  As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a substantial acceleration of the growth rate in the future. It is
also evident that the increasing number of IoT middleware solutions are
developed in both research and commercial environments. However, sensor search
and selection remain a critical requirement and a challenge. In this paper, we
present CASSARAM, a context-aware sensor search, selection, and ranking model
for Internet of Things to address the research challenges of selecting sensors
when large numbers of sensors with overlapping and sometimes redundant
functionality are available. CASSARAM proposes the search and selection of
sensors based on user priorities. CASSARAM considers a broad range of
characteristics of sensors for search such as reliability, accuracy, battery
life just to name a few. Our approach utilises both semantic querying and
quantitative reasoning techniques. User priority based weighted Euclidean
distance comparison in multidimensional space technique is used to index and
rank sensors. Our objectives are to highlight the importance of sensor search
in IoT paradigm, identify important characteristics of both sensors and data
acquisition processes which help to select sensors, understand how semantic and
statistical reasoning can be combined together to address this problem in an
efficient manner. We developed a tool called CASSARA to evaluate the proposed
model in terms of resource consumption and response time.
