Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

Corral-García, Javier; Lemus-Prieto, Felipe; Pérez-Toledano, José-Luis González-Sánchez and Miguel-Ángel

doi:10.3390/electronics8101192

Cited by 5 publications

(3 citation statements)

References 29 publications

(40 reference statements)

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“…Two parameters are evaluated, which were Efficiency and Security. For efficiency, the raspberry 3B+ researcher decides 800 mA as default power usage [31]. Raspberry power usage with Rasefiberry hardware is connected to USB, CPU usage over 90 percent, and running openHAB as the IoT.…”

Section: Resultsmentioning

confidence: 99%

Rasefiberry: Secure and efficient Raspberry-Pi based gateway for smarthome IoT architecture

Simadiputra

Surantha

2021

Bulletin EEI

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Internet-of-Things or IoT technology becomes essential in everyday lives. The risk of security and privacy towards IoT devices, especially smarthomes IoT gateway device, becoming apparent as IoT technology progressed. The need for affordable, secure smarthome gateway device or router that smarthome user prefer. The problem of low-performance smarthome gateways was running security programs on top of smarthome gateway programs. This problem motivates the researcher designing a secure and efficient smarthome gateway using Raspberry Pi hardware as an affordable smarthome gateway device and able to run both smarthome gateways and security programs. In this research, researchers implemented snort as intrusion detection system (IDS), openHab as IoT gateway applications, and well-known encryption algorithms for file encryption in Raspberry PI 3B+ model. The researcher evaluated Snort capability on network attacks and compared each of the well-known encryption algorithm efficiency. From the result, we found Rasefiberry customized snort configuration for Raspberry pi 60 percent of the simulated network attacks. Twofish encryption algorithms were found to have best encryption time, throughput, and power consumption compared to other encryption algorithms in the research. Rasefiberry architecture successfully processes both lightweight security programs and Openhab smarthome gateway programs with a lowperformance computing device such as Raspberry Pi.

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Section: Resultsmentioning

confidence: 99%

Rasefiberry: Secure and efficient Raspberry-Pi based gateway for smarthome IoT architecture

Simadiputra

Surantha

2021

Bulletin EEI

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“…Furthermore, the rising complexity of IoT data transmission, storage, and processing capabilities requires more power-intensive tasks. In this sense, the capacity of IoT devices to operate for long periods without recharging is a significant challenge [96], [98], [99]. Finally, the rise of IoT and the emergence of LPWAN IoT technologies, e.g., in traffic monitoring, PA and animal health monitoring, posed an additional challenge to power efficiency in long-range transmission situations [96], [100].…”

Section: A Power Consumption (Reliable and Permanent Power Source)mentioning

confidence: 99%

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

et al. 2022

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The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.

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“…Previous studies have shown that 40% of energy is consumed in buildings, including homes, offices, and hospitals [1]. These days, smart buildings are equipped with RnEs as a power supply that is controlled via an energy management system (EMS).…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

2023

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These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

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Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

Cited by 5 publications

References 29 publications

Rasefiberry: Secure and efficient Raspberry-Pi based gateway for smarthome IoT architecture

Rasefiberry: Secure and efficient Raspberry-Pi based gateway for smarthome IoT architecture

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

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