# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 2 Related Work
### IoT Services Composition and Coordination  
In artificial potential fields (APFs), [PERSON] et al. [23] provided a decentralized SC model for IoT environments. [PERSON] et al. [24] described an enterprise technology co-design methodology for an in-home healthcare station (IHHS). In addition, they used design principles of an IHHS solution, including 3C platform reuse and efficient SC, among other aspects. [PERSON] [25] introduced the ComSS middleware to operate and manage IoT compositional service flow. The performance of ComSS depends on several factors: the number of available services, inputs, data, and flow formats used. In contrast, [PERSON] et al. [26] proposed a scheme for developing business models in IoT applications based on literature, interviews, and a survey among IoT practitioners. Furthermore, [PERSON] et al. [10] presented a study on SC 4.0 cloud-centric manufacturing, as, in this environment, all objects, features, and resources representing their states, information, and mode of operation are considered services. Services, such as description, location, publication, and invocation in a network, respond to requests between consumers and service providers. On the other hand, [PERSON] et al. [27] provided two evolutionary algorithms (VPSO and NGA) for QoS in IoT, taking into account the network. For QoS, the algorithms search for the composition of services with optimal response time, network latency, cost, and reputation. [PERSON] [28] proposed a correctness rule for QoS activities and a transaction model in the context of IoT. The atomicity and isolation of information exchange are flexibly defined with his proposal to cope with the diversity of applications. From the perspective of the energy optimization mechanisms in IoT, [PERSON] et al. [22] optimized simultaneous requests in SC. Their proposal optimizes energy consumption and facilitates the services exchange between simultaneous requests in the context of the IoT. Furthermore, [PERSON] et al. [29] developed a business model devoted to companies implementing industrial internet of things technologies. The proposed concept was developed to support traditional companies transitioning to the digital market. Moreover, [PERSON] et al. [30] provided a generic business model for IoT services based on a literature review and interviews with eight IoT experts. [PERSON] et al. [31] reported an adaptive service composition framework that supports dynamic reasoning based on wEASEL, and an abstracted service model representing services and user tasks in terms of their signature, specification, and conversation. [PERSON] et al. [32] introduced a preliminary inspired biological modeling approach to exploit the peculiarities of the immune system in a way that enables dynamic and reliable software and service composition in IoT. In contrast, [PERSON] et al. [33] elaborated E2C2, a multi-cloud IoT SC algorithm that enables the deployment of an energy-aware composition scheme through the integration and retrieval of constrained services in the IoT. [PERSON] et al. [34] introduced Dracena, a data processing platform that supports easy integration of real-time IoT services. The performance of Dracena was assessed in handling many moving vehicles, where concurrently generated data was processed in real-time, while multiple services used the yielded data. [PERSON] et al. [35] developed IoT composer, a tool to support the development of IoT applications by providing a model of object behavior and composition. They also developed an implementation to effectively link and instantiate all the objects involved in the SC. In turn, [PERSON] et al. [36] presented a solution for decentralized SC based on blockchain to provide multimedia services for user subscription. For this purpose, they dynamically created user-defined services without requiring intermediary services. Considering spatial and temporal aspects, [PERSON] et al. [37] proposed a new energy SC model to use compositional patterns of devices in IoT.