[Paper Review] iSSEE: IMS Sensors Search Engine Enabler for Sensors Mashups Convergent Application

Abdelkader Outtagarts, Olivier Martinot|ArXiv.org|Dec 9, 2009

Video Analysis and Summarization14 references4 citations

TL;DR

This paper proposes iSSEE, a novel IMS-based Sensor Search Engine Enabler that dynamically discovers and indexes networked sensors (e.g., temperature, camera, audio) by their availability, location, and data type. By integrating sensor data with web content, iSSEE enables real-time, context-aware multimedia mashup applications through a standardized, scalable discovery mechanism within IMS architectures, enhancing convergence of sensing and communication services.

ABSTRACT

Integrating the sensing capabilities in Internet Protocol network will open the opportunities to build a wide range of novel multimedia applications. The problem when using sensors (e.g. temperature sensor, camera, audio, humidity, etc.) connected to the network is to know dynamically at any time if they are always connected or not, what type of data they can transmit and where they are geographically located. This paper describes an application enabler: IMS Sensor Search Engine Enabler (iSSEE), which allows IMS applications using sensors and IMS based devices, to get information about the sensor availability, its location and the type of the sensor. Using data collected by sensors and from the web, mash-ups convergent applications use cases are proposed by combining the contents from heterogeneous data.

Motivation & Objective

• To address the challenge of dynamically discovering and managing heterogeneous sensors in IP-based networks.
• To enable real-time, context-aware multimedia applications by exposing sensor metadata through a standardized interface.
• To integrate sensor data with web-based content for creating convergent, multi-source mashup applications.
• To support IMS-based services in discovering available sensors, their geographical locations, and data capabilities.
• To provide a scalable, extensible framework for sensor discovery that supports future converged communication and sensing services.

Proposed method

• iSSEE leverages the IP Multimedia Subsystem (IMS) framework to provide a standardized signaling and service delivery environment for sensor integration.
• It employs a centralized search engine component that indexes sensor metadata, including sensor type, location, availability status, and data format.
• Sensors register their presence and capabilities via SIP-based registration mechanisms, enabling dynamic updates to the index.
• The system uses a semantic-aware metadata model to describe sensor properties, allowing for intelligent querying and discovery.
• Mashup applications query the iSSEE engine using semantic or keyword-based criteria to retrieve relevant sensor data streams.
• Data from sensors and external web sources are correlated to enable context-aware, convergent multimedia applications.

Experimental results

Research questions

• RQ1 How can networked sensors be dynamically discovered and indexed in real time within an IMS-based architecture?
• RQ2 What mechanisms enable the integration of heterogeneous sensor data with web-based content for unified application development?
• RQ3 How can sensor location, availability, and data type be effectively modeled and exposed for service discovery?
• RQ4 What role does the IMS framework play in enabling scalable, interoperable sensor-based multimedia applications?
• RQ5 How can sensor mashups be built to support real-time, context-aware, convergent services?

Key findings

• iSSEE successfully enables real-time discovery of sensors based on their type, location, and availability status through a standardized IMS interface.
• The system supports dynamic registration and updates of sensor metadata, ensuring up-to-date information for application consumption.
• By combining sensor data with web content, iSSEE facilitates the creation of context-aware multimedia mashup applications with enhanced interactivity.
• The semantic metadata model allows for efficient, intelligent querying of sensor resources, improving service composition and discovery accuracy.
• The framework demonstrates feasibility and scalability for integrating sensing capabilities into converged communication and multimedia services.
• The implementation shows that sensor discovery and integration can be effectively managed within the IMS ecosystem, enabling new classes of real-time, location-aware applications.