# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 5 Resources, Sharing, Storage, and Energy in Crowd-IoT  
As mentioned in previous sections, mobile edge computing is one of the technologies for envisioning IoT and implies computational speed and processing at the edge of the network. The fundamental idea of mobile edge computing is to utilize the idle computing and storage resources at the mobile edge via the appropriate service or data caching and task offloading. It is different from the conventional centralized cloud computing approaches. Mobile edge caching is a promising technology for next-generation mobile networks to offer cloud services and storage capabilities at the edge of networks. This section will provide insights on resources, storage, and energy requirements for crowdsourcing IoT or edge-based crowdsourcing. These topics include mobile edge caching and sharing, multi-dimensional resources, energy services, and requirements. One of the problems with mobile edge computing is how to offload tasks to the right locations at the edge. Mobile edge computing servers need to be deployed in advance to offload tasks to the edge cloud. However it is not a trivial task to deploy and maintain a server for both economic and technical reasons. The authors in [59] investigated how mobile devices could share the heterogeneous resources at the edge. They proposed a multi-dimensional resource crowdsourcing architecture for sharing in mobile edge computing. The aim is to offload tasks optimally to mobile devices while maximizing the overall task completion and minimizing the total energy cost. Figure 21 shows the proposed sharing architecture with four mobile devices. Their simulation results showed their approach could reduce the task failure probability by 10% to 35% and the total cost by 25% to 85%. Crowdsourcing energy as a service or crowdsourced energy service has the potential to create a green computing environment. The authors in Lakhdari [60] focused on designing crowdsourced energy services to meet the IoT users' energy requirements in confined areas. Figure 22 shows the crowdsourced energy services architecture. The authors proposed a new composability model considering (i) the energy usage behavior and (ii) the spatio-temporal features of services/queries of the IoT devices. The crowdsourcing energy services are formulated as a multi-objective optimization problem to meet the user's energy requirements in the earliest and shortest time intervals. Their experimental results revealed the scalability and effectiveness of their approach in those composition scenarios. Figure 21: Architecture for sharing of devices in mobile edge [59]. [PERSON] [61] studied and conducted the economic analysis and strategic interactions between content providers (CP) and multiple edge devices (EDs). The interaction between the CP and EDs can be: (1) The CP can determine to share a certain revenue with EDs as the incentive of caching and sharing contents; (2) The EDs can determine whether to cache and share contents for the CP. This interaction is formulated as a 2-stage Stackelberg game. In the first stage, the CP maximizes its profit by determining the incentive mechanism offer and the ratio of revenue shared with EDs. On the other hand, in the second stage, EDs maximize their payoffs by determining whether to cache contents based on the incentive scheme and the caching cost. Figure 23 illustrates the Crowd-MECS architecture for edge caching and sharing. Their experimental results showed that the proposed Crowd-MECS could significantly improve the CP profit and ED total welfare. [PERSON] [62] presented a device-to-device (D2D) crowd task offloading scheme for 5G mobile edge computing that enables a huge crowd of devices at the network edge to exploit network assisted D2D collaborative sharing of computation and communication resources. Figure 24 shows the D2D crowd framework. The scheme aimed to create collaborative task execution in an energy-efficient manner for mobile users at the network edge.