# Internet of Things and Its Applications: A Comprehensive Survey
## 5 IoT Applications
### Commercial Applications
(2018) recommended a multi-purpose prototype for facility composition concerning a cost-efficient optimization. The QoS features, including the response period, accessibility, data rate, and reputation, were measured. The recommended approach was established by using the Artificial Bee Colony (ABC) algorithm, following the purpose of growth of the stated QoS considerations and reducing the costs. The designed model was evaluated through two datasets. The results focusing on enhancing the effectiveness and the quality of the elaborated model were compared to previous solutions. However, the environments, as clouds with a fast data flow that produces additional serious needs, were not considered. Another ABC algorithm was proposed by [PERSON] and [PERSON] (2019), in order to select appropriate combined facilities in a dynamic approach. The designed plan gives a proper and suitable response period and improved precision compared to other techniques, such as a genetic algorithm, mainly in a chaotic solution space. The quality elements are studied following the Internet Protocol (IP) case study to attain an optimum solution for service selection and the composition recommended methodology. The results obtained from MATLAB software show a low response period and lower average convergence repetition. Unfortunately, this designed algorithm has not examined the effect of sequential optimization of the task nodes. In another study conducted by [PERSON] et al. (2013), a web service collection and structure, obtained through a multi-agent QoS-aware methodology, has been suggested. The recommended method is founded on a context-aware supplied optimization method for service management design. To prevent transmission expenses, it introduces a multi-agent method to coordinate and compile various service implementations. In this research, the authors applied the simple additive weighting (SAW) technique to investigate the effectiveness of the collection and structure of current QoS-aware services. It consisted of employing the chart concept to explain a shorter response period and combine the service with existing methods. [PERSON] et al. (2018) recommended a solution for IoT Mashup application through the Relational Topic Model (RTM) and Factorization Machines (FMs). The RTM was used to characterize the relationships among services and Mashup. In contrast, the FMs were employed to understand and model the communication among defined variables of high shortage, as well as to estimate the relation between Mashup and services. The authors claimed that the recommended approach significantly improves the service accuracy. [PERSON] et al. (2018) designed a new method of using the one-step HullWhite model for an IoT-based financial situation ([PERSON] et al., 2018). It directed three major phases, starting with the extraction from different databases, followed by the investigation and the checking phase, and ending with the reporting phase. The gathered data are used to evaluate the interest rate in a HullWhite model concerning various issues through the software (R.). This approach helps to decrease the complexity period through the suggested parallel method. [PERSON] et al. (2018) suggested three possible designs for front-end BlockChain (BC) services. The recommended approach diverges in establishing the Ethereum BC users, including a local object or remote server, as well as in disposing of essential stores necessary for affable operation management. The limitations of the intended approach are the need to access the Ethereum main store, need to find the position and contribution of the BC module, and the data volumes. The authors also covered the applicability of the elaborated architectures with unique and restricted communication among the distant BC client and IoT device. This helped to enhance and increase the safety with better privacy and lower system congestion. The advantage of the proposed architecture is its higher throughput and energy-efficient approach for wireless communication.