Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems.

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research.

Introduction: Embedded systems are constrained by limited flash memory, RAM (random access memory), and processing power, making efficient local data storage and transmission challenging. This study evaluates the suitability of several lossless compression algorithms for resource-constrained embedded platforms and assesses how data representation influences compression performance. Methods: LZW, Heatshrink, LZ4, FastLZ, and Miniz were evaluated directly on an STM32U575ZI microcontroller (Arm Cortex-M33, 160 MHz). A 104,067-byte sensor dataset comprising repetitive time-series measurements of soil and ambient temperature, humidity, battery voltage, timestamps, and identifiers was used as the test workload. The dataset was stored in three formats: CSV (comma-separated values) (104,067 bytes), binary (33,576 bytes), and bit-packed binary (26,581 bytes). Compression ratio, execution time, and total application RAM consumption, including input and output buffers, were measured. Results: Miniz achieved the highest compression efficiency, reducing the 33,576-byte binary dataset to 11,352 bytes (33.81% of the original size). The fastest compression was obtained with LZ4 configured with acceleration = 16, which compressed the bit-packed representation in 9.437 ms and produced a 25,692-byte output. Among configurations selected for minimum compressed output, FastLZ achieved the shortest execution time at 31.653 ms. When total RAM consumption was evaluated consistently, including all required buffers, LZ4 operating on the bit-packed representation required the least memory among the minimum-output configurations, consuming 58,496 bytes. Discussion: The results demonstrate significant trade-offs between compression ratio, execution speed, and memory requirements in embedded environments. Data representation substantially influences compression effectiveness, with binary and bit-packed formats generally improving efficiency over CSV. The findings provide practical guidance for selecting compression algorithms in resource-constrained embedded systems, where minimizing storage, execution time, or memory usage may be prioritized depending on application requirements.