Scope of the issue
The general availability of low cost sensors, short range radio technology and advances in wireless networking are enabling the wide spread use and deployment of sensors. Sensors are often deployed in so called smart systems where intelligent algorithms are employed to turn the data collected by the sensors into actionable insights, as well as to inform decision making. Sensors are deployed today in a wide variety of application areas for taking all sorts of measurements. Examples include environmental sensors that provide real-time measurements of weather, air quality and other environmental parameters; wearable sensors that provide measurements of body movement, posture and general well-being; as well as industrial sensors used in predictive maintenance to measure the condition of industrial machinery.
Traditional sensor-based systems are convenient for static world-models. However, the real-world is far from static and is often characterised by variety and dynamism. For example, wearable sensors for recognising human activity are typically trained on a predefined, closed set of activity classes e.g. sitting, running, and walking. However, natural human activities are much more varied than these predefined set. Thus, for a human activity recognition system to have full utility in a natural setting, it should be able to recognise new activity types as it encounters them. Another important application of evolving sensor systems is intelligent and autonomous driving. Due to the variety in road circumstances and terrain, intelligent vehicles often need to be trained from live driving data. Thus, the sensors in such systems take in real-time data in order to update the knowledge model to handle new situations.
Evolving systems are inspired by the idea of system models that change and adapt in a dynamic environment. The aim of evolving systems is life-long learning and self-reorganisation in order to adapt to unknown and unpredictable environments through gradual change, system structure evolution and parameter adaptation. An important consideration is the ability of such systems to balance learning and change, while respecting past accumulated knowledge.
Aims and Objectives
The main aim of this issues is to bring together original research on advances in evolving sensor systems.
These are intelligent systems that can refine or evolve their knowledge, prediction models and reasoning in the context of sensor data and/or architectures.
Topics will include but are not limited to:
- Novel/Interdisciplinary Ideas
- Applications of Sensor Systems (e.g. Medical, Environmental, Transport)
- Wearable Sensors
- Context Awareness, Situation Awareness, Ambient Intelligence
- Evolutionary Systems
- Bayesian Systems
- Neural Networks and Deep Learners
- Machine Learning
- Knowledge Refinement
- Transfer Learning
- Submission deadline: 31st Mar, 2017
- Notification: 31st March, 2017
- Revised version: 15th April 2017
- Final notification: 15th May 2017
- Publication: June 2017
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Please use the special issue ‘Evolving Sensor Systems’ from the drop-down menu for your submission.