# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.3 Ocean/Marine & Environmental  
In the smart ocean, the Internet of Things (IoT) has grown quickly. It can monitor and manage marine complex data by integrating data and information generated from various mobile maritime terminals (including buoys, boats, cargo, and offshore platforms). Clearly, IoT for the smart ocean presents a viable way to solve those pressing issues by controlling and exploiting ocean data [107]. Mobile crowdsourcing is an efficient methodology for collecting IoT data and has been employed by researchers and industries in developing solutions to smart ocean problems. [PERSON] [52] constructed a crowdsourcing privacy protection scheme technique to solve the issue participants' privacy threats in a crowdsourced- IoT network for the smart ocean, using multi-authority ciphertext-policy attribute-based encryption (CSMA-ABE). The proposed system model comprises of requesters, semi-trusted third party, workers, a crowd  
Figure 35: Architecture of Sensquare [105]. sourcing platform, and a set of attribute authorities. The authors applied an independent key component distribution method via multiple authorities, taking into account the cooperation and independence of the various attributes, to enable the crowdsourcing platform to successfully disseminate security responsibility. They also introduced into the platform the concept of partial decryption in order to decrease mobile users' computation cost. Furthermore, they completed the attribute revocation method which ensured the authenticity of subsets of workers' attributes as well as the task search function, which allowed employees to swiftly perform searching jobs on the fly for the crowdsourcing platform. Simulation results demonstrated that their proposed method outperforms other comparable schemes in terms of lowering the time of consumption. [PERSON] (2018) presented a framework called DYNAMO (Distributed leisure Yachts sensor Network for Atmosphere and Marine Observation) for collecting coastal data generated by sensors and devices installed in maritime machinery in a crowdsourcing manner, in accordance with the Internet of Floating Things (IoFT) paradigm for environmental model simulation. The aim of the authors is to illustrate how the IoFT, as well as data on coastal marine life collected from instruments, in-built vessels, and sensors may be used to enhance the numerical prediction of the diffusion and transportation of pollutants for the quality evaluation of marine farm's products. As a result, utilizing the crowdsourced seabed depth data from the Bay of Pozzauoli in Naples, Italy with their proposed system as a software environment test-bed, they demonstrated the enhancements made in a practical implementation. The bathymetry data were utilized to generate an enhanced form of the numerical representation of the seafloor which the models employed to improve the general outcome for estimating pollutant distribution details. [PERSON] (2019) researched on the real-time and fine-grained air quality at the city scale using crowdsourced vehicles and their on-board sensors which substantially enhanced the sensing system's practicality. The idea behind their study was that air component concentration inside a vehicle is extremely comparable to that of its surrounding environment when the vehicles are open due to the reality that air will interchange between the inside and outside of the car through the open window. As a result, they introduced an intelligent algorithm to identify vehicle air exchange status, and the extracted pollutant concentrations when the concentration trend converges after the windows are opened. The sensed convergent value is referred to as the comparable degree of air quality in the surrounding environment. Streams of real-time air quality data gathered from around the city were analyzed in a data center using their IoT cloud platform and used to produce a fine-grained city air quality level map.