# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.4 Semantics & Data Mining  
Semantic information has been a critical link in connecting human intelligence to media data as well as big data in IoT contexts. As the IoT is becoming more social, computing based on crowdsourcing has emerged as a significant research subject in a variety of fields [86]. Many crowdsourcing frameworks have been created to investigate and address important issues in semantic information processing. [PERSON] [87] proposed a crowdsourcing scheme for semantic fusion and studied the critical challenges of processing semantic information of crowdsourced socialized IoT data. Social media data semantic fusion is confronted with new problems such as-Source heterogeneity, Storage heterogeneity, multimodal expression and dissemination socialization. In order to adjust to the latest prerequisites of social media semantic fusion in crowd sourcing environment, the authors designed an efficient fusion strategy for multimodal semantics together with a distributed scheme to wholly ensure sharing of semantic information as the social media files are distributed. The proposed framework is divided into three modules--semantic extraction, multimodal semantic fusion and semantic storage and distribution module. In the first stage, crowdsourcing users extract semantic information from heterogeneous media from various sources. They defined an extraction workflow that includes an interaction interface for the users. Tasks are suggested to appropriate people based on their semantic information. The system monitors extraction quality and modifies jobs as needed. Figure 32 illustrates the system framework. In the next stage, semantic items are disseminated. To avoid duplicate information, multimodal semantic items are synthesized to a single format while also minimizing the number of dimensions. They next extract some intelligence from the semantic data and improve it to remove any incorrect semantic data. The semantic data merged in the second stage is saved and disseminated with regards to the media files in the third stage. Because  
Figure 32: System framework review [87]. semantic information is dynamic, the authors proposed a reading and writing mechanism for semantic updating. Finally, the authors created an HBase-based database [88] to save semantic data and ensure synchronization. An experimental evaluation of the proposed framework on a categorized and an uncategorized dataset, revealed that the algorithm performed better on categorized dataset than in uncategorized dataset. [PERSON] [131] proposed a topic modelling framework based on crowdsourcing to enable various persons to contribute their knowledge and then utilized the outcome of their interaction to deduce meaningful topics. Figure 33 shows the service marching architecture for IoT based on crowd-LDA. First, the proposed model extracts raw topics from raw description texts and then requests users to cluster topic words using their own cognition so as to reduce the sparseness problem. The authors designed a crowd clustering algorithm to resolve the weakness among users and create accurate word clusters. Consequently, the author presented a new modelling method that integrates word clusters to extract feature vectors from raw data description text. Furthermore, they built a service matching algorithm which utilized features to discover service. Experimental study of the proposed method using real-world dataset demonstrates its effectiveness in terms of signature extraction and service matching in IoT. [PERSON] [87] designed a fog-cloud hybrid framework that enables energy efficiency among large ad-hoc crowd, comprising of large distributed IoT devices and social network within a smart city context. A framework based on fog computing is presented to incorporate IoT devices which interface with ad-hoc crowd to ensure energy efficiency.