Energy-Efficient Industrial Internet of Things in Green 6G Networks

Abstract

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

1. Introduction

1.1. The Background of the Study

Green artificial-intelligence-based renewable energy technologies (e.g., solar and geothermal energy, together with biomass, wind, and hydroelectric power) [1,2,3] shape clean circular economy practices [4,5] in the Internet of Robotic and Manufacturing Things and in cyber–physical production systems, together with Internet of Things (IoT)-sensing-network-based and deep-learning-assisted smart process planning and management [6], geospatial simulation tools, and environment mapping algorithms. Energy-harvesting technology and spectrum sensing [7,8] across cognitive-radio-based IoT networks [9] optimize resource allocation and data fusion [10] in vehicular networks [11] and unmanned aerial vehicles [10].

Energy-efficient techniques are pivotal in IoT-device- and fog-node-based storage and computation services [12] throughout 6G networks. Computation tasks can be carried out by use of fog and edge devices while optimizing energy efficiency, resource utilization, and power consumption. Autonomous connected vehicles and smart automated machines can leverage fog-computing-based 6G IoT network energy efficient techniques supporting pervasive connectivity in extended reality environments. Physical energy, software-based power networks, and distributing renewable resources [13] configure Internet of Energy cyber–physical systems by packaged power management tools. Data processing and intelligent measurement infrastructure, energy transmission lines, software network nodes, actual-time communicating technologies, power transmission and grids, and control systems develop on smart meters, distributed energy sources, and sensors. Flexible energy control, distributed network intelligence, and optimum power current can improve energy efficiency. System power effectiveness, reliability, and stability can be attained across energy consumption and production, data processing energy management and reduction, and supply distributed equipment by wireless communication technologies.

Energy sustainable networks can integrate energy aware resource and communicating node control in battery-powered connected devices and mobile terminals [14] across 6G green networks, reducing power consumption. Sixth-generation networks develop on green communication capabilities, efficient power control and signal processing, and energy-aware resource management algorithms. Sustainable green 6G IoT networks can cut down power consumption and energy overhead, while increasing coverage by intelligent communication capabilities. High throughput and energy efficiency, low latency, and intelligent traffic control, system transmission and distribution, and electricity generation [15] leverage 6G wireless network system connectivity of IoT device technologies in large-scale industrial production. Sixth-generation IoT communication and energy efficient and aware network systems can harness virtualization and mobile edge computing technologies in IoT wireless network scalability concerning operations and services. Performance is optimized by integrating spectrum sensing algorithms, sensor network fault detection, data transmission scheduling, and energy harvesting mechanisms.

Industrial 6G edge computing and distributed artificial intelligence technologies [16] can be leveraged for energy consumption reduction and sensor node clustering in automated equipment operations across multi-agent system-based industrial wireless sensor networks. Propagation neural and convolutional neural networks, data-mining-based clustering and collaborative perception technologies, and machine learning algorithms can configure mutual cluster correlation in individual node resource allocation for energy efficiency optimization and industrial 6G machine and equipment networking and automated operation for production efficiency based on network sensor node energy data and cooperative spectrum sensing. Cloud, edge, and fog computing technologies can bring about node energy consumption decrease by coherent wireless network resource allocation. Data mining and wireless communication technologies articulate energy resource-based network computing efficiency, resulting in traffic congestion mitigation in industrial production environments. Data fusion technology can assist network resource distribution for energy effectiveness optimization and network traffic congestion mitigation.

IoT-sensor-driven mobile devices and deep-learning-driven swarm-based edge cloud computing technologies [17] can shape energy and battery lifecycle management and monitoring for a sustainable environment by spectrum sharing capabilities, data transmission energy efficient mechanisms, and big data clustering and coordination, reducing energy consumption. IoT-driven mobile networked embedded systems across remote industrial automation integrate machine and deep-learning-based 6G radio resource allocation techniques and intelligent industrial processes, leading to energy consumption mitigation. Sixth-generation Internet of Everything (IoE) mobile ubiquitous intelligence and energy-efficient sustainable edge networks [18] can optimize interrelated edge node coordination by multi-agent deep reinforcement learning decentralized and collaborative computation offloading and resource distribution, resulting in decreased computing complexity and energy consumption.

Transmission resource scheduling, base station coordination, and edge computing nodes can be optimized by resource- and energy-efficient 6G wireless Industrial Internet of Things (IIoT) network operation automation [19] and integrated sensing techniques in Industry 4.0 cyber–physical production wirelessly connected multi-robotic systems. Deep convolutional neural networks, cloud and edge computing technologies, and signal processing algorithms can deploy knowledge and contextual data in wireless industrial environments. Digital twin AI sensing and imaging technologies for wireless environment modeling can enable integrated sensing and machine multiservice communication automation in smart factories. Wireless blockchain and mobile edge computing technologies can be leveraged in resource allocation development for sustainable communication and power effectiveness [20] for sensor node collaborative decision-making. Data transmission, power management, and network performance processes are pivotal in IoT reinforcement learning-based 6G wireless sensor networks for energy consumption efficiency and reliable connectivity. Fog computing and energy-aware routing techniques can improve energy use optimization in IoT heterogeneous device data transmission across automated manufacturing operations.

1.2. The Research Gap of the Study

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial IoT (IIoT) in terms of practical integration challenges and compatibility issues between existing industrial systems and emerging 6G technologies. Energy-optimized mobile and radio access networks [21] can reduce energy consumption and enhance sustainable green communication, power usage, and resource management across mobile devices. Sixth-generation cellular networks can deploy zero-energy cellular Internet of Things devices [22] using beamforming, wireless information and power transfer management, energy harvesting, and backscatter communication techniques. Internet-of-Everything-enabled 6G networks can improve energy efficiency and resource allocation [23] in terms of localization, coverage, convergence, connectivity, latency, and resilience.

We show how distributed AI green mobile network automation, spatial perception and sensor fusion algorithms, and dynamic spectrum sharing and energy harvesting technologies can be deployed in integrated wireless sensing, distributed computing, and mobile communication for environmental detection. Sixth-generation radio and spatial computing technologies, digital twin extended reality devices, and visual localization techniques can be leveraged in object recognition, 3D graphics rendering, and scene understanding across cloud virtualized networks. Real-time environmental, system-level semantic awareness, and contextual sensing, orthogonal multiple and spectrum access efficiency scaling, optimized network traffic and power management, and 6G precise positioning and mobile communication operations develop on edge computing and reconfigurable intelligent surface technologies, edge cloud processing, and federated reinforcement learning algorithms for cross-node machine-learning-based wireless system operation and contextual sensing performance.

We clarify that generative AI-native 6G end-to-end communication technologies and federated learning algorithms can optimize scalable network and device distributed inference, connected intelligent edge cloud processing distribution, and reconfigurable intelligent surfaces for sustainable power efficiency. Generative AI 6G mobile wireless communication and green-integrated high-resolution sensing technologies are pivotal in connected intelligent edge and efficiently distributed computing across energy-efficient network architectures, lowering power consumption.

