# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.4 Recruitment & Workers  
As an emerging intelligent data sensing model used in a wide-range of sensor applications for instance in the industrial IoT, mobile crowd sensing (MCS) allocates industrial sensing jobs to workers in order to collect and share data, creating a promising future for developing a robust industrial system and enhancing industrial efficiency (IIoT). The concept of MCS has been utilized to solve several challenges of industrial relations. [PERSON] [112] proposed a low-complexity collaborative Mobile Crowdsourcing (CMCS) team recruitment strategy for forming and hiring a group of socially linked employees with the necessary abilities to complete a CMCS job. Figure 36 shows the typical CMCS platform. The proposed strategy inspired by Swam intelligence (genetic algorithm) allowed for project matching and virtual team building based on four key fuzzy-logic-based criteria--degree of knowledge, recruitment cost, strength of social relationship, and platform confidence level. The strategy aimed at forming a team who are not just talented but also socially connected. The results of the proposed system evaluation using Monte Carlo Simulations demonstrated that the proposed CMCS recruitment algorithm realized loss outcomes to the baseline optimal Integer Linear Program (ILP) strategy while saving notable computational time and outperformed the Particle Swarm Optimization (PSO). [PERSON] [113] illustrated an automated crowdsourcing method to coordinate collaboration between financial advisers and investors. The system provides a recruiting platform to connect financial advisers to investors based on their profile and attributes, as well as the prior activities of their peers, without interfering with their private portfolio optimization strategies. An unsupervised approach was applied to the crowdsourcing database to group advisers with a high degree of similarity into clusters, reducing the search space. Following that, a machine learning regression model was trained and estimated the matching score that an investor could get if he or she selected a specific investor. In addition, a double-phased many-to-many maximum weight matching method was utilized to convert the scores to bipartite graph weights to find an appropriate investor-advisor pair. The performance evaluation of the proposed approach through simulation revealed a higher profit for both the investors and financial advisors compared to other conventional approaches. [PERSON] [114] researched the challenges of least workers selection to help big MCS systems execute sensing tasks more effectively and achieve specified coverage while satisfying certain restrictions. They introduced a Many-objective worker selection technique (MaOWS) to  
Figure 36: Typical CMCS platform [112]. optimize the profit of the workers and reduce payment from the requester, while increasing platform profit. They also presented a many-objective optimization technique utilizing enhanced differential evolution algorithm (EDEA) for a quick extraction of possible worker selection viable result scope while creating the required tradeoff between the objectives. The outcomes of the experiments demonstrated the proposed technique's effectiveness on real-life data. [PERSON] [115] suggested a Spatial Mobile Crowdsourcing (SMCS) recruitment method utilizing automated Social Internet of Things (SIoT) service discovery for the selection of reliable employees that satisfy the requirements of the requester. Figure 37 illustrates the SMCS high-level architecture. The system provided an employee filtering stage that applied the Louvain community detection algorithm (CD) on the SIoT relation graphs to reduce the employee's scope of the search for a selection of potentially reliable candidates. It also employed an ILP in the selection process to identify the final selection of devices/workers. The ILP improved employee efficiency metric taking into account the degree of skill/specs, cost of recruitment, and level of trustworthiness of IoT devices recruited.