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en:iot-reloaded:green_iot_energy-efficient_design_and_mechanisms [2025/01/07 20:16] – [Green computing] pczekalski | en:iot-reloaded:green_iot_energy-efficient_design_and_mechanisms [2025/05/13 15:23] (current) – pczekalski | ||
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====== Green IoT Energy-Efficient Design and Mechanisms ====== | ====== Green IoT Energy-Efficient Design and Mechanisms ====== | ||
- | As IoT is adopted to address problems in the various sectors of society or economy, the energy demand for IoT is increasing rapidly and almost | + | As IoT is adopted to address problems in the various sectors of society or economy, the energy demand for IoT is increasing rapidly and is following an exponential trend. As the number of IoT devices increases, the amount of traffic created by IoT devices increases, increasing the energy demand of the core networks that are used to transport the IoT traffic and also increasing the energy demand of data centres that are used to analyse the massive amounts of data collected by the IoT devices. The large-scale adoption and deployment of IoT infrastructure and services in the various sectors of the economy will significantly increase the energy demand from the IoT cyber-physical infrastructure (sensor and actuator devices) through the transport network infrastructure and the cloud computing data centre infrastructure. Therefore, one of the design goals of Green IoT is to develop effective strategies to reduce energy consumption. These strategies should be deployed across the IoT architecture stacks. That is, the energy-saving strategy should be implemented across all the IoT layers, including: |
- | *The perception or " | + | *The perception or " |
*The network or transport layer: Consists of the network (access and internet core network) infrastructure that is used to transport the data collected by the sensors to fog or cloud computing platforms and the feedback or commands from the fog or cloud computing platforms to manipulate actuation that controls cyber-physical systems at the perception or things layer. | *The network or transport layer: Consists of the network (access and internet core network) infrastructure that is used to transport the data collected by the sensors to fog or cloud computing platforms and the feedback or commands from the fog or cloud computing platforms to manipulate actuation that controls cyber-physical systems at the perception or things layer. | ||
*The Application layer: This layer processes (analyses) and stores the data collected by the IoT sensor devices, which are transported to the data centres through the transport layer. The computation results can be made available to users through applications or sent back to the things layer to manipulate actuators. | *The Application layer: This layer processes (analyses) and stores the data collected by the IoT sensor devices, which are transported to the data centres through the transport layer. The computation results can be made available to users through applications or sent back to the things layer to manipulate actuators. | ||
*The energy and sustainability management layer: It is an abstract layer that spans all three of the above layers, as energy efficiency and sustainability management are implemented across them. | *The energy and sustainability management layer: It is an abstract layer that spans all three of the above layers, as energy efficiency and sustainability management are implemented across them. | ||
- | At each layer, various energy-efficient strategies are implemented to reduce energy consumption. Much energy is used to perform computation and communicate at the multiple layers. A significant amount of energy is saved by deploying energy-efficient computing mechanisms (hardware and software), low-power communication and networking protocols, and energy-efficient architectures. Energy efficiency should be one of the main goals of green IoT systems: design, manufacturing, | + | At each layer, various energy-efficient strategies are implemented to reduce energy consumption. Much energy is used to perform computation and communicate at multiple layers. A significant amount of energy is saved by deploying energy-efficient computing mechanisms (hardware and software), low-power communication and networking protocols, and energy-efficient architectures. Energy efficiency should be one of the main goals of Green IoT systems: design, manufacturing, |
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<figure IoTDCES3> | <figure IoTDCES3> | ||
- | {{ : | + | {{ : |
< | < | ||
</ | </ | ||
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*Turning off idle devices. | *Turning off idle devices. | ||
*Energy-efficient manufacturing. | *Energy-efficient manufacturing. | ||
- | To achieve the green IoT vision, deploying energy-efficient hardware in the entire IoT infrastructure (from the perception layer to the cloud) throughout the IoT industry is essential. Green IoT hardware is not limited to energy-efficient hardware design and hardware-based energy-saving mechanisms in the IoT infrastructure but also includes sustainable hardware approaches such as: | + | To achieve the Green IoT vision, deploying energy-efficient hardware in the entire IoT infrastructure (from the perception layer to the cloud) throughout the IoT industry is essential. Green IoT hardware is not limited to energy-efficient hardware design and hardware-based energy-saving mechanisms in the IoT infrastructure, but also includes sustainable hardware approaches such as: |
- | *Using | + | *Using |
*Incorporating energy harvesting systems into IoT systems or infrastructure. | *Incorporating energy harvesting systems into IoT systems or infrastructure. | ||
- | **Reducing the size of hardware device** | + | **Reducing the size of a hardware device** |
There has been a significant reduction in the size of electronic hardware from the times of the vacuum tube to modern-day semiconductor chips. In the early days of electronics, | There has been a significant reduction in the size of electronic hardware from the times of the vacuum tube to modern-day semiconductor chips. In the early days of electronics, | ||
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Over the past few decades, the sizes of computing and communication devices have decreased significantly, | Over the past few decades, the sizes of computing and communication devices have decreased significantly, | ||
