger estimation, and passenger flow tracking. On-board processing and analytical approaches are more privacy-preserving and consume less energy for data communications. Experiments are conducted in the automated hanging trains system at Technische Universitat Dortmund.