Context-aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware
Charith Perera, Arkady Zaslavsky, Peter Christen, Michael Compton and, Dimitrios Georgakopoulos

TL;DR
This paper introduces CASSARAM, a context-aware model for efficient sensor search, selection, and ranking in IoT, addressing the challenge of choosing optimal sensors from large, overlapping sensor pools using semantic and quantitative reasoning.
Contribution
The paper presents a novel sensor search and ranking model that combines semantic querying with weighted Euclidean distance for improved sensor selection in IoT environments.
Findings
CASSARAM effectively ranks sensors based on user priorities.
The model reduces resource consumption and response time.
It demonstrates improved sensor selection accuracy.
Abstract
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…
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