Green IoT: A Review and Future Research Directions
1.1. Background and Motivations
1.2. Related Works and Contributions
- A review of current research on the green IoT ecosystem, including recent industry developments and embedded systems, key areas of application, deployment, challenges, and key players focusing on RFID, WSN, processors, MCUs, and ICs.
- A discussion of key design choices and features for RFID and WSNs that are considered to be among the highest priorities within green IoT networks.
- An exploration of the limitations and challenges that need to be overcome to fully realize the potential of a green IoT, including issues related to interoperability, security, and privacy, and recommendations for future research to support the efforts in developing hardware for green IoT that promotes eco-sustainability.
1.3. Paper Organization
- RQ1: What are the energy-efficient techniques that can be used to implement massive green IoT networks?
- RQ2: How can energy consumption be reduced in IoT devices without compromising performance and functionality?
- RQ3: What are the key challenges in designing and implementing green IoT networks?
3. Green Hardware IoT Framework
- Machine-to-Machine (M2M): M2M technology refers to the ability of smart devices to communicate with each other and exchange data without the need for human intervention. It forms the basis of IoT and allows for the creation of smart systems that can operate and make decisions independently . One of the key applications of M2M technology is in the field of smart cities , where it can be used to optimize the performance of infrastructure such as traffic systems, public transportation, and utilities. M2M communication can also be utilized in industrial settings to improve efficiency and productivity, as well as in healthcare to enable remote monitoring and treatment of patients. In order to achieve energy efficiency in M2M systems, various strategies can be employed (as shown in Figure 3). These may include the use of low-power communication protocols, the implementation of energy-efficient protocols for data transmission, and energy-harvesting techniques to power M2M devices. Overall, M2M technology plays a crucial role in the advancement of the IoT and has the potential to substantially improve the efficiency and sustainability of a wide range of systems.
- Wireless Sensor Networks (WSNs): WSNs are networks of small, energy--efficient devices equipped with sensors that are capable of wirelessly communicating with each other and with a central hub. WSNs can be used to monitor and collect data from various environments and are commonly deployed in a broad range of applications, including industrial monitoring, healthcare, and environmental monitoring. In order to make WSNs more energy-efficient, various strategies can be employed. These may include the use of energy-efficient communication protocols, the implementation of energy-harvesting techniques to power the sensors, and the use of energy-efficient storage and data processing methods (as depicted in Figure 3). One promising research context in the field of WSNs is the use of machine learning algorithms to improve energy efficiency. These algorithms can be applied to streamline data transmission and lower the overall network energy usage. Overall, WSNs have the potential to greatly boost efficiency and sustainability and will likely continue to be an important area of research in the field of IoT .
- Radio-Frequency Identification (RFID): RFID is a wireless technology that identifies and tracks objects using radio waves. It consists of a small chip, known as an RFID tag, which is associated with an object and a reader that is able to detect and communicate with the tag. RFID technology has a wide range of applications, including inventory tracking, supply chain management, and asset management. The technologies for improving energy efficiency may include the use of low-power communication protocols, the implementation of energy-efficient data processing techniques, and the use of energy-harvesting methods to power RFID devices. One promising area of research in the field of RFID is the development of passive RFID tags, which do not require an energy source and can operate indefinitely as long as they are within range of an RFID reader .
- Microcontroller Units (MCUs) and Integrated Circuits (ICs): MCUs and ICs are key components of many electronic devices, including those used in the IoT. MCUs are small computers that are used to control and monitor the operation of a device, while ICs are electronic circuits that are used to process and transmit information. The strategies for enhancing the energy efficiency of MCUs and ICs may include the use of low-power design techniques, the implementation of energy-efficient data processing algorithms, and the incorporation of energy-harvesting methods to power the devices. One promising area of research in the field of MCUs and ICs is the use of ML algorithms to optimize their performance and reduce energy consumption . Another area of focus is the emergence of more energy-efficient materials and intelligent manufacturing processes for these devices.