Quantitative analysis and empirical evidence demonstrate the energy efficiency improvements or cost benefits of implementing IIoT in green 6G networks. Strategies and technologies can ensure secure and privacy-preserving communication in green 6G IIoT networks. Technical standards, protocols, and architectures are pivotal for the deployment of IIoT in green 6G environments. The environmental impact of deploying green 6G networks can support sustainable industrial practices at scale. The core contribution is by showing how green 6G artificial intelligence IoT industrial wireless and network-based energy efficient systems integrate deep- and machine-learning-based green communications across 6G IIoT environments. Green 6G multi-access edge computing and vehicle-to-everything technologies [24] can mitigate energy use and improve resource utility by machine learning clustering. Green IoT networks integrate wireless sensor and cloud computing technologies, big-data-driven metering infrastructure, data centers, enhanced signal bandwidth, and machine-to-machine communication [25] to decrease energy consumption across smart grids based on predictive analytics-based renewable energy integration and edge- and fog-computing-driven distributed intelligence. We also clarified how 6G network infrastructures develop on green IoT and cloud-based computing technologies. Power allocation and transmission algorithms can enhance 6G-enabled cyber–physical system–IoT industrial environments [26] using sensors and actuators for energy efficiency resource optimization and channel allocation. IoT devices across 6G networks, radio-frequency-based energy harvesting techniques, and green Industry 4.0 wireless systems and real-time data digitization, gathering, and analysis articulate low-carbon net zero [27] by battery-less wireless sensing and operation and autonomous low-power, energy-efficient circuits, mitigating maintenance costs.

Related previous research focused mainly on sustainable vehicular communications and UAV-based green Internet of Things [28,29,30], energy-efficient self-governing wireless networks [13,14,31,32,33,34,35,36,37,38], 6G Internet of Things in relation to big data and artificial intelligence technology [15,39,40,41,42,43,44,45], sustainable 6G IIoT network automation [46,47,48,49,50,51,52], IoT green smart cities [53,54,55,56,57,58,59,60,61], digital twin intelligent planning and IoT smart industry energy management [12,62,63,64,65,66,67].

1.3. The Objectives of the Study

Through large-scale device interconnections, 6G wireless systems can bring about massive quantities of traffic data, leading to serious spectrum scarcity and system latency [66]. Our objective is to show that deep- and machine-learning-based data compression technologies can result in efficient input transmission across edge–cloud collaborative 6G IIoT and smart manufacturing networks in production lines. Smart IIoT devices can generate big data, causing intense computing load and unforeseeable processing delays. Energy is consumed by wireless transmission, and sensors and end nodes do not have enough computing resources, storage space, energy, or spectrum capabilities. Mobile-enhanced digital twin technologies can simulate 6G edge computing green communication wireless network planning, performance, and deployment [65], reducing operational costs. Artificial intelligence algorithms can be leveraged for energy-efficient 6G network deployment in smart factories by self-evolution capabilities and cloud collaborative mechanisms through complex data handling.

This is the first systematic review covering energy-efficient Industrial Internet of Things in green 6G networks due to the relevance of deep-learning-based 6G data-driven network and green 6G multi-access edge computing technologies in cyber–physical system–IoT industrial environments by machine learning clustering, energy harvesting techniques, and power allocation and transmission algorithms for energy aware scheduling algorithm-based 6G green communication systems and deep-neural-network-based energy-efficient transmission optimization. Sixth-generation wireless IoT networks and communication technologies [68] can be leveraged for high transmission rates, effective resource optimization, and energy, spectrum, bandwidth utilization, and power efficiency. Deep-learning-based 6G data-driven network and wireless communication technologies [69] can increase speed and decrease latency, enhancing end-to-end behavior. One-shot federated learning optimizes green and sustainable data-driven IoT connectivity [70], curtailing resource utilization and environmental footprint, energy consumption, and latency by IoT device knowledge sharing and lifespan extension. IIoT wireless environments can integrate sensors, actuators, and controllers [71], configuring air-to-ground links and wireless channels by unmanned aerial vehicles (UAVs), green device-to-device communication techniques, channel allocation parameters, and reconfigurable intelligent surfaces (RISs) for deep-neural-network-based energy-efficient transmission optimization.

The manuscript comprises an outline of the methodology (Section 2), source correlation analysis (Section 3), distributed artificial intelligence green 6G pervasive edge computing communication networks in energy-efficient IoT smart industrial urban environments (Section 4), energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments (Section 5), sustainable energy efficiency and green power generation in distributed artificial intelligence 6G pervasive edge computing communication networks (Section 6), results (Section 7), discussion (Section 8), case study (Fujitsu data application simulations on energy-efficient Industrial Internet of Things in autonomous green 6G networks) (Section 9), conclusions (Section 10), theoretical contributions to the literature (Section 11), practical contributions to the literature (Section 12), and limitations and further directions of research (Section 13).

2. Methodology

The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated (Figure 1). A quantitative literature review was performed in July 2024 across ProQuest, Scopus, and the Web of Science with the search terms “energy-efficient” + “Industrial Internet of Things” + “green” + “6G networks” for published original and review research between 2019 and 2024, with 59 final sources selected for analysis (

Table 1. Topics and types of scientific products identified and selected.

Topic	Identified	Selected
energy-efficient + Industrial Internet of Things	142	21
energy-efficient + green	134	19
energy-efficient + 6G networks	128	19
Type of paper
Original research	329	45
Review	33	14
Conference proceedings	22	0
Book	11	0
Editorial	9	0

Source: Processed by the authors. Some topics overlap.

). We followed [72] with regard to the most-cited papers covering the debated topics (

Table 2. Top 20 most-cited papers covering the debated topics.

