Decentralized Data Flows in Algebraic Service Compositions for the Scalability of IoT Systems

Arellanes, D. and Lau, Kung-Kiu (2019) Decentralized Data Flows in Algebraic Service Compositions for the Scalability of IoT Systems. In: IEEE 5th World Forum on Internet of Things (WF-IoT 2019) :. IEEE, pp. 668-673. ISBN 9781538649817

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Abstract

With the advent of the Internet of Things (IoT), scalability becomes a significant concern due to the huge amounts of data generated in IoT systems. A centralized data exchange is not desirable as it leads to a single performance bottleneck. Although a distributed data exchange removes the central bottleneck, it has network performance issues as data passes among multiple coordinators. A decentralized approach is the only solution that fully enables the realization of efficient IoT systems, since there is no single performance bottleneck and network overhead is minimized. In this paper, we present an approach that leverages the semantics of DX-MAN for realizing decentralized data flows in IoT systems. The algebraic semantics of such a model allows a well-defined structure of data flows which is easily analyzed by an algorithm that forms a direct relationship between data consumers and data producers. For the analysis, the algorithm takes advantage of the fact that DX-MAN separates control flow and data flow. Thus, our approach prevents passing data alongside control among multiple coordinators, so data is only read and written on a decentralized data space. We validate our approach using smart contracts on the Blockchain, and conducted experiments to quantitatively evaluate scalability. The results show that our approach scales well with the size of IoT systems.