4. Energy-Efficient M2M Communications
4.1. Energy-Efficient Data Transmission
- Data compression: Compressing data before transmission can reduce the amount of energy used to transmit it. Data compression techniques that are straightforward to use and have low computational requirements often use less energy . However, the balance between the amount of data that is compressed and the energy needed can be complex, as techniques that compress data more may require more energy . Table 1 provides a summary of data compression techniques, highlighting their respective advantages and disadvantages. Based on information from existing reviews or research in the field [22,23,24], these factors were determined by the authors of this article.
- ML algorithms: ML algorithms can be used to minimize data redundancy and optimize the rate of data transmission in multicell networks. The type of algorithm depends on the specific needs of the M2M application, including the amount and complexity of available data and the required accuracy. Testing different algorithms and evaluating their performance can help determine the most suitable one for a specific M2M application [31,32,33,34].
- Power control: By carefully adjusting the transmission power of M2M devices, it is possible to reduce the energy required for data transmission. There are several techniques that can be implemented to control power and improve energy efficiency performance in M2M data transmission, such as power-aware routing, adaptive modulation and coding, and duty cycling .
- Energy-efficient protocols: There are numerous communication protocols that have been designed specifically to improve the EE of data transmission in a green M2M network. However, each of these protocols has its own unique set of advantages and disadvantages, and the most suitable protocol can be chosen based on the specific requirements of the M2M application. Details of the transmission protocols and technologies are discussed in the following sections.
4.2. Power-Aware Scheduling
4.3. Offloading Computation/Task Offloading
- Edge-based offloading: In this type of offloading, computation-intensive tasks are transferred to a nearby edge device, such as a gateway or a fog node. This can be done over a local area network (LAN) or a wireless network.
- Fog-based offloading: A decentralized computing paradigm that gathers computing and data storage closer to the devices and users that need them, enabling more efficient and effective use of resources.
- Cloud-based offloading: In this type of offloading, computation-intensive tasks are transferred to a remote server or cloud computing infrastructure. This can be done over the internet or a wide area network (WAN).
- Data-intensive offloading: In this type of offloading, tasks that involve large amounts of data, such as data analysis and machine learning, are transferred to external resources.
- Computation-intensive offloading: In this type of offloading, tasks that require significant processing power, such as image processing and video encoding, are transferred to external resources.
4.4. Energy-Efficient Hardware
- Energy-efficient sensors (Green WSNs): Some sensors including, temperature and humidity sensors, are known to be very power-hungry devices. Therefore, using energy-efficient sensors is the best way to reduce the power consumption in M2M systems. Details are given in Section 5.
- Energy-efficient radios (Green RFID): Radio transceivers are a major source of power consumption in M2M devices. Thus, using an energy-efficient radio can improve the power consumption in M2M networks. Details are given in Section 5.
- Low-power microprocessors (Green MCUs and ICs): By using a low-power microprocessor, the overall power expenditure of an M2M device can be considerably reduced. Details are presented in Section 6.
5. Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
5.1. Radio Optimization Techniques
5.2. Sleep/Wake-Up Techniques
5.3. Energy Harvesting, Batteries, and Wireless Charging Techniques
5.3.1. Energy Harvesting Sources
5.3.2. Battery Technologies for IoT/Sensor Devices
5.3.3. Wireless Charging Techniques
- The non-directive RF radiation technique uses electromagnetic radiation in the radio frequency range between 300 GHz and 3 kHz to transmit electric energy. This method is well suited for far-field communication; however, it has low efficiency in converting RF energy to DC energy when the harvested RF power is low. For further details on this technique and other RF power transfer methods, readers can refer to [62,63].
- Magnetic resonance coupling involves the utilization of an evanescent field that generates and transmits electrical energy between two resonators . To create this type of resonator, a capacitance is inserted between an induction coil. An illustration of the operating concept is depicted in Figure 8.
5.4. Energy-Efficient WSN Architecture and Routing Protocols
- It decreases the communication distance within the cluster, which reduces the need for high transmission power.
- It reduces the number of transmissions by leveraging data fusion at the cluster head.
- It cuts down energy-consuming activities such as coordination and data aggregation by distributing them to the cluster head.