No.	Authors	Nationality	Paper Title	Journal Title	Paper Type	Number of WoS Citations	Ref.
1	Nguyen, D.C.; Ding, M.; Pathirana, P.N.; Seneviratne, A.; Li, J.; Niyato, D.; Dobre, O.; Poor, H. V.	Australia, China, Singapore, Canada, USA	6G Internet of Things: A Comprehensive Survey (2022)	IEEE Internet of Things Journal	Review	384	[48]
2	Huang, T.; Yang, W.; Wu, J.; Ma, J.; Zhang, X.; Zhang, D.	China	A Survey on Green 6G Network: Architecture and Technologies (2019)	IEEE Access	Review	241	[42]
3	Lu, Y.; Zheng, X.	USA	6G: A survey on technologies, scenarios, challenges, and the related issues (2020)	Journal of Industrial Information Integration	Review	163	[44]
4	Mao, B., Tang, F.; Kawamoto, Y.; Kato, N.	Japan	AI Models for Green Communications towards 6G (2022)	IEEE Communications Surveys & Tutorials	Original research	113	[56]
5	Zhen, L.; Bashir, A.K.; Yu, K.; Al-Otaibi, Y.D.; Foh, C.H.; Xiao, P.	China, UK, Japan, Saudi Arabia	Energy-Efficient Random Access for LEO Satellite-Assisted 6G Internet of Remote Things (2021)	IEEE Internet of Things Journal	Original research	109	[67]
6	Mao, B.; Kawamoto, Y.; Kato, N.	Japan	AI-Based Joint Optimization of QoS and Security for 6G Energy Harvesting Internet of Things (2020)	IEEE Internet of Things Journal	Original research	108	[45]
7	Alsamhi, S.H.; Afghah, F.; Sahal, R.; Hawbani, A.; Al-qaness, M.A.A.; Lee, B.; Guizani, M.	Ireland, Yemen, USA, China, Qatar	Green internet of things using UAVs in B5G networks: A review of applications and strategies (2021)	Ad Hoc Networks	Review	103	[28]
8	López, O.L.A.; Alves, H.; Souza, R.D.; Montejo-Sánchez, S.; Fernández, E.M.G.; Latva-Aho, M.	Finland, Brazil, Chile,	Massive Wireless Energy Transfer: Enabling Sustainable IoT toward 6G Era (2021)	IEEE Internet of Things Journal	Original research	91	[36]
9	Ghiasi, M.; Wang, Z.; Mehrandezh, M.; Jalilian, S.; Ghadimi, N.	Canada, Iran	Evolution of smart grids towards the Internet of energy: concept and essential components for deep decarbonization (2023)	IET Smart Grid	Original research	90	[13]
10	Malik, U.M., Javed, M.A.; Zeadally, S.; Islam, S. u.	Pakistan, USA	Energy-Efficient Fog Computing for 6G-Enabled Massive IoT: Recent Trends and Future Opportunities (2022)	IEEE Internet of Things Journal	Review	84	[12]
11	Verma, S.; Kaur, S.; Khan, M.A.; Sehdev, P.S.	India, Saudi Arabia, USA	Toward Green Communication in 6G-Enabled Massive Internet of Things (2021)	IEEE Internet of Things Journal	Original research	71	[60]
12	Wang, J., Zhu, K.; Hossain, E.	China, Canada	Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking (2022)	IEEE Transactions on Green Communications and Networking	Review	70	[29]
13	Wang, D., Zhong, D.; Souri, A.	China, Malaysia, Turkey	Energy management solutions in the Internet of Things applications: Technical analysis and new research directions (2021)	Cognitive Systems Research	Original research	46	[64]
14	He, P., Almasifar, N.; Mehbodniya, A.; Javaheri, D.; Webber, J. L.	China, Turkey, Kuwait, Republic of Korea	Towards green smart cities using Internet of Things and optimization algorithms: A systematic and bibliometric review (2022)	Sustainable Computing: Informatics and Systems	Review	44	[53]
15	Qadir, Z., Le, K.N.; Saeed, N.; Munawar, H.S.	Australia, Saudi Arabia	Towards 6G Internet of Things: Recent advances, use cases, and open challenges (2023)	ICT Express	Review	42	[57]
16	Li, J.; Dai, J.; Issakhov, A.; Almojil, S.F.; Souri, A.	China, Kazakhstan, Saudi Arabia, Turkey	Towards decision support systems for energy management in the smart industry and Internet of Things (2021)	Computers & Industrial Engineering	Original research	41	[62]
17	Chi, H.R.; Wu, C.K.; Huang, N.-F.; Tsang, K.-F.; Radwan, A.	Portugal, Hong Kong, Taiwan	A Survey of Network Automation for Industrial Internet-of-Things Toward Industry 5.0 (2023)	IEEE Transactions on Industrial Informatics	Review	40	[46]
18	Chen, N.; Okada, M.	Japan	Toward 6G Internet of Things and the Convergence with RoF System (2021)	IEEE Internet of Things Journal	Original research	39	[39]
19	Mahmood, M.R., Matin, M.A.; Sarigiannidis, P.; Goudos, S.K.	Bangladesh, Greece	A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT toward 6G Era (2022)	IEEE Access	Review	37	[15]
20	Hu, N., Tian, Z.; Du, X.; Guizani, M.	China, USA, Qatar	An Energy-Efficient In-Network Computing Paradigm for 6G (2021)	IEEE Transactions on Green Communications and Networking	Original research	30	[34]

), examples of experimental data and results (Table 3), and main topics covered by the authors in each group, research focus reasons, and the number of research groups having shared interests (Table 4, Table 5, Table 6 and Table 7).

Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software [9] harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis [73,74] included Abstrackr (for title and abstract organization and screening semi-automation to improve evidence synthesis efficiency by machine-learning-based text mining and record pattern recognition), DistillerSR (for swift evidence-based research production, artificial-intelligence-powered literature collection, screening, evaluation, transparency and reproducibility by automatic entry and decision tracking, and complex dataset duplicate detection and removal management), CADIMA (for evidence synthesis process efficiency, automated duplicate removal and record allocation, and study selection and critical appraisal management and conduct), Rayyan (for study screening and selection processes and citation organization and tracking), and SRDR+ (for study design and research question data extraction, management, and archiving as the evidence synthesis process flow). For data visualization and analysis by bibliometric mapping and layout algorithms, Dimensions and VOSviewer were deployed (Figure 2, Figure 3, Figure 4 and Figure 5).

Table 3. Examples of experimental data and results.

No.	Source	Applied Research
1	[39]	Sixth-generation IoT Radio-over-Fiber (RoF) system flexibility and efficiency across high-density heterogeneous devices by use of artificial intelligence (AI)-based smart algorithms
2	[41]	Air–space–ground–sea AI 6G integrated network green architecture, radio and traffic map network perception green solutions for efficient AI-based green fusion, and KubeEdge wireless green management platform for node containerization and wireless connection expansion to carry out joint scheduling and resource orchestration
3	[32]	Cost-efficient computation offloading (CeCO)—an industrial network computation offloading framework with network regulation master fog controller and fog device IIoT data distribution, and IIoT device weighted energy-delay cost sum optimization function design
4	[33]	Intelligent-algorithm-based ambient backscatter system for resource scheduling scheme and task offloading optimization to reduce energy consumption and latency, while constituting a federated learning and asynchronous advantage actor–critic algorithm-based Markov decision process
5	[34]	Sixth-generation energy-efficient in-network computing for network function integration in a general computing platform that serves as a network node and configures a unified hypervisor and container-based application task operating environment, significantly reducing data center energy consumption
6	[43]	Long short-term memory deep recurrent neural networks in next-generation 6G edge network design for green computation in terms of energy consumption joint optimization Markov decision process
7	[35]	A distributed federated learning (DBFL) framework for distant device connection in mobile edge computing architectures in a clustering-protocol-based distributed manner, overcoming energy efficiency issues
8	[54]	Green energy wireless charging algorithms can efficiently power IoT devices by multi-green base station joint accumulative charging schemes
9	[63]	A 5G-technology-based heterogeneous wireless network resource virtualization architecture for green computing with regard to IoT data classification
10	[45]	An extended Kalman filtering method for 6G IoT sensing devices can predict harvesting power
11	[37]	Blockchain, green ubiquitous IoT wireless spectrum sharing, and 6G mobile communication technology for energy efficiency
12	[14]	Energy-aware resource management, 6G-enabled cell-free massive MIMO networks, and green communication capabilities can provide optimized network coverage and power based on MIPA-MCAS algorithms
13	[59]	A 6G IoT confidence information coverage reinforcement learning node sleep scheduling algorithm and Q-learning collaborative intelligence can extend network lifetime, balance the energy consumption, and enhance resource efficiency with fewer active nodes
14	[60]	A hybrid whale spotted hyena optimization (HWSHO) algorithm for 6G-enabled massive IoT device green communication in terms of energy-efficient network cluster-based data dissemination
15	[75]	Deep reinforcement learning MIMO-NOMA IoT systems and Age of Information can shape optimal channel capacity, power allocation, spectrum efficiency, and energy consumption
16	[65]	Digital twin AI algorithms enable energy-efficient 6G network deployment in smart factories based on mobile-enhanced big data edge computing–cloud collaborative mechanisms for green communication and network performance optimization
17	[52]	Fifth-generation Industrial Internet of Things (IIoT) heterogeneous network intelligence-driven green energy-efficient resource allocation mechanisms based on deep reinforcement learning algorithms
18	[61]	Integrated relative energy efficiency can assess traffic profiles and wireless network capacities for green communications
19	[66]	Sixth-generation edge–cloud collaborative industrial-IoT-learning-based data compressions and transmissions for spectrum efficiency increase and system latency mitigation

Table 3. Examples of experimental data and results.

