Context-aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

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 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.