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On-demand Generation of Location Specific Sensor Data For The Smarter Planet

IP.com Disclosure Number: IPCOM000209386D
Publication Date: 2011-Aug-02
Document File: 2 page(s) / 55K

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The IP.com Prior Art Database


The "Internet of Things" vision is that of a world where systems are instrumented using a vast number of sensors supplying up-to-date information about things people care about. These systems could include transport infrastructure, environment and energy infrastructure to name a few. By nature, these systems are present in most of the inhabited world and geographically spread across the planet. One of the challenges with implementing such systems is the sheer volume of data collected by sensors. A large bulk of this data collected has the potential to not be used or consumed by any end system or process that might typically take that data and turn it into a useful insight. There are potentially billions of sensors generating tens, hundred or thousands of readings a day.

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On-demand Generation of Location Specific Sensor Data For The Smarter Planet

Known solutions to this data problem include discarding unwanted information once its been received by the enterprise/server side of the system. This is very inefficient as energy, bandwidth and other resources are consumed in collecting and transmitting the unwanted data in the first place from each sensor. Other techniques such as reporting by exception reduce the amount of data collected. However these still do not go far enough.

    Crowd Sensing is increasingly popular. The idea is that people use their mobile phones and other environmental sensors (fixed or mobile) such as air quality sensors to collect and share information about the environment. One option is for everyone to collect and share everything when possible. However this is not a very efficient approach.

    This paper proposes an approach that allows data consumers to register an interest in receiving sensor data from a specific location or set of locations. Data producers (i.e. the sensors) only collect and deliver the information if there is an interest in the information. The environment and air quality in a specific area could be one example. A car driving through the middle of the Gobi Desert in Mongolia can spend resources collecting air quality information in its proximity. It could then use expensive bandwidth to transmit that data. However this would have been a wasted exercise if there is no interest, now or in the future, who was interested in this data. This is the area addressed by this approach proposed here.

    This approach involves giving the sensor (e.g. the car) the awareness of whether anyone in the world would like it to collect and transmit particular information.

    One embodiment of this approach could be based on the publish/subscribe messaging paradigm and the introduction of a location specific context to this approach.. The novelty of this idea is that it gives a publisher the (anonymous) awareness of the interest of one or more subscribers in it's location and/or area specific sensor data. Based on this information, the publisher does not unnecessarily collect or transmit sensor data thus behaving intelligently and reducing the amount of storage, processing, power and bandwidth.

    The approach could be implemented as follows. A subscriber would subscribe to topics of information with location parameters. For example a general carbon dioxide (CO2...