No.	Source	Applied Research
1	[39]	Sixth-generation IoT Radio-over-Fiber (RoF) system flexibility and efficiency across high-density heterogeneous devices by use of artificial intelligence (AI)-based smart algorithms
2	[41]	Air–space–ground–sea AI 6G integrated network green architecture, radio and traffic map network perception green solutions for efficient AI-based green fusion, and KubeEdge wireless green management platform for node containerization and wireless connection expansion to carry out joint scheduling and resource orchestration
3	[32]	Cost-efficient computation offloading (CeCO)—an industrial network computation offloading framework with network regulation master fog controller and fog device IIoT data distribution, and IIoT device weighted energy-delay cost sum optimization function design
4	[33]	Intelligent-algorithm-based ambient backscatter system for resource scheduling scheme and task offloading optimization to reduce energy consumption and latency, while constituting a federated learning and asynchronous advantage actor–critic algorithm-based Markov decision process
5	[34]	Sixth-generation energy-efficient in-network computing for network function integration in a general computing platform that serves as a network node and configures a unified hypervisor and container-based application task operating environment, significantly reducing data center energy consumption
6	[43]	Long short-term memory deep recurrent neural networks in next-generation 6G edge network design for green computation in terms of energy consumption joint optimization Markov decision process
7	[35]	A distributed federated learning (DBFL) framework for distant device connection in mobile edge computing architectures in a clustering-protocol-based distributed manner, overcoming energy efficiency issues
8	[54]	Green energy wireless charging algorithms can efficiently power IoT devices by multi-green base station joint accumulative charging schemes
9	[63]	A 5G-technology-based heterogeneous wireless network resource virtualization architecture for green computing with regard to IoT data classification
10	[45]	An extended Kalman filtering method for 6G IoT sensing devices can predict harvesting power
11	[37]	Blockchain, green ubiquitous IoT wireless spectrum sharing, and 6G mobile communication technology for energy efficiency
12	[14]	Energy-aware resource management, 6G-enabled cell-free massive MIMO networks, and green communication capabilities can provide optimized network coverage and power based on MIPA-MCAS algorithms
13	[59]	A 6G IoT confidence information coverage reinforcement learning node sleep scheduling algorithm and Q-learning collaborative intelligence can extend network lifetime, balance the energy consumption, and enhance resource efficiency with fewer active nodes
14	[60]	A hybrid whale spotted hyena optimization (HWSHO) algorithm for 6G-enabled massive IoT device green communication in terms of energy-efficient network cluster-based data dissemination
15	[75]	Deep reinforcement learning MIMO-NOMA IoT systems and Age of Information can shape optimal channel capacity, power allocation, spectrum efficiency, and energy consumption
16	[65]	Digital twin AI algorithms enable energy-efficient 6G network deployment in smart factories based on mobile-enhanced big data edge computing–cloud collaborative mechanisms for green communication and network performance optimization
17	[52]	Fifth-generation Industrial Internet of Things (IIoT) heterogeneous network intelligence-driven green energy-efficient resource allocation mechanisms based on deep reinforcement learning algorithms
18	[61]	Integrated relative energy efficiency can assess traffic profiles and wireless network capacities for green communications
19	[66]	Sixth-generation edge–cloud collaborative industrial-IoT-learning-based data compressions and transmissions for spectrum efficiency increase and system latency mitigation

3. Source Correlation Analysis

Co-authorship correlations (Figure 2) show that 6G AI green computing and autonomous wireless communications configure energy-efficient IIoT in terms of resource and performance management optimization. Smart energy management of green 6G artificial intelligence IoT industrial wireless systems in big-data-driven equipment and manufacturing environments [28,39,53] in terms of edge–cloud collaborative 6G IIoT networks and fog device IIoT data distribution [32,43,66] typify practical integration challenges and compatibility issues between existing industrial systems and emerging 6G technologies [12,47,54]. Edge computing IIoT process intelligence, green-network-based energy efficient systems, and collaborative industrial artificial-intelligence-based smart IoT devices [35,58,67] ensure smooth integration of energy-efficient IIoT devices in green 6G networks and smart industrial urban environments. Practical integration challenges include undeveloped fog-computing-based 6G IoT network energy efficient techniques, deep- and machine-learning-based green communications, and energy-aware resource management and spectrum-sensing algorithms [29,41,60] in green sustainable IIoT environments. Compatibility issues comprise energy harvesting and cloud collaborative mechanisms, artificial intelligence energy-efficient communication autonomous networks, and large-scale industrial production-based IoT device technologies for 6G wireless network system connectivity [36,49,65] in 6G AI green computing and autonomous wireless communications. The IIoT device smooth integration in green 6G networks requires context-aware energy efficiency management, 6G-network-based energy efficient and green artificial intelligence IoT industrial wireless systems, and massive wireless energy-efficient sustainable network automation [34,51,63] in 6G IIoT environments and across sustainable energy infrastructures.

Figure 2. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding co-authorship (see Table 4 for VOSviewer clusters).

Figure 2. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding co-authorship (see Table 4 for VOSviewer clusters).

Table 4. VOSviewer clusters.

Main topics covered by the authors in each group	pear (collaborative industrial artificial-intelligence-based smart IoT devices), maroon (green-network-based energy efficient systems), orange (6G-network-based energy efficient and green artificial intelligence IoT industrial wireless systems), violet (energy-aware resource management and spectrum sensing algorithms), turquoise (green 6G artificial intelligence IoT industrial wireless systems), azure (large-scale industrial production-based IoT device technologies)
Research focus reasons	pear (6G AI green computing and autonomous wireless communications), maroon (deep- and machine-learning-based green communications), orange (edge–cloud collaborative 6G IIoT networks), violet (artificial intelligence energy-efficient communication autonomous networks), turquoise (massive wireless energy-efficient sustainable network automation), azure (edge computing IIoT process intelligence)
The number of research groups having shared interests	pear (25), maroon (24), orange (23), violet (28), turquoise (26), azure (10)

Table 4. VOSviewer clusters.

Main topics covered by the authors in each group	pear (collaborative industrial artificial-intelligence-based smart IoT devices), maroon (green-network-based energy efficient systems), orange (6G-network-based energy efficient and green artificial intelligence IoT industrial wireless systems), violet (energy-aware resource management and spectrum sensing algorithms), turquoise (green 6G artificial intelligence IoT industrial wireless systems), azure (large-scale industrial production-based IoT device technologies)
Research focus reasons	pear (6G AI green computing and autonomous wireless communications), maroon (deep- and machine-learning-based green communications), orange (edge–cloud collaborative 6G IIoT networks), violet (artificial intelligence energy-efficient communication autonomous networks), turquoise (massive wireless energy-efficient sustainable network automation), azure (edge computing IIoT process intelligence)
The number of research groups having shared interests	pear (25), maroon (24), orange (23), violet (28), turquoise (26), azure (10)

Citation correlations (Figure 3) show that AI intelligent energy harvesting and optimization algorithms articulate 6G IIoT green communication and resource allocation across sustainable smart cities. All the identified sources either include quantitative impact analysis or provide empirical evidence demonstrating the energy efficiency improvements e.g., [12,13,14,15,66] or cost benefits e.g., [29,37,65,66] of implementing IIoT in green 6G networks in terms of digital twin e.g., [44,65] and blockchain-technology-based e.g., [36,44,48,58,64] energy efficient resource management e.g., [12,13,14,63,66] in smart factories e.g., [51,65]. Based on the inspected literature, future research can include case studies or simulations to quantify these benefits with regard to energy efficiency techniques and smart performance optimization in sustainable green 6G IIoT networks [15,30,31,42,52,62], e.g., on context-aware autonomous power efficiency, intelligence-driven self-organizing energy-efficient green resource allocation, energy-aware resource management algorithms, 6G edge computing green communication wireless network planning, artificial intelligence energy-efficient communication autonomous networks, blockchain-based transitive energy management, IIoT distributed artificial intelligence 6G pervasive edge computing communication networks, and energy-efficient IoT smart industrial urban computing environments [33,38,44,48,59].

Figure 3. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding citation (see Table 5 for VOSviewer clusters).

