Sensor Network Scheduling for Power Reduction
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Solution Overview
Problem
Existing sensor networks in infrastructure systems face challenges in managing power consumption efficiently, as wireless communication networks are dynamic and can lead to increased power usage due to congestion and interference, compromising the energy efficiency of transmission nodes.
Innovation Solution
A method and system that reschedule transmit schemes for network-connected measurement units based on wireless network parameters, such as signal quality and power consumption, to minimize energy usage by determining optimal transmission time slots using statistical analysis and cost-benefit analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network-level load balancing is implemented to reduce congestion, then network bandwidth and efficiency are improved, but power consumption at transmission nodes increases due to interference
Solution Approach 1:
The system performs preliminary actions by predicting future network conditions and proactively scheduling transmissions during optimal time slots before interference occurs. The network controller analyzes historical traffic patterns and predicts congestion periods, then schedules sensor transmissions during predicted low-interference periods, preventing power waste rather than reacting to it.
Solution Approach 2:
The system implements feedback mechanisms where the network controller continuously monitors actual network conditions, compares them with predicted conditions, and adjusts transmission schedules accordingly. Sensors provide feedback on their power consumption and transmission success, allowing the system to refine its predictions and scheduling decisions to minimize power usage while maintaining network efficiency.
2Reliability
If sensors transmit data continuously to ensure real-time monitoring, then measurement reliability is improved, but power consumption increases
Solution Approach 1:
The system applies dynamics by making transmission schedules adaptive rather than static. Transmission intervals and timing are dynamically adjusted based on predicted network conditions, sensor battery status, and data priority. High-priority sensors or those with sufficient power may transmit more frequently, while others use optimized intervals, allowing the system to balance reliability requirements with power conservation in real-time.
Solution Approach 2:
The system changes transmission parameters such as timing, frequency, and power level based on predicted optimal conditions. Instead of fixed transmission schedules, the network controller adjusts transmission parameters dynamically according to predicted network interference patterns and sensor power status, optimizing the balance between reliable data collection and power consumption.
3Reliability
If transmission power is increased to overcome network interference, then data transmission reliability is improved, but power consumption at the node increases
Solution Approach 1:
The system performs preliminary actions by predicting future network interference patterns and scheduling transmissions during predicted low-interference time slots. This proactive approach eliminates the need to increase transmission power to overcome interference, as transmissions are timed to occur when interference is naturally minimal, maintaining reliability while conserving power.
Data Source
AI summary
Provided herein a system and a method that may include the following steps: receiving data from one or more sensors configured to measure one or more predefined metrics of an infrastructure; establishing a wireless channel with a wireless network; obtaining time-varying characteristics of the wireless network; and determining a series of transmission time slots for transmitting the data received from the sensor, based on the time-varying characteristics, for reducing overall power consumption of the data transmission.