Nowadays, we live in a global village where many physical objects (Things) are connected to the Internet and are accessible through a web interface. Recent researches on the subject show that there has been a significant increase in the number of IoT devices during the last decade, which has attracted many researchers. Efficient searching of the Things connected to the Internet requires indexing and ranking, which are the two vital factors in the performance of search engines. Ranking is based on parameters such as the types of Things, services, current and historical locations, descriptions and features. The existing ranking techniques use either services, features or current locations as parameters for ranking, but other parameters such as historical location, type, and description are not profoundly analyzed in terms of performance. In this paper, a novel ranking mechanism for the Web of Things Search Engine (RMoWoTSE) is proposed, which ranks the indexed Things efficiently by using a combination of parameters and individual parameters. Results indicate that the accuracy, precision, recall and f-measure of the proposed ranking approach (RMoWoTSE) is better when a combination of ranking parameters is used compared to using individual ranking parameters