Figure 3. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding citation (see Table 5 for VOSviewer clusters).

Table 5. VOSviewer clusters.

Main topics covered by the authors in each group	violet (AI intelligent energy harvesting and optimization algorithms), pear (6G IIoT green communication and resource allocation), turquoise (IIoT in green 6G networks), amaranth (context-aware autonomous power efficiency), azure (blockchain-technology-based energy efficient resource management), baby blue (energy-aware resource management algorithms)
Research focus reasons	violet (sustainable green 6G IIoT networks), pear (6G edge computing green communication wireless network planning), turquoise (IoT distributed artificial intelligence 6G pervasive edge computing communication networks), amaranth (energy-efficient IoT smart industrial urban computing environments), azure (intelligence-driven self-organizing energy-efficient green resource allocation), baby blue (artificial intelligence energy-efficient communication autonomous networks)
The number of research groups having shared interests	violet (7), pear (13), turquoise (7), amaranth (8), azure (6), baby blue (6)

Table 5. VOSviewer clusters.

Main topics covered by the authors in each group	violet (AI intelligent energy harvesting and optimization algorithms), pear (6G IIoT green communication and resource allocation), turquoise (IIoT in green 6G networks), amaranth (context-aware autonomous power efficiency), azure (blockchain-technology-based energy efficient resource management), baby blue (energy-aware resource management algorithms)
Research focus reasons	violet (sustainable green 6G IIoT networks), pear (6G edge computing green communication wireless network planning), turquoise (IoT distributed artificial intelligence 6G pervasive edge computing communication networks), amaranth (energy-efficient IoT smart industrial urban computing environments), azure (intelligence-driven self-organizing energy-efficient green resource allocation), baby blue (artificial intelligence energy-efficient communication autonomous networks)
The number of research groups having shared interests	violet (7), pear (13), turquoise (7), amaranth (8), azure (6), baby blue (6)

Bibliographic coupling correlations (Figure 4) show that massive wireless energy-efficient sustainable network automation and distributed federated learning shape 6G green communications and IIoT fog networks. Mobile device computational, distributed sensing, and holographic beamforming capabilities can ensure secure and privacy-preserving communication mechanisms [55,57,58] with regard to green IIoT data in 6G IoT energy-efficient fog computing networks. Sixth-generation edge computing, wireless spectrum, and digital twin technologies; federated learning, 6G wireless communication, and multi-agent systems; and collaborative and distributed computing intelligence tools [14,56,64] can consolidate privacy and cybersecurity across energy-efficient IoT smart industrial urban computing environments. Secure and resilient sustainable energy sources require green IoT, immersive simulation, and cloud-based computing technologies, edge intelligence and mobile communication network management automation systems, and environment mapping and spectrum sensing algorithms [40,50,61] in 6G sensor-based energy-efficient IoT smart industrial urban environments. Green 6G IIoT security and privacy concerns and issues can be solved by leveraging energy-efficient 6G IoT fog computing and environmentally aware wireless communication technologies [13,45,46] throughout artificial intelligence energy-efficient communication autonomous and clustered wireless sensor networks.

Figure 4. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding bibliographic coupling (see Table 6 for VOSviewer clusters).

Figure 4. VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding bibliographic coupling (see Table 6 for VOSviewer clusters).

Table 6. VOSviewer clusters.

Main topics covered by the authors in each group	pear (federated learning, 6G wireless communication, and multi-agent systems), jade (massive wireless energy-efficient sustainable network automation), turquoise (edge intelligence and mobile communication network management automation systems), violet (environment mapping and spectrum sensing algorithms), blue-green (6G-sensor-based energy-efficient IoT smart industrial urban environments), amaranth (6G green communications and IIoT fog networks)
Research focus reasons	pear (6G IoT energy-efficient fog computing networks), jade (6G edge computing, wireless spectrum, and digital twin technologies), turquoise (green IoT, immersive simulation, and cloud-based computing technologies), violet (energy-efficient 6G IoT fog computing and environmentally aware wireless communication technologies), blue-green (artificial intelligence energy-efficient communication autonomous and clustered wireless sensor networks), amaranth (energy-efficient IoT smart industrial urban computing environments)
The number of research groups having shared interests	pear (29), jade (36), turquoise (10), violet (8), blue-green (7), amaranth (37)

Table 6. VOSviewer clusters.

Main topics covered by the authors in each group	pear (federated learning, 6G wireless communication, and multi-agent systems), jade (massive wireless energy-efficient sustainable network automation), turquoise (edge intelligence and mobile communication network management automation systems), violet (environment mapping and spectrum sensing algorithms), blue-green (6G-sensor-based energy-efficient IoT smart industrial urban environments), amaranth (6G green communications and IIoT fog networks)
Research focus reasons	pear (6G IoT energy-efficient fog computing networks), jade (6G edge computing, wireless spectrum, and digital twin technologies), turquoise (green IoT, immersive simulation, and cloud-based computing technologies), violet (energy-efficient 6G IoT fog computing and environmentally aware wireless communication technologies), blue-green (artificial intelligence energy-efficient communication autonomous and clustered wireless sensor networks), amaranth (energy-efficient IoT smart industrial urban computing environments)
The number of research groups having shared interests	pear (29), jade (36), turquoise (10), violet (8), blue-green (7), amaranth (37)

Co-citation correlations (Figure 5) show that artificial intelligence technology and 6G blockchain-based green IoT big data networks optimize energy-efficiency management and spectrum sharing. Technical standards, protocols, and architectures pivotal for IIoT deployment in green 6G networks and environments include big-data-based intelligent decision and energy-efficient in-network computing task scheduling algorithms in 6G energy-efficient and energy-aware resource management [13,56,66], mobile-enhanced digital twin technologies for pervasive edge computing IIoT process intelligence [15,54,64], and edge–cloud collaborative 6G IIoT fog computing energy efficiency [35,57,67] in intelligent urban computing and industrial systems [48,50,58]. Of relevance are also 6G network equipment transmission and computing capabilities [12,45,55] in energy-efficient IoT smart industrial urban computing environments [28,33,47], 6G IIoT edge and cloud collaborative mechanisms for energy-aware decision support systems [14,52,53], IIoT data sharing and computing devices [36,41,46] in 6G green mobile network architectures [30,38,63], industrial cloud computing capabilities for energy efficient IoT network connectivity [31,42,60], and IIoT network edge computing nodes and edge–cloud collaborative flexible computing resource allocation and sharing [32,51,65] in 6G pervasive edge computing communication networks [37,40,61].

Figure 5. VOSviewer mapping of energy-efficient Industrial Internet of Things in green 6G networks regarding co-citation (see Table 7 for VOSviewer clusters).

Figure 5. VOSviewer mapping of energy-efficient Industrial Internet of Things in green 6G networks regarding co-citation (see Table 7 for VOSviewer clusters).

Table 7. VOSviewer clusters.

Main topics covered by the authors in each group	pear (IIoT network edge computing nodes and edge–cloud collaborative flexible computing resource allocation and sharing), violet (big-data-based intelligent decision and energy-efficient in-network computing task scheduling algorithms), blue-green (6G energy-efficient and energy-aware resource management), emerald (6G IIoT edge and cloud collaborative mechanisms for energy-aware decision support systems), amaranth (mobile-enhanced digital twin technologies for pervasive edge computing IIoT process intelligence)
Research focus reasons	pear (6G blockchain-based green IoT big data networks), violet (IIoT deployment in green 6G networks and environments), blue-green (energy-efficient IoT smart industrial urban computing environments), emerald (6G pervasive edge computing communication networks), amaranth (industrial cloud computing capabilities for energy efficient IoT network connectivity)
The number of research groups having shared interests	pear (38), violet (10), blue-green (32), emerald (34), amaranth (64)

Table 7. VOSviewer clusters.

