The Internet of Things (IoT) is an emerging paradigm where practically every (physical and virtual) thing will be interconnected through innovative distributed services. Since the number of connected things is rapidly growing, IoT systems will require the composition of plenty of services into complex workflows. Thus, scalability in terms of the size of IoT systems becomes a significant concern. In this paper, we review and evaluate the fundamental semantics of existing IoT service composition mechanisms to determine how well they fulfil the scalability requirements of IoT systems. We identify scalability desiderata and, accordingly, our findings show that dataflows, orchestration and choreography do not fully satisfy such desiderata, unlike a novel composition mechanism called DX-MAN.

Service composition is currently done by (hierarchical) orchestration and choreography. However, these approaches do not support explicit control flow and total compositionality, which are crucial for the scalability of service-oriented systems. In this paper, we propose exogenous connectors for service composition. These connectors support both explicit control flow and total compositionality in hierarchical service composition. To validate and evaluate our proposal, we present a case study based on the popular MusicCorp.

Scalability is an important concern for Internet of Things (IoT) applications since the amount of service interactions may become overwhelming, due to the huge number of interconnected nodes. In this paper, we present an IoT scenario for real-time Electrocardiogram (ECG) monitoring, in order to analyze how well different kinds of service interactions can fulfill the scalability requirements of IoT applications.

Autonomic IoT systems require variable behaviour at runtime to adapt to different system contexts. Building suitable models that span both design-time and runtime is thus essential for such systems. However, existing approaches separate the variability model from the behavioural model, leading to synchronization issues such as the need for dynamic reconfiguration and dependency management. Some approaches define a fixed number of behaviour variants and are therefore unsuitable for highly variable contexts. This paper extends the semantics of the DX-MAN service model so as to combine variability with behaviour. The model allows the design of composite services that define an infinite number of workflow variants which can be chosen at runtime without any reconfiguration mechanism. We describe the autonomic capabilities of our model by using a case study in the domain of smart homes.

With the advent of the Internet of Things, scalability becomes a significant concern due to the huge amount of data involved in IoT systems. A centralized data exchange is not desirable as it leads to a single performance bottleneck. Although a distributed exchange removes the central bottleneck, it has network performance issues as data passes among multiple coordinators. A decentralized data flow exchange is the only solution that fully enables the realization of efficient IoT systems as there is no single performance bottleneck and the network overhead is minimized. In this paper, we present an approach that leverages the algebraic semantics of DX-MAN for realizing decentralized data flows in IoT systems. As data flows are not mixed with control flows in algebraic service compositions, we developed an algorithm that smoothly analyzes data dependencies for the generation of a direct relationship between data consumers and data producers. The result prevents passing data alongside control among multiple coordinators because data is only read and written on a data space. We validate our approach using the Blockchain as the data space and conducted experiments to evaluate the scalability of our approach. Our results show that our approach scales well with the size of IoT systems.