- It allows for some nodes to be powered off within the cluster, as the cluster head assumes forwarding responsibilities.
- It distributes energy consumption evenly among nodes by rotating the cluster head position.
5.5. Aggregation and Reduction of the Data
6. Energy-Efficient Radio-Frequency Identification
6.1. Passive RFID Systems
6.2. Active RFID Systems
- Batteries: Batteries have been a popular power source for various devices since their invention and are still widely used today. However, they have a short lifespan and must be replaced after a certain period. Even rechargeable batteries lose their energy-retaining capabilities over time. Another important factor to consider when choosing a storage device is the form factor, which refers to the shape and size of the device. However, the relationship between form factor and capacity is often conflicting, as a larger capacity often means a larger device size . As a result, batteries may not be the best option for RFID environments, where a small and flexible device is needed. Research has been conducted to find ways to develop compact energy storage devices that offer more flexibility while maintaining a high capacity. The emergence of thin film technology has made it possible for batteries to have the form factor needed for RFID applications . This technology allows for the creation of electronic devices that are paper-thin and recognized as well-matched for thermo-electric micro-systems. Studies have shown that thin-film batteries are an ideal choice for applications where a thermal difference is present, such as the human body . Overall, thin-film technology offers versatility and is a promising option for RFID tags.
- Capacitors: Capacitors are highly energy-efficient storage devices and do not diminish their capacity quickly, making them an ideal component for certain applications. However, the capacity of a capacitor is largely dependent on its size, and so capacitors often sacrifice form factors for a longer lifespan. Additionally, capacitors are unreliable to some extent because they are extremely susceptible to changes in current and voltage. However, recent advancements in capacitor storage technology have led to the development of ultra-capacitors and super-capacitors, which can store large amounts of energy and combine the reliability and performance of rechargeable batteries with the longevity of regular capacitors . These ultra-capacitors are selected by designers because they have high energy densities that are many orders of magnitude higher than those of conventional capacitors. Some designs deploy a combination of storage devices, creating a hybrid system that combines the best features of different devices. These models have been investigated and tested using solar-powered wireless sensors with promising results . The “Prometheus” model, for example, uses a dual-stage storage model in conjunction with a super-capacitor in the first stage and a lithium ion rechargeable battery in the second stage . This system is regarded as a buffer for powering a Berkeley Telos mote extracted from a PV solar panel system, and experimental results have shown that it can run for up to 43 years at a 1% load and up to 1 year at a 100% load.
7. Energy-Efficient Microcontroller Units and Integrated Circuits
8. Potential Future Directions
- Zero energy
- Routing schemes
- Adaptive AI and ML
- Intelligent sleep modes
- Wireless charging
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Huffman coding||Lossless data compression algorithm that encodes data using a variable-length prefix code based on character frequencies.|
|Lempel-Ziv-Welch (LZW) algorithm||Encodes data by identifying and replacing repeated patterns with references.|
|Run-length encoding (RLE)||A technique that reduces data size by replacing repeated characters with a single character and a count of their occurrences.|
|Delta encoding||Encodes data by representing the difference between successive values rather than the values themselves.|
|Predictive coding||A technique that predicts values and encodes the difference between the prediction and actual value.|