Main topics covered by the authors in each group	pear (IIoT network edge computing nodes and edge–cloud collaborative flexible computing resource allocation and sharing), violet (big-data-based intelligent decision and energy-efficient in-network computing task scheduling algorithms), blue-green (6G energy-efficient and energy-aware resource management), emerald (6G IIoT edge and cloud collaborative mechanisms for energy-aware decision support systems), amaranth (mobile-enhanced digital twin technologies for pervasive edge computing IIoT process intelligence)
Research focus reasons	pear (6G blockchain-based green IoT big data networks), violet (IIoT deployment in green 6G networks and environments), blue-green (energy-efficient IoT smart industrial urban computing environments), emerald (6G pervasive edge computing communication networks), amaranth (industrial cloud computing capabilities for energy efficient IoT network connectivity)
The number of research groups having shared interests	pear (38), violet (10), blue-green (32), emerald (34), amaranth (64)

4. Distributed Artificial Intelligence Green 6G Pervasive Edge Computing Communication Networks in Energy-Efficient IoT Smart Industrial Urban Environments

Sixth-generation communication technology advancements can significantly impact massive network infrastructures by the large-scale volume of network nodes [29], resulting in high energy consumption. Green Internet of Vehicle (IoV)-based intelligent transportation systems can configure sustainable vehicular communication, computation, traffic, and networking by diminishing electricity expenses and fuel consumption. Energy harvesting and vehicular networking technologies can be leveraged in green 6G IoV systems on a large scale by use of big data analytics, deep and machine learning algorithms, and cognitive computing. Onboard information service, routing design, network topology, traffic control and conditions, and connected IoV devices can optimize vehicular sensor integration and increase energy efficiency. Big-data-based intelligent decision algorithms can improve energy efficiency in terms of management, harvesting, and sharing across sustainable vehicular communication. Edge computing, traffic management, and 6G green IoV networking infrastructures are also instrumental in this respect. Network node computing and transmission capabilities [34] can be optimized in 6G network environments. Software-defined technology-based in-network computing can manage complex computing tasks while reducing energy consumption by downsizing the data scale. Virtualization and container technologies are pivotal in computing task unified scheduling and programming, application-level service and in-network computing task scheduling, and data traffic processing in intelligent transportation systems.

Sixth-generation energy-efficient and energy-aware resource management requires efficient computing and storage distribution and task offloading [12] in the large-scale IoT-fog environment. Energy-aware mechanisms leveraged throughout the distributed computing environment typically shape the quality of service in fog IoT networks. Performance-aware mechanisms can enhance fog computing energy efficiency in the 6G IoT area. By compressing artificial intelligence data shared across resource constrained IIoT wireless networks [66], spectrum usage, end-to-end latency, input redundancy and irrelevancy, and energy consumption can decrease while having low computational expenses. Extended-reality-technology-related compressed big data and stringent latency requirements can challenge transmission efficiency across edge–cloud collaborative 6G IIoT and resource-constrained wireless networks. Complex data processing supports ubiquitous intelligence in artificial intelligence model design by deep and machine learning algorithms.

Distributed artificial intelligence 6G pervasive edge computing communication networks for IIoT [47] can enhance predictive maintenance, computing and networking resources, and operational efficiencies. Machine learning algorithms and cloud computational capabilities can optimize pervasive edge computing IIoT process intelligence across 6G communication and automation networks for energy efficiency. Edge computing can optimize 6G network data processing capabilities [43] and reduce energy consumption. The smart IoT device data volume and ever-reducing size of the IoT system energy-constrained device can impact 6G edge network technical design with regard to computationally extensive task offloading. Long short-term memory-based deep recurrent neural algorithms can thus be used.

Sustainable edge intelligence can optimize sensing and computing capabilities of intelligent connected vehicles across green smart 6G networks for vehicle-to-everything communication and autonomous driving traffic fluctuations [37] while decreasing carbon emissions via renewable energy power. Cooperative perception and inference tasks facilitate sensor data fusion in green smart 6G network computation traffic and route planning in transportation systems by traffic demand monitoring and prediction. Consequently, carbon emission and renewable energy consumption are reduced. Intelligent urban computing and industrial systems can assess energy-efficient performance [62] by ant colony and particle swarm optimization algorithms. Smart transportation, environmental, and industrial supply chain management can decrease energy consumption by supervised machine learning and unsupervised recommender algorithms. Bio-inspired, genetic, and evolutionary algorithms are pivotal in energy-aware decision support system and smart grid management across energy-efficient IoT smart industrial urban computing environments.

Our analyses prove that the long-term sustainability and environmental footprint of widespread adoption of IIoT in green 6G networks and across low-carbon sustainable energy in green environments [13,37,63] are related to low-carbon sustainable development and resource allocation [28,53], IoT-energy- and cloud-computing-based pollution and carbon footprint decrease [53,63], renewable energy power carbon emission reduction [13,28], energy generation and distribution management [28,37,53], and smart grid and zero-carbon technology-driven distributed energy generations [13,37,63].

5. Energy-Efficient Algorithm and Green Computing Technologies in Smart Industrial Equipment and Manufacturing Environments

Edge intelligence, IoT data sensing, reconfigurable intelligent surfaces, blockchain technologies, and reliable low-latency communications [48] shape 6G network management. Sixth-generation wireless communication and vehicular IoT intelligent and autonomous networks as well as data-centric intelligent systems can optimize seamless mobile traffic capability. Edge and cloud computing services, large-scale device connectivity density, spectrum availability, and network latency assist in this respect. Multiple target decomposition evolutionary algorithms can carry out multiple data stream task scheduling [34], decreasing system energy consumption and network transmission traffic. Task scheduling can enhance resource utilization by the use of virtual machines and containers. Energy-efficient in-network computing task scheduling algorithms can deploy 6G network equipment transmission and computing capabilities in virtual machine environments. Thus, network traffic and data processing service scheduling are improved.

Internet of Energy (IoE) comprises smart and high-voltage grids, 6G communication technologies, and renewable energies [13], integrating decentralized network control and power grid system monitoring, intelligent energy packaging management, and IoT measurement infrastructure with regard to electricity production and distribution. IoE technologies can decrease energy consumption, handle energy reserves, and manage energy generation and distribution, being instrumental in reducing carbon emissions. IIoT device multi-source heterogeneous data compressibility can be used to carry out data aggregation [66] by use of cloud manufacturing networks and industrial cloud computing capabilities. IIoT data sharing and computing devices, 6G IIoT edge and cloud collaborative mechanisms, and production line operation control actuators can be also harnessed. IIoT network edge computing nodes can assess operation status and abnormal events by multi-dimensional heterogeneous data through sensor instrumentation and control. Edge–cloud collaborative flexible computing resource allocation and sharing can optimize data transmission analysis, efficiency, prediction, connectability, inference, and interoperability.

The massive volume of distributed devices [47] may intensify computational workloads and energy consumption significantly. Deep learning artificial intelligence and federated learning techniques can reduce energy consumption and increase computation capacity, integrating distributed learning node capabilities, augmented and virtual network functions, and customized communication networks. Insufficient computation capability and battery-powered energy [43] affect edge-centric 6G network data offloading across IoT devices. Energy-efficient algorithm and green computing technologies enable 6G edge network data offloading, cutting down energy consumption and latency in computation offloading in 6G IoT networks. Smart sensors and battery-powered renewable IoT node-based ambient energy sources can reduce computation tasks, resulting in low energy consumption.

