IoT Sensor Energy Management via Service-Instance Prediction
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Solution Overview
Problem
Existing IoT energy management systems face challenges in efficiently managing power consumption across different network domains, leading to potential service disruptions due to uneven battery life of sensors, especially in remote or hard-to-reach locations, and lack flexibility in combining service capabilities.
Innovation Solution
Implementing a service-instance oriented energy management system that predicts target service instances, selects an ON-sensor set based on critical covering sets and energy parameters, and performs ON/OFF control to optimize sensor usage, independent of MAC layer protocols and transparent to lower layer communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If energy management is performed at the access network level on individual sensors, then local power consumption is optimized, but service continuity deteriorates due to uneven battery life across sensors in different domains
Solution Approach 1:
The system segments service instances into different priority levels (high priority and low priority). High priority service instances maintain their sensors in active state to ensure service continuity, while low priority service instances allow sensors to enter sleep mode for energy savings. This segmentation resolves the contradiction by differentiating management strategies based on service importance.
Solution Approach 2:
The system dynamically changes the operational parameters of sensors based on service instance priorities and energy conditions. For high priority services, sensors maintain high activity parameters; for low priority services, sensors switch to low activity or sleep parameters. This parameter adjustment enables both energy optimization and service reliability maintenance.
2Reliability
If sensors are kept active to ensure service availability, then service quality is maintained, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor operational states based on real-time service instance priorities and energy conditions rather than using static configurations. Sensors can transition between active and sleep states depending on whether they are supporting high or low priority services, enabling adaptive optimization of both availability and power consumption.
Solution Approach 2:
The system implements feedback mechanisms where the energy management server continuously monitors service instance priorities, sensor energy levels, and operational states. Based on this feedback, the server dynamically adjusts sensor scheduling decisions to maintain service availability while optimizing power consumption, creating a closed-loop control system.
3Reliability
If manual battery replacement is performed for sensors in remote locations, then sensor functionality is restored, but operational complexity and time cost increase
Solution Approach 1:
The system performs preliminary actions by predicting future energy depletion of sensors based on their current energy levels and consumption patterns. Before batteries are completely depleted, the system proactively schedules sensors to enter sleep modes or adjusts their operational parameters to extend their functional life, preventing service disruptions and eliminating the need for urgent maintenance interventions.
Solution Approach 2:
The system enables sensors to self-manage their operational states based on energy conditions and service priorities. Sensors automatically transition between active and sleep states without requiring manual intervention, and the system dynamically reallocates sensor responsibilities to maintain service continuity, effectively making the network self-maintaining and eliminating the need for manual battery replacement in remote locations.
4Loss of energy
If service-instance oriented energy management is implemented, then long-term power savings are achieved, but system complexity increases due to prediction and coordination requirements
Solution Approach 1:
The system introduces an energy management server as an intermediary between sensors and service instances. This central coordinator handles the complex tasks of predicting service instance priorities, calculating optimal sensor scheduling, and managing energy allocation. By concentrating the computational complexity in a dedicated intermediary component, the system achieves long-term power savings while isolating the complexity from individual sensors and service implementations.
Data Source
AI summary
A system and a method for efficient service-instance oriented energy management in the IoT are provided. The method comprises: a predicting step for predicting target service instances to be serviced in a subsequent time period based on a service instance stance transition model; a selecting step for selecting an ON-sensor set to be turned on to provide services on which said target service instances are based, according to a critical covering set corresponding to the target service instances, the use history data of sensors in the critical covering set and energy parameters of said sensors; a controlling step for performing, when said time period begins, an ON/OFF control on the sensors in the IoT so that the sensors in the ON-sensor set are turned on and the sensors other than those in the ON-sensor set are turned off; and an updating step for updating the use history data of sensors according to the usage of sensors in said time period.