Mobile and ubiquitous computing

Pervasive computing embeds wireless communication and computation into material objects, locations and living entities, thus bringing together the physical and the digital into a single information ecology. We explore how such ubiquitous and tangible technologies can be exploited for applications in learning, particularly in their potential to enhance learning experiences through physical, kinaesthetic engagement with digital technologies and digital representations. We also investigate how highly detailed data about the physical and the built environment, captured through the sensing capability of pervasive computing, can support novel learning activities developed around fine-grained observation of our surroundings.

An Interoperable and Cognitive Wireless Sensor Network
Friday, 13 August 2010



Peng Du

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George Roussos

Giovanna Di Marzo Serugendo


Project Details

PhD research

Started Oct 2009

Expected to finish in 2012


 Wireless Sensor Network, Interoperable, Cognitive, 6LoWPAN




The vision of pervasive computing is increasingly becoming a reality due to rapid advances in the fields of computer science and manufacturing.

A wireless sensor network (WSN) is an important infrastructure of this paradigm as it interfaces with the physical world. Implemented using considerable numbers of small sensor devices (motes), WSNs are able to capture environmental parameters upon which computer systems then perform processing and trigger appropriate actions.

Research Questions

Because WSNs are inherently power-constrained, WSN protocols have been evolving separately from the mainstream IP technology. Consequently "translation" mechanisms have to be in place for them to interoperate. This both introduces computational complexity and also in some cases compromises performance.

Another issue needs further research involves the increasingly crowded spectral space, especially the 2.4GHz band where technologies like WSN and WIFI operate. Interference can potentially disrupt their functions and needs to be avoided.


 Following from the above, this project is aiming to provide:

  • An IP-based WSN model:  The project adopts IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN), an IPv6 dialect, as the convergence platform for different protocols so that any particular WSN protocol does not have to understand all the others so long as they can all be mapped to IPv6. As a result, the whole network, from the users' viewpoint, is based on IP and all communications are performed in a unified manner.
  • Cognitive radio (CR) functions in WSNs: By allowing WSNs to sense the spectral utilisation and availability, frequency overlaps are detected. WSNs can then dynamically make adaptations to their parameters to avoid or minimise interferences.
  • A more efficient data forwarding scheme for sensor motes: Because of the enhancements introduced by the above two features, both the size and the journey of packets are likely to grow. Such a mechanism is consequently necessary to keep the energy profile sustainable and also to facilitate the exchange of spectrum information for CR functions.


The project applies to both scientific areas and civilian purposes. For example, data collected at an animal habitat can be efficiently exchanged over the Internet among devices supporting IPv6 regardless of the physical layer (PHY) and medium access control (MAC) protocols. And the fact that each mote has its own IP address enables fine granularity, which allows researchers to examine a specific mote of interest without being swamped with unwanted data. For intelligent homes, as another example, cognitive WSNs can avoid or minimise interferences with other devices such as the baby monitoring system.





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