Collaborative scheduling in highly dynamic environments using error inference

Keyphrases

• Collaborative Mechanism 33%
• Collaborative Sensors 33%
• Coordinated Scheduling 100%
• Duty Cycle 33%
• Energy Conservation 33%
• Energy Consumption Reduction 33%
• Energy Saving 33%
• Environmental Monitoring 33%
• Error Tolerance 33%
• ESense 33%
• Existing Techniques 33%
• Fixed Probability 33%
• Highly Dynamic Environment 100%
• Lab Test 33%
• Minnesota 33%
• Monitoring Applications 33%
• Practical Deployment 33%
• Probability Bounds 33%
• Sensing Activity 33%
• Sensing Error 100%
• Sensing Probability 33%
• Sensing Scheme 33%
• Soil Temperature 33%
• System Failure 33%
• System Performance 33%
• Temperature Data 33%
• Temperature Shift 33%
• Tolerance Bounds 33%
• Wireless Sensor Network Application 33%
• Wisconsin 33%

Engineering

• Application Monitoring 33%
• Energy Conservation 33%
• Energy Constraint 33%
• Error Tolerance 33%
• Fits and Tolerances 33%
• Lab Test 33%
• Network Application 33%
• Nodes 100%
• Reduce Energy Consumption 33%
• Sensing Error 100%
• Simulation Result 33%
• Systems Performance 33%
• Temperature Data 33%
• Test Bench 33%
• Wireless Sensor Network 33%

Computer Science

• Dynamic Environment 100%
• Energy Consumption 50%
• Energy Saving 50%
• Error Tolerance 50%
• Monitoring Application 50%
• Network Application 50%
• Systems Performance 50%
• Temperature Data 50%
• Wireless Sensor Network 50%