In this paper simple temporal compression algorithms' efficiency to reduce LoRa-based sensor node energy consumption has been evaluated and measured. It is known that radio transmission is the most energy consuming operation in a wireless sensor node. In this paper three lightweight compression algorithms are implemented in an embedded LoRa platform to compress sensor data in on-line mode and the overall energy consumption is measured. Energy consumption is compared to the situation without implementing any compression algorithm. The results show that a simple compression algorithm is an effective method to improve the battery powered sensor node lifetime. Despite the radio transmission's high energy consumption, the sleep current consumption is a significant factor for the device overall lifetime if the measurement interval is long.

Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO₂ emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO₂ emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO₂ emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO₂ emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO₂ emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO₂ emissions in the energy sector.

Purpose: Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node. Design/methodology/approach: In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured. Findings: Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods. Originality/value: This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

The escalation of the Internet of Things applications has put on display the different sensor data processing methods. The sensor data compression is one of the fundamental methods to reduce the amount of data needed to transmit from the sensor node which is often battery powered and operates wirelessly. Reducing the amount of data in wireless transmission is an effective way to reduce overall energy consumption in wireless sensor nodes. The methods presented and tested are suitable for constrained sensor nodes with limited computational power and limited energy resources. The methods presented are compared with each other using compression ratio and inherent latency. Latency is an important parameter in on-line applications. The improved variation of the linear regression-based method called RT-LRbTC is tested and it has proved to be a potential method to be used in a wireless sensor node with a fixed and predictable latency. The compression efficiency of the compression algorithms is tested with real measurement data sets.

This study presents three different versions of Do-It-Yourself (DIY) high frequency current probes made, tested and characterized with a Do-It-Yourself (DIY) calibration fixture. The calibration fixture was used to measure transfer impedances for current probes. Basic characteristics of current probes are discussed and limitations and possibilities of DIY current probes outlined.