# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.2 Monitoring and Guidance  
Wireless network technologies advancement and the demand for mobile internet devices have enabled the creation of participatory sensing networks that can exploit mobile user's mobility, smartphone built-in sensor and communication heterogeneity of existing infrastructure to obtain ubiquitous sensing results in a low-cost and improved manner [71]. Smartphones with numerous built-in sensors can be utilized to collectively retrieve environmental, infrastructural and transportation conditions to form a body of information. Specifically, using smartphones together with IoT localization device scan effectively collect current location and distribution of indoor individuals for smart application. [PERSON] [72] proposed a mobile system called iBaby for monitoring and finding children who drift away from their parents using IoT devices (wearables) to actively detect if they are being led away by strangers; and utilizing neighboring smartphones to collaboratively find a missing child via IoT localization technologies. The system has two modes--monitoring  
Figure 27: System flow of the proposed survey-reduction framework [69]. and finding modes. In the monitoring mode, the children are provided with wearable devices that contain a mobile iBeacon and 3-axis accelerometer for calculating the distance between the parent and the child, detecting straner holding, informing parents of straner holding behavior and broadcast the signal of the mobile iBeacon to neighboring smartphones to initiate crowdsourced finding. The parents, on their side, can monitor the current state of the child, receive warning alert from wearable devices, as well as view current trajectories of missing children using smartphones/mobile tablets. In the finding mode, the BLE signals of the iBeacon is continuously broadcasted by the wearable device to the neighboring BLE scanning enabled smartphones to make internet connection via Wi-Fi access point or 4G-based stations. The signal of the mobile iBeacon received by the neighboring smartphones is utilized to seamlessly find the present location position via outdoor GPS/GNSS as well as indoor IoT/Wi-Fi localization. A posture recognition method based on multi-feature, artificial neural network and convolutional neural network (CNN) is designed to precisely detect the holding behaviors of the straner. The system was implemented using an IOS-baby prototype together with Arduino wearable device and a fixed iBeacon which proved the feasibility and precision of a system. Figure 28 shows the system architecture of iBaby. [PERSON] [73] designed and implemented EasyFind, a mobile crowdsourced guiding system (utilizing smartphones) to guide indoor people to make timely emergency evacuation as well as find lost items via IoT localization technologies. Figure 29 shows the system architecture of EasyFind. In an ideal situation, the proposed platform can utilize the density of indoor users in all areas to provide the quickest grouping parts that has the shortest/lowest moving time to a destination position. In an emergency situation, the proposed system can utilize spatial and temporal mobility of indoor users to evacuate all users in the lowest/shortest over escaping time. In addition, EasyFind system can collaboratively detect a lost item filled with a dynamic iBeacon node via crowd sourced sensing network comprised of mobile smartphone users in position with fixed iBeacon nodes. To achieve precision, in localizing the lost items, the system addressed six items localization cases to minimize the positioning error associated with both various numbers of smartphones finding the lost item and static iBeacon nodes surrounding them. EasyFind was implemented in an android based prototype and experiment proved its effectiveness. Figure 28: System architecture of iBaby [72]. [PERSON] [74] proposed a situation-enabled method for detecting and blacklisting malicious phone calls/numbers based on crowdsourcing and edge computing.