Sensors are everywhere, but keeping them powered sustainably is tricky. From smart algorithms to self-charging grids, here’s how engineers are making sensors smarter about energy.
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Sensors are embedded in everything from smart buildings and industrial machines to wearable medical devices. Like a digital nervous system, they constantly monitor, measure, and relay information that keeps modern technology running smoothly.
But there’s a catch: sensors consume energy. As they are integrated into more and more devices, their combined power demands risk undermining the very sustainability gains they promise. That challenge has set off a wave of research into making sensors more energy-aware and more energy-efficient, without compromising performance.
Researchers have developed a series of possibilities to meet this. From clever algorithms to energy harvesting techniques, a new generation of “smarter” sensors is being developed. These smarter devices know when and how to save energy, sometimes even contributing, rather than consuming energy in larger networks.
Core Principles of Sensor Power Management
One key idea is to structure sensor systems so energy use is distributed intelligently. A recent work published in MDPI Electronics, for example, outlines a four-layer framework for smart public buildings. These four layers divide operation into perception for sensing, network for data transmission, application for processing in the cloud, and cognition for data analytics. This structuring of different processes ensures that only the required energy is used, with less critical tasks drawing less power.
In their implementation, researchers used Arduino-based interfaces with voltage transformers carefully calibrated to keep measurement errors below 5 %. This method demonstrates that it is possible to achieve precision while also focusing on energy efficiency.1
Power cycling is a simpler method: when things aren't needed, this strategy turns them off, like switching off the lights when leaving a room. By calculating average power use over time, power cycling sensors reduce the duration of "on" states and shorten measurement phases.
For example, a Micro-Electro-Mechanical Systems (MEMS) tilt sensor achieved a significant reduction in average power consumption by minimizing the signal settling time and cutting the data acquisition phase to a single sample cycle.2
Machine Learning-Driven Optimization
Sensors are also being helped by artificial intelligence. By integrating AI algorithms, passive sensors have been transformed into intelligent systems capable of predictive energy management.
Long Short-Term Memory networks and Seasonal Autoregressive Integrated Moving Average (SARIMA) models have shown significant accuracy in forecasting energy demand patterns. Applying these algorithms to building management systems has resulted in a mean squared error (MSE) of 0.01 in consumption predictions in some studies. This accuracy means HVAC systems are able to pre-cool spaces during periods of lower energy rates, reducing their peak-load energy expenditure.1
Federated learning, which trains AI models across many devices without centralizing their data, adds another layer of efficiency. This method allows edge devices to collaboratively train models while keeping raw data private, helping to lower the energy costs associated with data transmission.
In healthcare, wearable networks powered by renewable energy have used deep learning combined with hidden Markov models to sense activity based on patient movement patterns. This distinction means the system only activates high-power sensors when sporadic or risky movement occurs, whilst staying in low-power standby the rest of the time. This balances the need for monitoring of vulnerable patients without expending excess energy.3
Communication Efficiency Innovations
Often, the biggest drain on a sensor’s battery isn’t sensing at all; it’s talking to the rest of the system. Data transmission typically consumes the most energy, but adaptive communication protocols are making a difference as they adjust how often and how much data is sent, depending on its urgency and the current state of the network.
A recent study published in MDPI Energies highlighted a smart meter system that employed Long Range Wide Area Network (LoRaWAN) and Narrowband Internet of Things (NB-IoT) technologies.4
In this system, transmission intervals were adjusted in real-time based on fluctuations in usage. This method led to an 86.81 % reduction in the number of transmitted packets and an 88.5 % decrease in energy consumption spikes. As a result, the battery life of the devices was extended, changing from a duration of months to years. This approach illustrates the potential for improving energy efficiency in sensor network applications.4
Edge computing further optimizes communication energy by processing data locally. Instead of continuously streaming raw sensor readings, on-device algorithms extract key features and transmit only the relevant information.
A campus microgrid implementation demonstrated that processing electrical quality metrics, such as voltage anomalies and harmonic distortions, at the sensor level significantly reduced the volume of data transmission compared to traditional cloud-based approaches. The system utilized multi-hop mesh networking, where intermediate nodes collected data from several sources.
This approach minimizes the need for long-range data transmissions, leading to more efficient communication and reduced energy consumption in the process.5
Energy Harvesting and Storage Integration
Then there’s the question of where the power comes from. Batteries alone are no longer enough. So, researchers are turning to energy harvesting technologies that draw power from the environment. Solar, thermal, and vibrational energy sources are being combined with hybrid storage, like solid-state supercapacitors, to keep sensors running reliably.