Network function virtualization and slicing technologies, real-time sensing data and inference task execution, and software-defined and self-organized networking can enhance renewable energy utilization [37]. Movable infrastructure and mobile network power consumption, intelligent edge energy costs, and intelligent wireless network performance degradation in artificial-intelligence-based IIoT can thus be reduced. Context-aware electrical power generation management, energy management, harvesting, and power consumption techniques and power supply monitoring improve energy efficiency throughout smart girds in 6G IoT environments [64]. This is typically carried out using deep refinement learning and particle swarm optimization algorithms. Renewable energy generation, edge-based service scheduling, and blockchain-based transitive energy and solar power management can minimize power consumption in smart industrial equipment and manufacturing environments by deep learning and clustering techniques. Green energy computing and big-data-based intelligent power metering can be deployed for smart grid and transportations.

Our analyses prove that energy-efficient environmentally aware wireless communication and environmental pollutant reduction technologies determine natural environment changes, energy utilization efficiency, and environmental sustainability [13,28,29,54,55,62,64] in low-carbon sustainable energy in green environments. We also clarify the importance of environment mapping algorithms in IoT decision support and intelligent environmental systems.

6. Sustainable Energy Efficiency and Green Power Generation in Distributed Artificial Intelligence 6G Pervasive Edge Computing Communication Networks

Artificial intelligence techniques, edge intelligence systems, and fog intelligence [48] can configure low-latency IoT services across fog-node-based mobile intelligent networks. Convolutional-neural-network-based image classification simulations can result in accuracy performance across 6G intelligent IoT networks. Significant energy resources enable cloud-computing-based distributed IoT network operations and unmanned aerial vehicle communications with regard to cooperative sensing, wireless vehicular data sharing, and input transmission. Internet-of-Everything-based smart services develop on artificial intelligence 6G wireless communication systems [57] by integrating edge computing, holographic beamforming, and backscatter communications for IoT networks. Mobile communication network management automation systems impact artificial intelligence 6G IoT-based wireless robust connectivity. Spectrum technologies, machine learning techniques, and molecular and quantum communication networks can optimize low communication latency.

IoV network resource allocation computing and 6G intelligent transportation systems (51) can optimize vehicular traffic and mobility pattern prediction and collision and traffic jam warning. Collaborative industrial artificial-intelligence-based smart IoT device, manufacturing, and factory systems can improve production processes and reduce energy consumption by real-time sensor data collection and communication services in distributed network environments. Artificial intelligence edge-side data compression can cut down transmission latency [66], with computational complexity being pivotal in data aggregation processes. Multi-layer deep- and machine-learning-based large-scale data compression nonlinear mapping capabilities can eliminate data redundancy by integrating long short-term memory, recurrent, and convolutional neural networks. Weight fine-tuning technologies and principal component and linear discriminant analysis can be harnessed across distributed IIoT devices for feature extraction, spectrum, storage, and computing resource consumption as well as high-dimensional data visualization.

Computational workloads should be adequately distributed throughout pervasive edge-computing-assisted IIoT networks to decrease energy usage [47] by mobile edge and cloud computing task processing capabilities, optimized data storage and analysis, and heterogeneous IIoT service quality. Energy-centric design of distributed artificial intelligence 6G pervasive edge computing communication networks develops on IIoT device power supplies, energy resources, and computational capabilities. Extended reality, digital twin, and blockchain technologies, artificial intelligence algorithms, and IoT shape 6G communication systems by holographic and ubiquitous connectivity [44] while generating energy efficiency and spectrum allocation issues.

IoT decision support and intelligent environmental systems and IIoT energy consumption and management techniques [62] can harness deep and machine learning algorithms for real-time decision automation in travel optimization due to IoT intelligent device communication complexity. Artificial neural networks, power consumption and energy allocation algorithms, and data mining and energy management techniques [64] shape sustainable energy efficiency and power generation. Industrial IoE, Internet of Drones (IoD), IoV, and Environmental Internet of Everything (EIoE) are pivotal in energy management and harvesting techniques, energy consumption management, and green power generation and sustainability in smart grid and industrial equipment environments.

Our analyses prove that energy-efficient environmentally aware wireless communication technologies can optimize power efficiency and reduce system energy consumption and emissions [13,28,29,33,38,53] throughout power plants and grids. Sixth-generation sensor-based IoT green sustainable IIoT networks and wireless power transmission techniques integrate renewable and sustainable energy sources through big-data-driven IoT device sensor connectivity.

7. Results

Deep- and machine-learning-based green communications can optimize energy efficiency [75] and alleviate fossil fuel usage in 6G network infrastructures and connected terminals [56] by dynamic energy harvesting while improving power control, accuracy rate, and network management. Green computing and 6G IoT communication and heterogeneous wireless networks [63] can enable low-carbon sustainable development and resource allocation by adaptive boosting algorithm-based data classification accuracy and spectrum utilization efficiency. Energy harvesting technologies and green IIoT can enhance energy efficiency, transmit power control, and sustainable machine-to-machine communication services [30] throughout unmanned aerial vehicle (UAV) networks. Ultrascale multiple antennas and reconfigurable intelligent surfaces [61] are pivotal in the 6G energy-efficient green wireless communication network design. Autonomous network wireless technologies require data-intensive 6G Internet of Everything wireless communication networks in smart resource distribution [31] by artificial intelligence energy-efficient communication autonomous networks and device features and functions. Green sustainable IIoT can reduce system energy consumption and emissions [33] by efficient deployment of equipment resources. Federated-learning-based ambient backscatter systems can enhance resource scheduling schemes, while intelligent algorithms can find a solution to offloading task issues.

Intelligence-driven self-organizing energy-efficient green resource allocation in dynamic complex environments and 6G heterogeneous networks [52] develop on reliable IIoT services by harnessing deep reinforcement learning algorithms. IoT devices require massive volumes of energy consumption [54], impacting the electric grid load and bringing about natural environment changes. Green energy wireless charging algorithms can significantly power IoT devices. Energy harvesting technologies can extend 6G IoT device lifetime [49], facilitating green sustainable communication through transmission effectiveness by use of deep reinforcement learning and location preserving algorithms. Green energy-efficient ubiquitous artificial intelligence 6G Internet of Everything technologies are based on edge cloud computing [41] due to mobile device network traffic development and radio map network perception. Ubiquitous and seamless green communication and interconnection among huge volumes and the high mobility of 6G-enabled IoT devices [60] can decrease energy consumption through reduced end-to-end delay, cluster-based data dissemination, and a significant data rate. Whale optimizer algorithms and spotted hyena optimizers can also be harnessed in this respect. Data analytics technologies, computational optimization algorithms, and mobile edge computing [53] can be deployed for power-efficient structures, energy efficiency and performance, and streamlined energy resource management in smart cities. Green urban operations can optimize smart city equipment energy and power efficiency and consumption, reducing greenhouse gas emissions. IoT energy and cloud computing can optimize urban operations in green city building and in pollution and carbon footprint decrease in terms of smooth energy, smart grids, and electrical flow.

IoT sensing devices can strengthen energy harvesting techniques [45] in IoT networks and 6G environments. IoT devices require cellular network connections by various frequency bands in 6G IoT networks, and energy exhaustion should be avoided. Network automation and robotic technologies and deep and machine learning algorithms [46] configure 6G IIoT environments. Sixth-generation IoT communication systems and devices [67] can minimize energy consumption. By intelligent offloading, fog computing technology [32] can reduce IIoT device energy consumption. Industrial network computation offloading is pivotal in fog device IIoT data distribution through reinforcement learning techniques and controller-based device adaptation. Wireless energy transfer processes can power 6G IoT devices [36] by energy beamforming, resource scheduling, distributed antenna systems, and distributed ledger and blockchain technologies. Sixth-generation IoT networks necessitate constant uninterrupted operations by ambient energy sources through scalable wireless powering. Machine learning and clustering techniques can increase transmission power and connectivity in IoT portable and mobile device communication services [35], reducing energy consumption with regard to edge and vehicular devices. Sustainable energy infrastructures can cut down transmission delays, computational overhead, and energy constraints by nearest neighbor, mobile edge-computing-based distributed federated, and cluster head selection algorithms in 6G network aerial base stations.

