Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things (1309.3618v1)

Abstract: The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

• The paper introduces CASSARAM, a context-aware model utilizing semantic and quantitative reasoning for efficient sensor search and selection in large-scale IoT networks.
• The model employs a sensor ontology based on SSN, a comprehensive context framework, and algorithms like CPWI and CPHF to rank and filter sensors efficiently based on user preferences.
• Evaluation results demonstrate significant improvements in processing time and memory usage, facilitating CASSARAM's integration into IoT middleware for enhanced dynamic sensor data acquisition.

Analyzing Sensor Search Techniques for Sensing as a Service in IoT

The paper "Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things" outlines a comprehensive model for sensor search and selection in the context of the Internet of Things (IoT), coined as 'Sensing as a Service.' The authors present CASSARAM, a context-aware sensor search model designed to efficiently address the tremendous data onslaught anticipated with the global proliferation of IoT devices. The paper explores methodologies for selecting a suitable subset of sensors from a plethora of IoT sensors based on user-defined context and preferences.

Overview of the Proposed Model

CASSARAM focuses on facilitating the selection, search, and ranking of sensors by harnessing user preferences through contextual characteristics like reliability, accuracy, location, and battery life. The authors emphasize the inadequacy of traditional text-based search engines for sensor selection, advocating for a nuanced approach that employs semantic and quantitative reasoning.

Contextual Data Modeling

At the core of CASSARAM is a sensor ontology built on the Semantic Sensor Network (SSN) Ontology, ensuring comprehensive modeling of sensor characteristics. The paper introduces a versatile context framework that includes availability, accuracy, precision, latency, and other sensor attributes essential for effective sensor deployment in various applications.

Key Methodologies and Techniques

The authors detail algorithms that capture user preferences and priorities through an interactive user interface, normalize context properties, and employ quantitative reasoning to rank sensors. The paper introduces the Comparative-Priority Based Weighted Index (CPWI), a Euclidean distance-based metric used to rank sensors in a multi-dimensional space defined by the context attributes.

Additionally, CASSARAM incorporates heuristic methods such as the Comparative-Priority Based Heuristic Filtering (CPHF) to process vast sensor networks efficiently. By discarding sensors that diverge significantly from user-defined ideals, the model significantly improves computational efficiency.

Sensor Search Challenges and Distributed Approaches

With the vast distribution of IoT sensors, the authors address challenges by proposing distributed sensor search mechanisms, enabling the use of chain and parallel processing techniques supplemented by a k-extension strategy to optimize bandwidth usage.

Evaluation and Implications

Extensive evaluation against various parameters highlighted CASSARAM's scalability and efficiency. Numerical results demonstrate substantial improvements in processing time and memory usage when leveraging context-based heuristic treatments, which become pivotal for supporting IoT architectures at scale.

These optimizations facilitate the integration of CASSARAM into existing IoT middleware solutions like GSN, potentially revolutionizing sensing applications by providing dynamic and efficient sensor data acquisition frameworks.

Future Prospects

The paper suggests that CASSARAM's strategies might significantly impact multiple domains, especially for task management in sensor networks. Future research could investigate further efficiencies using machine learning to dynamically adjust parameters such as 'k' in distributed querying, allowing CASSARAM to adapt intuitively across various IoT applications.

In conclusion, this paper presents a robust model ensuring effective sensor search and selection in the swelling IoT landscape, leveraging semantic reasoning and quantitative insights to yield substantial efficiency and scalability benefits.