|Transform coding||Uses a mathematical transformation to convert the data into a different domain, where it can be more efficiently encoded.|
|Standard||LoRaWAN R1.0||IEEE 802.15.1||IEEE 802.15.4 (ZigBee)||2G-GSM, CDMA 3G-UMTS, CDMA2000|
|IEEE 802.16||IEEE 802.11 a/c/b/d/g/n|
|Energy consumption||Very Low||Bluetooth: |
Medium; BLE: Very Low
|Frequency band||868/900 MHz||2.4 GHz||868/915 MHz, 2.4 GHz||865 MHz–2.6 GHz||2–66 GHz||5–60 GHz|
|Data rate||0.3–50 Kb/s||1–24 Mb/s||40–250 Kb/s||200 kb/s–1 Gb/s||1 Mb/s–1 Gb/s (Fixed) |
50–100 Mb/s (mobile)
|1 Mb/s–6.75 Gb/s|
|Transmission range||<30 Km||8–10 m||10–20 m||Entire cellular area||<50 Km||20–100 m|
|Wireless Technology||Healthcare||Smart Cities||Smart Building||Automotive||Industry||Local Network (M2M)|
|Bluetooth (BLE)||very high||low||low||very low||very high||medium|
|WiMAX||low||very high||high||high||very high||high|
|Mobile communication||low||high||high||high||medium||very low|
|Solar panel||Wind generator||Thermoelectric||Electromagnetic|
|Power density of the indoor environment||100 µW/cm2||35 µW/cm2 @ wind speed < 1 m/s||100 µW/cm2 @ 5 °C||4 µW/cm3 @ human motion (Hz) 800 µW/cm3 @ machine (kHz)|
|Power density of the outdoor environment||10 mW/cm2||3.5 mW/cm2 @ wind speed ≤ 8.4 m/s||3.5 mW/cm2 @ 30 °C|
|Magnetic inductive coupling||Near-field.|
|Magnetic resonance coupling||Near-field.|
|Non-directive RF radiation||Far-field.|
|Energy-Efficient Technique||Energy Savings||Advantages||Disadvantages|
|Radio Optimization Techniques||Transmission power control: Dynamic adjustment of transmission power to minimize energy consumption [46,88,89].||Up to 50% compared to a constant transmission power approach.|
|Cooperation between wireless sensors: Collaborative data processing, routing, and sharing among neighboring sensors [89,90].||Up to 80% compared to independent sensor operation.|
|Spatial diversity: Involves using multiple antennas to transmit the same signal from different locations, which can reduce the transmission power required to achieve the same level of communication performance, leading to energy savings [91,92].||20–50%|
|Modulation optimization: Adjusts the modulation scheme based on the channel conditions and distance between nodes to reduce the energy consumption of transmissions while maintaining acceptable levels of communication performance [50,93].||Up to 60%|
|Sleep/Wake-up Techniques||Topology control: Adjusting the transmission range; nodes can avoid unnecessary communication and reduce energy consumption [90,94,95].||15–60%|
|Duty cycling: In which a sensor node is put into sleep mode for a certain period of time, known as the “duty cycle”. During this sleep period, the node does not transmit or receive data, and its radio is turned off. When the sleep period ends, the node wakes up and resumes its normal operations [92,96,97].||30–50%|
|Energy Harvesting Techniques [92,98,99].||Solar||70–90%|
|Architecture and Routing Protocols [100,101,102].||Cluster architecture||20–40%|
|Relay node placement||30–50%|
|Data Reduction Techniques [92,103,104].||Aggregation||20–80%|
|Number of cores||1||1||1||1||64||4|
|Data format||16-bit||32-bit||32-bit VLIW||32-bit VLIW||32-bit||32-bit|
|VDD range (V)||0.4 (1.0)||(0.4–1.2)||0.6–1.0||0.4–1.3||0.65–1.15||0.32–1.2|
|Max freq. (MHz)||25||82.5||331||2600||80||825|
|Power dens. (µW/MHz)||7.7||10.2||409||62||317||20.7|
|Best Perf. (MOPS)||25||57.5||662||2600||1600||3300|
|Energy eff. (MOPS/ mW)||64.5||68.6||4.5||16||3.9||193|
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Alsharif, M.H.; Jahid, A.; Kelechi, A.H.; Kannadasan, R. Green IoT: A Review and Future Research Directions. Symmetry 2023, 15, 757. https://doi.org/10.3390/sym15030757
Alsharif MH, Jahid A, Kelechi AH, Kannadasan R. Green IoT: A Review and Future Research Directions. Symmetry. 2023; 15(3):757. https://doi.org/10.3390/sym15030757Chicago/Turabian Style
Alsharif, Mohammed H., Abu Jahid, Anabi Hilary Kelechi, and Raju Kannadasan. 2023. "Green IoT: A Review and Future Research Directions" Symmetry 15, no. 3: 757. https://doi.org/10.3390/sym15030757