Wearable medical sensors, for example, now feature flexible solar cells that charge supercapacitors quickly and withstand more charge cycles than conventional batteries. These systems use maximum power point tracking algorithms, which help improve conversion efficiency and make the most of available energy resources.6
Power routing architectures mark a significant advancement in energy management, allowing sensors to operate within intelligent energy ecosystems. Some experimental systems even allow sensors to act as “prosumers,” both consuming and producing energy within their networks.
In one university campus microgrid, solar-powered sensors fed excess energy back into the grid during the day and drew from supercapacitors at night, creating a self-balancing ecosystem that reduced demand on the main grid. This method, published in MDPI Sensors, transformed the sensor network from a traditional energy sink into an active participant in the campus energy system, enhancing overall efficiency.5
Future Directions and Implementation Challenges
Probabilistic behaviour modeling is the next big thing in sensor efficiency. This idea means sensors can anticipate, rather than react, to energy demands. In one study of electric trucks, researchers introduced a regenerative braking probability metric that forecast how much energy could be recovered on a given route. This concept might also benefit sensor networks operating in predictable environments.7
By incorporating this metric into power management algorithms, researchers reported improvements in predicting state-of-charge levels. This study's approach may also work well in other settings where environmental conditions can be anticipated and used to recover energy.7
Quantum computing may offer another path forward. Simulations suggest quantum algorithms could find optimal balances between harvesting, storage, and use much faster than classical approaches, though the technology isn’t yet ready for practical use.
The challenges standing in the way predominantly involve managing the heat generated by quantum processors and developing fault-tolerant systems that can maintain accuracy in the face of environmental interference.8
Studies also indicate that redesigning sensor circuitry alongside control algorithms could lead to better energy savings. Additionally, gaps in standardization limit operation between power management systems from various manufacturers. Industry groups are now developing open communication protocols for energy coordination, which could help drive broader adoption in the coming years.9
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Conclusion
What’s clear is that energy efficiency doesn’t have to mean scaling back what sensors can do. Thanks to advances in algorithms, communications, and harvesting technologies, sensors are becoming more capable and more self-sufficient at the same time.
These advancements have important implications for large-scale IoT applications in smart cities and industries, enabling sustainable sensing infrastructures with minimal energy footprints.
References and Further Reading
- Starace, G. et al. (2022). Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach. Electronics, 11(23), 3904. DOI:10.3390/electronics11233904. https://www.mdpi.com/2079-9292/11/23/3904
- Power Cycling 101: Optimizing Energy Use in Advanced Sensor Products. Analog Devices. https://www.analog.com/en/resources/analog-dialogue/articles/optimizing-energy-use-in-advanced-sensors.html
- Rajawat, A. S. et al. (2022). Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements. Sensors, 22(15), 5654. DOI:10.3390/s22155654. https://www.mdpi.com/1424-8220/22/15/5654
- Alaa, K. et al. (2025). Optimizing IoT Energy Efficiency: Real-Time Adaptive Algorithms for Smart Meters with LoRaWAN and NB-IoT. Energies, 18(4), 987. DOI:10.3390/en18040987. https://www.mdpi.com/1996-1073/18/4/987
- Muqeet, H. A. et al. (2022). Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges. Sensors, 22(6), 2345. DOI:10.3390/s22062345. https://www.mdpi.com/1424-8220/22/6/2345
- Nordin, N. A. et al. (2021). Integrating Photovoltaic (PV) Solar Cells and Supercapacitors for Sustainable Energy Devices: A Review. Energies, 14(21), 7211. DOI:10.3390/en14217211. https://www.mdpi.com/1996-1073/14/21/7211
- Junhuathon, N. et al. (2025). Route-Based Optimization Methods for Energy Consumption Modeling of Electric Trucks. Energies, 18(8), 1986. DOI:10.3390/en18081986. https://www.mdpi.com/1996-1073/18/8/1986
- Rojek, I. et al. (2025). Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities. Energies, 18(2), 407. DOI:10.3390/en18020407. https://www.mdpi.com/1996-1073/18/2/407
- Rahimi, A., Gupta, R.K. (2021). Hardware/Software Codesign for Energy Efficiency and Robustness: From Error-Tolerant Computing to Approximate Computing. In: Henkel, J., Dutt, N. (eds) Dependable Embedded Systems. Embedded Systems. Springer, Cham. DOI:10.1007/978-3-030-52017-5_22. https://link.springer.com/chapter/10.1007/978-3-030-52017-5_22
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