The Internet of Things envisions billions of physical devices connecting over the Internet to provide a near real-time view of the state of the world. These devices' capabilities can be abstracted as IoT services and provided on demand. To enable quality-aware service provision, Service Level Agreements (SLA) are widely used as legally binding contracts to obligate service providers to comply with a pre-negotiated Quality of Service (QoS). With a possible ever-increasing number of service providers in an IoT environment, multi-bilateral SLA negotiation is likely to be prohibitively time-consuming without an a-priori process to select trusted candidate providers with whom to negotiate. In this article, a trust model is proposed to identify trusted service providers in a dynamic IoT environment before attempting to negotiate an SLA. A trust credit that indicates both the SLA’s fulfillment and the possible negotiation success rate is derived based on historical information relating to a service’s previous negotiations and its monitored run-time performance. Indiscernibility analysis in Rough Set theory is used to predict the negotiation success rate, while Bayesian inference is applied to deduce the possibility of SLA violation according to the monitored data. The simulation results demonstrate the feasibility and efficiency of the proposed trust model.

This paper discusses the challenges in developing an IoT platform for registering, discovering and composing heterogeneous services from multiple provider types, viz., Wireless Sensor Networks (WSNs), Web Service Providers (WSPs), and Autonomous Service Providers (ASPs), without human intervention. The platform executes a service composition in a decentralised fashion, with a mechanism to detect service provider failure and fallback to previously discovered services to complete a service composition flow. We comment on technical and scientific challenges involved in managing these heterogeneous, autonomous, and resilient IoT services.