# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.3 Video & Live Streaming  
The widespread availability of IoT cameras, including dash mounted cameras, smartphone cameras, drone cameras, and Google Glass, has supported a plethora of beneficial applications [78]. Video and photo crowdsourcing, which takes advantage of the power of IoT devices and everyday mobile users for large-scale sensing, has been utilized by researchers in many applications areas such as 3D reconstruction, live streaming, etc. [PERSON] [79] proposed a framework for photo crowdsourcing based on Edge computing. Figure 30 shows the proposed edge computing-based crowd sourcing framework. Their aim is to utilize ubiquitous mobile and IoT devices within the network edge to harvest a collection of representative photos for real-time 3D model reconstruction in cognizance to monetary cost and network resource issue. The framework consists of three modules: (1) photo/participant module which utilizes photo crowdsourcing for collection of photos at reduced monetary cost. (2) a 3D reconstruction module, which can utilize the selected photos contained in the MEC server side to reconstruct the needed 3D modules. (3) a caching module that makes dynamic decisions of whether to cache the reconstructed 3D reconstruction in the MEC server or not. First, the authors recommended a monetary-based motivation strategy which promotes pricing of photos based on data size, freshness, resolution and wireless channel state of their linked participants. This is to ensure the selection of enough and superior fresh photos from closet participants which guarantees great results of 3D reconstruction. They also proposed a photo selection strategy to choose a representative set of photos with low cost to meet the expected coverage requirement. However, finding an optimal set of photos with low cost is proven to be NP-hard. The author therefore presented a grid-based approximation algorithm in order to get a near-optimal solution. Furthermore, the author designed an optimal resource allocation strategy to reduce the highest uploading delay. An evaluation of the proposed framework through experimenting with real-world datasets revealed that the framework is superior to existing schemes. Figure 30: Proposed edge computing-based crowd sourcing framework [79]. [PERSON] [80] demonstrated a system that employed commercial fleets as Vehician Fog Computing (VFC) nodes for real-time video crowdsourcing and processing from vehicles within the communication domain. The feasibility of the system is assessed by first, investigating the availability of fog nodes with respect to the spatial temporal dispersal of the buses. A real-life dataset generated from city of Luxembourg traffic network was used for the simulation experiment. The networking performance of the proposed VFC-based video crowd sourcing system was evaluated using Simulation of Urban Mobility (SUMO) [81] and VeinsLTE [82] at differing time intervals over 24 h. The simulation result revealed that over 90% client vehicles are positioned lower than 200 m from the nearest vehicular fog node, which implies that within a short transmission distance, they can stream videos in real-time to the VFC node. However, when there is crowd in the traffic (i.e., over 200 client vehicles flying a designated area), more than 80% of client vehicles would be able to connect to a VFC node within 100 m.  
[PERSON] [83] provided a background review of current crowdcast solution in uniform crowdcast as well as personalized crowdcast, and highlighted their challenges and opportunities. They further designed an intelligent framework based on edge-learning (EL cast) which combines CNN and deep reinforcement learning (DRL) in an edge computing architecture to enable personalized crowdcast. A trace-driven experiment showed that the proposed framework was more effective than current approaches.