# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.1 Disaster, Emergency and Risk Management  
Disaster is a prevalent phenomenon in our world today. Every country and human is vulnerable to natural or man-made disasters [90]. During such an emergency situation, gathering and analyzing accurate information in a timely manner is of great importance especially when lives and critical infrastructures are in danger. Crowdsourcing and IoT paradigms have been employed to develop applications to respond to disaster and emergency situations [91]. [PERSON] [92] designed a disaster rescue framework called Echo for searching missing people in a disaster situation. The framework employed edge servers (ECV) computing, communication and storage abilities to preprocess and analyze in real-time, crowdsourced disaster photos to generate more precise visuals that will immensely assist in conducting emergency response and rescue operation. In order to conserve bandwidth, Echo allowed for only the extraction and uploading of significantly associated photos to the cloud center for successive facial recognition. Furthermore, the author developed an adaptive photo detector to make optimal use of the valuable and unsteady communication bandwidth while still ensuring photo sensing precision and recall rate. Echo prototype was implemented, and its performance was evaluated using a real-world disaster photo set. Using the search for missing persons as an example, tests showed that Echo can assist with the real-time and effective search for missing persons. [PERSON] [93] applied crowdsourcing based and fog computing model to detect disasters in actual-time as well as disseminate timely warning to ensure the safety of the public. A data offloading mechanism is employed for the model to ensure the transfer of IoT data related to disaster to the fog platform even when a direct connectivity is unavailable. The suggested model as well as its data offloading mechanism showed great effectiveness in detecting disaster in actual time, and in broadcasting timely reportage to the public when compared to traditional cloud computing-based models for for detecting disaster situations. [PERSON] [94] proposed a content-aware crowdsourcing method called COCO which takes advantage of edge computing to enable image sensing in real time disaster environment. The framework takes advantage of mobile edge computing technologies to deliver dynamic computing resources close to the data source. To overcome the resource limitation on mobile devices, the proposed method filters crowdsourced pictures at the data source and transmits only images that include relevant items to the application. To maximize the upload speed under varied bandwidth conditions, the authors discovered the best processing speed of the computer vision detectors under a given bandwidth and presented an adaptive technique to make the speed of processing optimal with varying bandwidth. A coco prototype was implemented and its performance was evaluated with a real-world image set. The result of the experiment showed that COCO can regulate the content of photos sent to the cloud as well as manage information loss within a certain limit. Furthermore, as compared to methods with adaptive technique, the adaptive technique fast-tracks image uploading by 21% to 35%. [PERSON] [95] designed RAMBLE, an architecture for geo-referenced content sharing in territories with limited communications infrastructure such as those seen during rescue operation after a natural disaster. RAMBLE employed mobile edge-clouds, network of neighboring mobile devices, as well as light-weight cloudlets that service a small geographical region. The authors designed an Android app which rambling users utilized to create geo-referenced materials (such as sensor reading, video, text messages, or photos) and distributed them opportunistically to neighboring devices, cloudlets, or even cloud servers, as enabled by irregular wireless connections.