Egalitarian Transient Service Composition in Crowdsourced IoT Environment

The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the CIS market perspective. We apply a DynamicBayesian Network to model the dynamic service provision behavior of the providers. The proposed framework transforms the composition of transient services into a multi-objective temporal optimization, i.e., providing continuous services to the maximum number of consumers, and minimizing the consumers' cost of service usages over a long-term period. We incorporate a Pareto-based genetic algorithm to enable the fair distribution of services among the consumers. Experimental results prove the efficiency of the proposed approach in terms of continuous availability of service as well as fair distribution among consumers.

Automated summary

This article proposes an egalitarian transient service composition framework from the perspective of the CIS market. It models the dynamic service provision behavior of providers using a Dynamic Bayesian Network and incorporates a Pareto-based genetic algorithm to ensure fair distribution of services among consumers.