Sensor Networks are of interest as a basic instance of a non-living collaborative intelligence network. Better understanding of collaborative intelligence is needed to drive innovation in a range of areas, including:

• Automated sensing activities
• Autonomous/Intelligent sensor networks to support social networks
• Autonomous/Intelligent social sensor networks
• Competing sensors or sensor networks
• Collaborating sensors and sensor networks
• Context awareness in sensor networks
• Data management in sensor networks
• Data and resource sharing in sensor networks
• Decision support systems for sensor networks
• Heterogeneous sensor networks
• Intelligent sensors and sensor networks
• Multi-service sensor networks
• New architectures and protocols for sensor networks
• New sensor network applications
• Quality of service in sensor networks
• Resource management in sensor networks
• Self-organization and self-adaptation in sensor networks
• Semantic-based management of sensor networks
• Sensor networks for autonomous/intelligent management of spatial resources
• Sensor networks for autonomous/intelligent risk management
• Sensor network control
• Sensor network maintenance
• Sensor networks for sustainable development
• Sensor networks on the web
• Sustainable sensor networks
• Virtual environment for supporting sensor networks

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Practical Applications.

The distant (or not so distant) future vision of a system to support collaborative intelligence will be inspired by life-like systems with autonomy and capacity for pattern recognition (the A-PR Hypothesis), enabling them to navigate. Life-like capacity for pattern recognition and choice may exhibit what Babbage alluded to as a violation, an unpredictability to which the system must be able to adapt, and intelligence to respond to random unpredictable elements, accepting and adapting usable random variations. Babbage, the computer visionary, may have seen beyond the possibilities of today’s algorithmic computing to an era of intelligent computing with the unpredictabilities of life. If so, he anticipated that collaborative intelligence will require the capacity to accept and respond to unpredictability.

Innovation (or Knowledge) Networks link participants, while maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes.

Problem mapping a priori, in contrast to information visualisation after-the-fact, generates visual frameworks, or “empty constructs” to structure the process of knowledge-gathering. Problem maps can evolve into navigable user interfaces. These open frameworks (partial patterns) tap the pattern recognition capabilities of users, serving as vehicles to order incoming information in process, and for use by participants during the problem-solving process. A classic example of a problem map is Dmitri Mendeleev’s Periodic Table of Elements, which prompted chemists to look for elements that appeared logically likely to exist, based upon the pattern of the Table.

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Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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