Following two decades of intensive research, the field of wireless networked sensing has reached considerable maturity. Advances in wireless sensing and computing devices and the ubiquity of smart phones enable gathering of data about people and environments at low cost. Many challenges traditionally faced by Wireless Sensor Network (WSN) designers and implementers have been resolved and the availability of robust sensors and devices as well as adequate communication protocols have acted as enablers for the adoption of this technology in a variety of application areas.


However, the tasks of interpreting, visualising, and exploiting the flood of data streamed by WSNs are now emerging as timely research fields that call for input from a variety of engineering and computer science areas.

Great potential for innovation and development exists, for example, at the interface between WSN research and well-established domains such as Signal Processing, Machine Learning and Artificial Intelligence (AI). Several excellent examples of WSN systems embedding AI methods exist to date. However, few have been deployed and the potential of embedded intelligence is largely untapped in the context of WSN theoretical and practical works.


The core challenges and roadblocks to progress for this field are to do with the need for multidisciplinary support, the constrained nature of WSN node platforms (both in terms of computation and power) and the traditional perception of WSN systems as data gatherers rather than knowledge generators.

This session calls for contributions towards the realisation of tomorrow’s WSNs as end-to-end knowledge generation and decision making systems.

Topics of interest are listed below:

WSN systems case studies that embed some form of processing and intelligence to deliver informational and knowledge outputs in real-time or near-real-time
Theoretical and practical developments in bridging the data-to-information-to-knowledge gaps in end-to-end WSN systems
Embedding predictive capability in WSN systems through sophisticated processing
Use of AI methods for sensor placement, network optimization and other WSN related issues
Examples of field deployed wireless networked sensing systems with embedded decision making
WSN -- data interpretation and associated knowledge generation tools and processesInformation extraction and data mining methods applied to WSNs
Practical implementation examples, achievements and challenges in effectively deploying in-network data mining and data reduction techniques