# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things
### Trustworthy in Crowd-IoT  
Trust and privacy in the previous subsection are usually synonymous. A solution that targets protecting trust relationships should also be protecting user privacy and device information. Many trust-based approaches target to balance the connectivity between service or task requestor/owner and workers. This section discusses recent and specific works related to crowdsourcing IoT for centric or edge computing. In crowdsourcing IoT, a network with trust is important for sustaining applications. However, trust also needs strict privacy-preserving mechanisms that can assure protected environments for all users and prevent the leak of an individual's personal data and damage of social reputation. An earlier work on trustworthy crowdsourcing in cloud-centric IoT can be found in [46]. The authors proposed mobility-aware crowdsourcing (MACS) architecture in IoT for smart city management. The MACS applied an incentive mechanism that chooses the users based on an auction and makes payments to the chosen users based on their bids. The MACS also predicts the future locations of participating users in the auction. The authors also proposed Mobility-Aware Trustworthy Crowdsourcing (MATCS) for crowdsourcing IoT. The MATCS first confirms the presence of the participants and updates the trustworthiness of users. The bids, marginal values, and trustworthiness are used to determine the payments to the auction winners. The authors performed simulations to evaluate the performance of MATCS and showed the improvement of smart city management authority (up to 70%) and the decrease in disinformation probability (up to 55%). The authors in [47] proposed a crowdsourcing assignment model based on social relationship cognition and community detection. Figure 12 shows the proposed architecture of the crowdsourcing model and a use case. In the scenario, based on the mobility of users, a trusted route is established from the service requester (SR) to the service provider (SP) for the assignment of crowdsourcing tasks to realize efficient distribution and delivery of requests. The assignment of crowdsourcing uses decision-making factors, including the service quality factor (SQF), link reliability factor (LRF), and region heat factor (RHF). The procedure has the following steps: (1) Detection of crowdsourcing service candidates in the target location; (2) Computation of crowdsourcing decision-making factors of SQF, LRF, and RHF; and (3) Assignment of crowdsourcing sensing task using the crowdsourcing algorithm. For performance evaluation, simulations were conducted on the mobile crowdsourcing platform developed by the authors. The experimental results showed the correctness and robustness of the approach. Figure 11: Privacy-preserving incentive mechanism for social crowdsourcing [45]. [PERSON] [48] proposed just-in-time memoryless trust for crowdsourced IoT services. The provider's trustworthiness is evaluated without requiring historical records (memoryless). Figure 13 shows their framework, which utilizes the service session characteristics and its surrounding environment to infer the service's trustworthiness. Their approach has three stages: (1) IoT service initiation, (2) IoT service monitoring; and (3) Trustworthiness assessment. Another challenge of assessing trust in dynamic nature is its dependability on how the service is used. To overcome this challenge, the same author [PERSON] [49], proposed an approach termed Adaptive Trust framework for crowdsourced IoT services. The framework of usage-based trust assigns trust values to IoT services based on the consumers' usage. Figure 12: Crowdsourcing assignment model based on social relationship cognition and community detection [47]. Figure 13: Just-in-time memoryless trust for crowdsourced IoT services [49]. [PERSON] [50] investigated the impact of trustworthy relationships between Social IoT nodes on social team crowdsourcing mechanism sensing in social IoT.