# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 3 Architectures of Crowdsourcing and Proposed Crowdsourcing Internet of Things
### Centralized Architectures of Crowdsourcing  
Most studies on general architectures for crowdsourcing focused on the functions such as data storage, capture, data processing, task management, security, scalability, and privacy. Table 2 shows a summary of the research works on centralized architectures of crowdsourcing. The non-functional aspects (e.g., trust, security, and privacy) will be discussed in the next section (Section 3). Several architectures of applications focused on task management and distributing tasks to participants. Most of these works are based on the general or centralized architecture of crowdsourcing. There are some studies or reviews on crowdsourcing, e.g., its applications, techniques, and technologies. To the best of our knowledge, there is no comprehensive review of the architectures of crowdsourcing IoT or IoT architectures for crowdsourcing. In the review paper by [PERSON] [129], the authors provide a survey of the literature on mobile crowdsourcing research. From the current mobile architectures and applications, the authors classified the architectures of crowdsourcing based on typical roles, components, and functionalities. The review \begin{table}
\begin{tabular}{c c} \hline \hline
**Classification Descriptors** & **References** \\ \hline \multicolumn{3}{c}{**Architectures for Crowd-IoT**} \\ \hline Centralized architectures for Crowd-IoT & [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] \\ Decentralized architectures for Crowd-IoT & [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38] \\ \hline \multicolumn{3}{c}{**Trustworthy, Privacy and Security for Crowd-IoT**} \\ \hline Privacy in Crowd-IoT & [39; 40; 41; 42; 43; 44; 45] \\ Trustworthy in Crowd-IoT & [46; 47; 48; 49; 50; 51] \\ Security in Crowd-IoT & [52; 53; 54; 55; 56; 57; 58] \\ \hline \multicolumn{3}{c}{**Resources, Sharing, Storage and Energy Considerations for Crowd-IoT**} \\ \hline Considerations for Crowd-IoT & [59; 60; 61; 62; 63] \\ \hline \multicolumn{3}{c}{**Applications for Crowd-IoT**} \\ \hline Localization & [64; 65; 66; 67; 68; 69; 70] \\ Monitoring and guidance & [71; 72; 73; 74; 75; 76; 77] \\ Video and live streaming & [78; 79; 80; 81; 82; 83; 84; 85] \\ Semantics and data mining & [86; 87; 88; 89] \\ Disaster and risk management & [90; 91; 92; 93; 94; 95; 96; 97; 98; 99] \\ Transportation and smart cities & [100; 101; 102; 103; 104; 105; 106] \\ Ocean and environmental & [107; 108; 109; 110; 111] \\ Recruitment and workers & [112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123] \\ Finance and pricing & [124; 125; 126; 127; 128] \\ Health and medical & [124; 126; 127; 128] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the paper structure and main classification descriptors. provides a good understanding of typical functionalities, design aspects, and concerns during the development of mobile crowdsourcing systems. \begin{table}
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**Authors \& Ref** & **Year** & **Focus Areas** \\ \hline [PERSON] et al. [6] & 2008 & The authors presented AnonySense, a general architecture focusing on privacy-aware mobile crowdsourcing. Sensing tasks are distributed across anonymous participating mobile devices, verified, anonymized, in this participatory sensing mode. & The authors presented a Platform for Remote Sensing using Smartphones (PRISM) which is general architecture for crowdsourcing applications. It allows developers to package their applications as executable binaries, which offers efficiency and also the flexibility of reusing existing code modules. & The authors presented and implemented Medusa, which is a novel programming framework to provide high-level abstractions for specifying the steps for a crowd-sensing task. Medusa employs a distributed runtime system to coordinate the tasks between mobile and a cluster on the cloud.