# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.2 Transportation & Smart Cities  
One significant IIOT application scenario is smart cities, which attempt to improve public services in urban areas and address urbanization issues including traffic congestion, environmental pollution, and energy consumption. The major problem in smart cities is divided into two parts. The first is how to successfully gather and capture huge volumes of dynamic data in ubiquitous settings. Another issue is the approach to evaluate this multisource diverse spatio-temporal data and then accurately create data profile for certain activities. As an effective and critical IoT technique, crowdsourcing focuses on connecting a wide range of mobile devices equipped with various sensing, actuation, and computational abilities to the wireless network, thereby supporting decentralized pervasive applications and services in a smart city environment [100]. [PERSON] [101] proposed a smart city shared bus profiling framework using diverse mobile crowdsourced data. First, the authors used a shared bus application (APP) for mobile phones called Futureflleet to collect traveler's order data as well as driver's GPS data. Second, once the shared bus data has been obtained, the critical challenge is determining an approach to study and comprehend the requirements for travel of residents from different perspectives and subsequently creating precise travelers profiles. To address the challenge, the authors presented a travel profiling (TP) to set out resident travel, which they further refined into loss tolerance, delay tolerance, seat utilization rate (SUR), waiting time and journey time. Lastly, a multi-constraint evolution algorithm (MCEA) was designed to improve the routes based on the TP. The experimental results showed that TRProfiling performed excellently in terms of meeting traveler's requirements. [PERSON] [102] designed a mobile system called ParkNet that used designated vehicles to obtain information about parking space occupancy. Each vehicle used for the ParkNet system was provisioned with a GPS receiver and an ultrasonic range finder attached to the passenger's side to ascertain parking space occupancy. A central server was used to aggregate data and created a real-time map of available parking spaces, applying fingerprinting method to realize better location accuracy. The system could provide parking availability information to parking search queries made by clients. A system test of ParkNet demonstrated a parking spot count accuracy of 95% and occupancy maps accuracy of 90% based on 500 km of road-side parking data gathered over two months by San Francisco taxicabs. The result also revealed that ParkNet provided enough coverage as well as cost effectiveness by an estimated ratio of 10-15 if implemented in city taxi cabs compared to a network of sensors with dedicated sensors at different parking spaces. Figure 34 shows the parking space framework using mobile sensors. [PERSON] [103] designed two complementary mobile applications called CityCare and CityCareW that aimed to enhance citizen's quality of life in smart city via crowdsourcing and participatory sensing principles. CityCare enables citizens to report concerns relating to the environment in which they reside, and also receive notifications about the progress in processing their complaints. On the other hand, CityCareW was designed for use by municipal personnel and allows them to organize, prioritize and process citizens' request. Aside from easing information sharing between citizens and services, the two apps focused on the effective administration and processing of both citizens and services, the two apps focused on the effective administration and processing of both citizens and services to the benefit of the smart city. A trial assessment revealed that the two apps are highly user-friendly and pleased their end-user to a large extent. [PERSON] [104] proposed a vehicular crowdsourcing framework named VACCS for congestion control support in smart cities and developed its theoretical basis.