Microsense: Sensor Framework for IoT System-on-Chip

Microsense: Sensor Framework for IoT System-on-Chip

Srinivasa K.G. (CBP Government Engineering College, New Delhi, India), Ganesh Hegde (M S Ramaiah Institute of Technology, Karnataka, India), Kushagra Mishra (M S Ramaiah Institute of Technology, Karnataka, India), Mohammad Nabeel Siddiqui (M S Ramaiah Institute of Technology, Karnataka, India), Abhishek Kumar (M S Ramaiah Institute of Technology, Karnataka, India) and Pradeep Kumar D. (M S Ramaiah Institute of Technology, Karnataka, India)
Copyright: © 2016 |Pages: 18
DOI: 10.4018/IJHCR.2016070104
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Abstract

With the advancement of portable devices and sensors, there has been a need to build a universal framework, which can serve as a nodal point to aggregate data from different kinds of devices and sensors. We propose a unified framework that will provide a robust set of guidelines for sensors with varied degree of complexities connected to common set of System-on-Chip (SoC). These will help to monitor, control and visualize real time data coming from different type of sensors connected to these SoCs. We have defined a set of APIs, which will help the sensors to register with the server. These APIs will be the standard to which the sensors will comply while streaming data when connected to the client platforms.
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In the current scenario, mobile phones not only serve as communication means, but also as a source of data from sensors that the distributed human centric sensing applications can collect and exploit. Among them, environmental monitoring systems and emergency response systems will specifically benefit from human-based sensing. Owing to the restriction on resources of mobile devices, data sensed is normally uploaded to the cloud. Nonetheless, modern solutions lack an integrated approach to support varied applications suitably, whilst lowering the power consumed on the mobile device.

In this context, Fakoor, Raj, Nazi, Di Francesco and Das (2012) put forward an integrated framework to store; process and deliver sensed data to human-centric applications installed in the cloud. The integrated platform forms the backbone of a novel delivery model, namely, Mobile Application as a Service (MAaaS) that permits the development of human-centric applications covering many disciplines, along with mobile social networks and participatory sensing. It particularly addresses a case study of an emergency response system for raising alarm in the event of fire. The framework showed reduction in the energy consumed on the mobile devices by the way of a prototype test bed implementation, while satisfying the requirements of the application.

Additionally, Sharma and Ghose (2009) presented an integrated comprehensive security framework that provides security services for all services of sensor network. Additional components i.e. Intelligent Security Agent (ISA) to evaluate degree of security and cross layer interactions in many components like Intrusion Detection System, Trust Framework, Key Management scheme and Link layer communication protocol.

Sensors attached to the smart phones and smart buildings makes possible mobile sensing and users’ behavior modeling that opens the door for cutting-edge applications such as customized intelligent computing, activity prediction, behavior intervention and health monitoring. Privacy is a major hurdle in mobile sensing and behavioral modeling. The end users invariable have less trust in the processing and storing for their personal information in the public cloud. The framework presented here uses hybrid cloud to control the computation between the devices, public cloud and personal cloud. Personal cloud is used for storing the raw sensor data and users are given complete control over it physically. Analytical widgets (e.g., health monitor or marketing survey) can collect user-authorized data on approval only. We evidently show that with this approach, there is considerable reduction in the users’ anxiety and increase in the acceptance rate of the mobile sensing technology from 23% to 60% (Zhang, Wu, Zhu et al., 2013).

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