Model-based design for self-sustainable sensor nodes

Mayer, Philipp; Magno, Michele; Benini, Luca

doi:10.1016/j.enconman.2022.116335

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Previous works have demonstrated the key role of energy-aware algorithms in achieving self-sustainability [17].…”

Section: Related Workmentioning

confidence: 99%

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Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices

Giordano,

Cortesi,

Mekikis

et al. 2023

Proceedings of the 11th International Workshop on Energy Harvesting &Amp; Energy-Neutral Sensing Systems

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In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This paper presents an energy-aware adaptive sampling rate algorithm designed for embedded deployment in resource-constrained, battery-powered IoT devices. The algorithm, based on a finite state machine (FSM) and inspired by Transmission Control Protocol (TCP) Reno's additive increase and multiplicative decrease, maximizes sensor sampling rates, ensuring power self-sustainability without risking battery depletion. Moreover, we characterized our solar cell with data acquired over 48 days and used the model created to obtain energy data from an open-source world-wide dataset.To validate our approach, we introduce the EcoTrack device, a versatile device with global navigation satellite system (GNSS) capabilities and Long-Term Evolution Machine Type Communication (LTE-M) connectivity, supporting MQTT protocol for cloud data relay. This multi-purpose device can be used, for instance, as a health and safety wearable, remote hazard monitoring system, or as a global asset tracker.The results, validated on data from three different European cities, show that the proposed algorithm enables self-sustainability while maximizing sampled locations per day. In experiments conducted with a 3000 mAh battery capacity, the algorithm consistently maintained a minimum of 24 localizations per day and achieved peaks of up to 3000.

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“…Previous works have demonstrated the key role of energy-aware algorithms in achieving self-sustainability [17].…”

Section: Related Workmentioning

confidence: 99%

“…Smart energy-aware algorithms can contribute to ensuring system operation is adapted according to the available energy [4,17,29]. Literature provides several potential solutions in this field, such as dynamic voltage and frequency scaling (DVFS), energy-aware scheduling algorithms [28], and wireless sensor networks protocols [21].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices

Giordano,

Cortesi,

Mekikis

et al. 2023

Proceedings of the 11th International Workshop on Energy Harvesting &Amp; Energy-Neutral Sensing Systems

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“…[8], [9] Other considerable challenges arise from the variability of environmental energy sources in real-world deployment scenarios, further complicated by the demand for resource-efficient and reliable transducers. [10] Depending on the application, these challenges can also affect thermoelectric EH, where the typically low-voltage signals require careful signal conditioning, conversion efficiency optimization, and thermal-electrical matching. [11]- [13] Beyond the essential design and optimization of the circuitry itself, this emphasizes the importance of a meticulous analysis of application-specific energy intake and power consumption statistics to guarantee sustained long-term availablility.…”

mentioning

confidence: 99%

From Heat to Power: Assessing Thermoelectric Energy Harvesting for Self-sustainable Sensors

Lenz,

Vostrikov,

Mayer

et al. 2023

2023 IEEE International Workshop on Technologies for Defense and Security (TechDefense)

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Autonomy and self-sustainability are essential for the next generation of Internet of Things (IoT) and sensing devices. Harnessing electrical energy from temperature gradients, thermoelectric generators (TEGs) have great potential to power these devices for extended time periods. Analyzing the performance of transducers and converters is crucial for designing compact, low-power systems. This work highlights the importance of a comprehensive experimental evaluation of advanced low-voltage, high-efficiency conversion architectures in conjunction with TEGs. Characterization of multiple transducers and converters demonstrates the potential and limitations of thermoelectric energy harvesting (EH), showcasing the possibility of supplying a power of 0.65 µW/cm 2 harvested from a temperature gradient of only 1 K and 150 µW/cm 2 for 10 K. Conversion efficiencies of 75 % are achievable despite high voltage conversion ratios of 1:60.

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