# 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
& The authors presented MoCoMapps (Mobile Collaborative Map-based Applications) to provide a mobile service for users to use and create mobile map-based applications (e.g., data entry, data look-up, and collaboration). & The authors presented MoCoMapps (Mobile Collaborative Map-based Applications) to provide a mobile service for users to use and create mobile map-based applications (e.g., data entry, data look-up, and collaboration). & The work is about general architectural components, including the collection, aggregation, and analysis of data. \\ \[PERSON] et al. [13] & 2013 & Crowdsourcing architecture/system with a focus on campaign management. Contains four components: (1) Task management; (2) User management; (3) Contribution management; and (4) Workflow management. & (1) Task management; (3) Contribution management; and (4) Workflow management. & The authors focused on crowd tasks and proposed an underlying categorization structure, which extracted from the task descriptions and deployed the worker profiles from social network information. Then the most suitable workers and tasks are automatically matched. & Architecture for mobile crowdsourcing applications. The architecture is basic and only has client/participant and server. On the server-side, recruitment and interaction with participants are performed. On the client-side, functions, e.g., user interface and data capture are provided. The data include geospatial data captured and shared using mobiles or smartphones. & The authors focused on the functional components for the transformation process. These components are: (i) Assigners—initiate and manage the task, (ii) Providers—respond to task and submit solutions; and (iii) Intermediation platform—connects assigners and providers, functions as crowdsourcing enabler. & The authors focused on the functional components for the transformation process. These components are: (i) Assigners—initiate and manage the task, (ii) Providers—respond to task and submit solutions; and (iii) Intermediation platform—connects assigners and providers, functions as crowdsourcing enabler. & The authors focused on the functional components for the transformation process. These components are: (i) Assigners—initiate and manage the task, (ii) Providers—respond to task and submit solutions; and (iii) Intermediation platform—connects assigners and providers, functions as crowdsourcing enabler. & (2) Task management; (3) Contribution management; and (4) Workflow management. & (4) Task management; (5) Task management; (6) Task management; (7) Task management; (8) Task management; (9) Task management; (10) Task management; (11) Task management; (12) Task management; (13) Task management; (14) Task management; (15) Task management; (16) Task management; (17) Task management; (18) Task management; (19) Task management; (20) Task management; (21) Task management;  
The authors in [129] proposed a generalized centralized mobile crowdsourcing architecture for IoT. Figure 2 shows the centralized mobile crowdsourcing architecture for IoT in which the information processing is performed at the cloud server. This architecture has four layers: (1) Mobile sensing/gathering layer; (2) Connectivity/network layer; (3) Crowd processing layer; and (4) End-user layer. The mobile sensing layer has sensors or sensing devices, including user devices such as mobile/smartphones, wearable devices, smart appliances, smart vehicles, and so on. The major task of the connectivity layer is to provide network connectivity to mobile crowdsourcing. These communication networks can be cellular networks (3G/4G/5G), wireless sensor networks (WSN), Wi-Fi, Bluetooth, and vehicular ad hoc networks (VANETs). These communication technologies transfer the data collected by the sensors, including mobiles, to the cloud or server. In the crowd processing layer, the data from sensors/mobiles and crowd tasks are stored, processed, analyzed, and visualized. Cloud computing which is a centralized paradigm, plays a vital role in the above purposes.