Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This largescale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides' leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.

Wireless sensor networks are being the focus of several research application domains, and the concept of sensing-as-a-service is on the rise in wireless sensor networks. Large service repositories comprising more services and functionalities usually impose new challenges to users while identifying their preferred services and may incur higher costs. Thereby, service recommendation systems have become important and integral tools of service models to provide personalized products for consumers. However, many existing methods of sensor service recommendation focus only on service discovery. To this end, this article proposes a novel hybrid recommendation method, named new hybrid recommendation method. First, latent Dirichlet allocation model is used to compute the similarity of the latent topics of the services, and the user’s latent semantic themes are used to extract the potential interest services. Moreover, the relevance of neighbourhood services is considered, which can improve the accuracy of quality of service prediction. Experiments conducted on real datasets demonstrate that the proposed method is more accurate than the existing methods of service recommendation.

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with the fast growing number of web services and consumers, selecting a suitable service for the consumer is becoming a crucial issue. Quality of Service (QoS) plays a significant role for both service and consumer in determining requirements. A number of models have been proposed for service selection by considering QoS parameters. In this paper we propose a service selection algorithm by considering multiple consumer criteria and allowing partially matched services to be counted as a candidate for the selection process. Based on consumer criteria, the algorithm returns a list of recommended services. Experimental results from real world web services shows our algorithm has a significant improvement in the quality of recommended web services.

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