- | One of the Co-founders of Intel, Gordon Moore, observed that "the number of transistors and resistors on a chip doubles every 24 months", | + | One of the Co-founders of Intel, Gordon Moore, observed that "the number of transistors and resistors on a chip doubles every 24 months", |
In some energy-hungry IoT devices, batteries with higher energy capacity are required. The energy capacity of a battery is correlated with its size. That is, batteries with higher energy capacities may be larger and heavier, limiting the extent to which the device' | In some energy-hungry IoT devices, batteries with higher energy capacity are required. The energy capacity of a battery is correlated with its size. That is, batteries with higher energy capacities may be larger and heavier, limiting the extent to which the device' | ||
- | Another approach to keep decreasing the sizes of IoT devices and possibly reduce energy consumption is to integrate the entire electronics of an IoT device, computer or network node into a single Integrated Circuit (IC) called a System on a Chip (SoC) ((Anysilicon, | + | Another approach to keep decreasing the sizes of IoT devices and possibly reduce energy consumption is to integrate the entire electronics of an IoT device, computer or network node into a single Integrated Circuit (IC) called a System on a Chip (SoC) ((Anysilicon, |
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The increasing proliferation of IoT devices in almost every sector or industry in developing and developed economies has increased the amount of data collected from the environment, | The increasing proliferation of IoT devices in almost every sector or industry in developing and developed economies has increased the amount of data collected from the environment, | ||
- | Green computing strategies can be implemented in software or hardware. Some of the hardware-based green computing strategies have been discussed above in the section on Green IoT hardware. The software strategies will be addressed in the Green IoT software section below. Hardware acceleration is a primary green computing strategy that improves | + | Green computing strategies can be implemented in software or hardware. Some of the hardware-based green computing strategies have been discussed above in the section on Green IoT hardware. The software strategies will be addressed in the Green IoT software section below. Hardware acceleration is a primary green computing strategy that improves performance and energy efficiency. Hardware accelerators such as GPUs and Data Processing Units (DPUs) are major green computing drivers because they provide high-performance and energy-efficient computing for AI, networking, cybersecurity, |
+ | |||
+ | Green software goes back to the beginning of the computer era in terms of code efficiency and compactness. For example, it uses assembler and C/C++ code that is far more efficient in terms of performance and memory compared to modern high-level programming languages such as Python or Java. It also emphasises the importance of proper software-based energy management, such as asynchronous routines, use of interrupts, and sleep modes. | ||
+ | |||
+ | Recent developments in AI models and edge and fog computing enable the use of lightweight AI models in the fog and edge class of devices commonly powered by green energy sources. | ||
Green computing is not only about devising strategies to reduce energy consumption. It also includes leveraging high-performance computing resources to tackle climate-related challenges. For example, GPUs and DPUs are used to run climate models (e.g., predict climate and weather patterns) and develop other green technologies (e.g., energy-efficient fertiliser production, development of battery technologies, | Green computing is not only about devising strategies to reduce energy consumption. It also includes leveraging high-performance computing resources to tackle climate-related challenges. For example, GPUs and DPUs are used to run climate models (e.g., predict climate and weather patterns) and develop other green technologies (e.g., energy-efficient fertiliser production, development of battery technologies, | ||
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===== Green IoT Software ===== | ===== Green IoT Software ===== | ||
- | Optimised software plays a critical role in reducing the energy footprint of IoT systems: | + | Optimised software plays a critical role in reducing the energy footprint of IoT systems. Besides computing considerations presented in the chapter above, the following approaches are efficient: |
* Energy-aware algorithms: Algorithms that minimise computational complexity reduce CPU cycles and energy usage. | * Energy-aware algorithms: Algorithms that minimise computational complexity reduce CPU cycles and energy usage. | ||
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A similar trend can be observed in the PC industry, although there is no guarantee that more advanced chip manufacturing processes will continue to improve chip performance and energy efficiency. Designing energy-efficient chips for 5G/6G base stations is crucial to meet the growing demands of high-speed communication while minimising energy consumption and environmental impact. These chips are engineered with advanced semiconductor technologies to reduce power consumption and improve energy efficiency. They integrate specialised hardware accelerators for signal processing and AI-driven resource management to optimise network performance dynamically. Power-saving techniques like dynamic voltage and frequency scaling (DVFS) are also employed to adapt energy usage based on real-time load. | A similar trend can be observed in the PC industry, although there is no guarantee that more advanced chip manufacturing processes will continue to improve chip performance and energy efficiency. Designing energy-efficient chips for 5G/6G base stations is crucial to meet the growing demands of high-speed communication while minimising energy consumption and environmental impact. These chips are engineered with advanced semiconductor technologies to reduce power consumption and improve energy efficiency. They integrate specialised hardware accelerators for signal processing and AI-driven resource management to optimise network performance dynamically. Power-saving techniques like dynamic voltage and frequency scaling (DVFS) are also employed to adapt energy usage based on real-time load. | ||
- | ===== Green IoT policies | + | ===== Green IoT Policies |
Regulatory frameworks and corporate policies play a foundational role in driving energy-efficient IoT adoption: | Regulatory frameworks and corporate policies play a foundational role in driving energy-efficient IoT adoption: |