Green and smart networks enable 6G IoT sustainable development [59], enhancing resource efficiency and increasing network lifetime by confidence information coverage reinforcement learning node sleep scheduling algorithms. Q-learning collaborative intelligence can satisfy the least active node coverage rate. Analytic hierarchy processes can be used to evaluate energy consumption, management, and monitoring [40] by neural network and system identification operations, IoT devices, deep and machine learning algorithms, and 6G communication technologies. Sixth-generation IoT services, artificial-intelligence-based smart algorithms, and communication, computation, and robust radio-over-fiber system architectures [39] integrate sensors, actuators, microcontrollers, high-density heterogeneous devices, and transceivers. Three-dimensional network-based green ubiquitous IoT wireless spectrum sharing [58] can lead to energy efficiency by blockchain, cloud, smart contract, and 6G mobile communication technologies. Smart mobile networks, Internet of Everything, and 6G spectrum technology [50] require broadening frequency bands, lower latency, and remote device connectivity. Context-aware energy efficiency management, blockchain and environmental pollutant reduction technologies, and green autonomous power consumption [64] are associated with 6G IoT smart harvesting environments. IoT industrial and smart harvesting environment develops on context-aware autonomous power efficiency and blockchain-based transitive energy management, intelligent power metering, and energy consumption reduction.

8. Discussion

Sustainable communication networking and vehicular edge computing, intelligent traffic and charging management, and scheduling optimization [29] can reduce energy consumption by green 6G IoV networking technologies and by dynamic energy sharing and harvesting. Sixth-generation wireless signal networking, sensing, and communication integration develops on wider bandwidth, antenna array dense distribution, and higher frequency bands. IoV-based vehicle-to-everything communication can shape suitable speed adjustment, traffic sensor data collection, and route planning, saving energy consumption. It also enables high-accuracy location and swift interruption recovery across the surrounding environment. Energy consumption can be minimized in optimized offloaded task transmission and processing and resource allocation across large-scale distributed networks and low-complexity smart traffic management in vehicular edge and cloud computing environments by artificial-intelligence-based self-learning algorithms. Significant resource utilization and reduced energy consumption [34] typify energy-efficient computing in 6G networks. In-network computing enables decreased cloud-side system energy consumption by network data plane application-layer processing functions. Sixth-generation energy-efficient in-network computing leverages hypervisors and containers across application task unified operating environments. Cloud servers and network nodes can integrate data processing tasks. Network node computing resources can cut down network transmission overhead, data processing pressure, and energy consumption.

Clustered wireless sensor networks can improve energy efficient communications [12] by reducing network traffic, transmission emissions, and routing delay. The resource allocation decision process across 6G IoT energy-efficient fog computing networks and distributed heterogeneous devices is enabled by precise network data and deep and machine learning algorithms for transmission energy efficiency and optimization. Diverse computation task, networking, and latency limitations should be analyzed in workload distribution to attain energy efficiency without computing device overloading for coherent resource allocation. Internet of Energy and smart control systems, together with distributed energy generations by use of smart grids and zero-carbon technologies [13], advance low-carbon sustainable energy in green environments. Intelligent energy system interoperability and electrical mobility facilitate automatic consumption enhancement and boosts network efficiency management in terms of consumption and distribution. Electric energy consumption and misallocation can shape greenhouse gas emission by power plants and grids, data management networks, and large-scale renewable generations.

IoT wireless network systems require deep and machine learning algorithms, supervised and unsupervised, deep reinforcement, and federated learning algorithms as well as particle swarm and ant colony optimization algorithms in artificial network nodes. Energy efficient IoT network connectivity and resource management assist reconfigurable intelligent surfaces and self-operable communication systems [15] in energy efficient network and spectrum management and across smart grids. Green and sustainable federated learning- and blockchain-based IIoT [51] can shape autonomous vehicles and unmanned aerial vehicles in intelligent transportation systems. The multi-layer and edge computing cloud architecture in green sustainable factories develop on 6G mobile networks and unmanned aerial vehicle systems, advanced artificial intelligence techniques, mobile edge computing services, decentralized remote monitoring, and federated learning system computation resources.

Data compression algorithms can enhance end-to-end reliability, mapping, and latency [66] through 6G edge computing IIoT network function and lightweight virtualization. Green 6G artificial intelligence IoT industrial wireless systems facilitate real-time data acquisition, compression, and reconstruction, determining data transmission performance by real-time closed-loop control, edge–cloud collaborative mechanisms, and predictive maintenance for energy efficiency. IIoT device data collection in relation to pervasive edge computing nodes leads to machine learning process-based energy consumption optimization [47] in increased industrial network uplink traffic. Edge computing federated learning techniques, radio access network spectrum resources, and distributed artificial intelligence can lead to energy efficiency and optimize computational performance in industrial 6G communication networks.

Digital twin network adaptive intelligence supports real-time 6G scalable network operation monitoring, ensuring reliable wireless connectivity, while enabling green high network capacity, real-time environmental modeling, ultra-low latency, and lower-complexity devices by IoT perception and sensing technologies. Generative AI digital twins and 6G sustainable mobile wireless communication and integrated radio frequency sensing technologies can enable 6G network scalability and proactive planning, fostering seamless multi-connectivity, high-reliability communication, and intelligent computing operations. Adaptive intelligence 6G technologies can enhance ubiquitous wireless connectivity capabilities, low-power joint communications, and spatial perception and broadband services. Generative AI-enabled digital twin scalable network operations and end-to-end wireless systems foster system spectral and network energy efficiency, quantum-safe communications, and channel non-linearity optimization. High-speed 6G wireless network operations and green technologies for environmental sustainability decrease network energy consumption.

9. Case Study: Fujitsu Data Application Simulations on Energy-Efficient Industrial Internet of Things in Autonomous Green 6G Networks

We have chosen Fujitsu as the case study and carried out application simulations based on sensed data mapping related to energy-efficient Industrial Internet of Things in autonomous green 6G networks by harnessing 3D holographic and extended reality technologies for feature-rich devices and cyber–physical production systems in IoT digitized spaces. Holographic communication depends on network virtualization capabilities and IoT end device data, image, and digital signal processing performance, with mobile core network functions as distinct microservices (disaggregation). Edge computing technologies enable IoT sensor and video data collecting and processing management across end-to-end cloud-native networks. Fujitsu integrates highly flexible open energy-efficient mesh networks and green technology (considering millimeter-wave access system advancements) for intelligent orchestration efficiency by multi-vendor and -layer operation automation. Sensor- and drone-technology-based factory floor work and production automation performance develop on plant and equipment condition inspection for preventive maintenance by autonomous robots. Spatial augmentation and digital twin technologies can be harnessed in smart factories for energy supply and demand stabilization.

Fujitsu develops digital twin high-performance 6G networks for energy efficiency, considering ever-changing situational conditions by use of real-time sensor- and camera-based machine control, production-process-unattended operation, and high-capacity data communication-based wireless devices. IoT camera and sensor data collecting and processing, high-speed data transfer environmental change detection, and power consumption mitigation can be achieved by high-capacity data communication-based wireless devices, software-defined network virtualization functions, and edge computing and green technologies across geo-distributed end-to-end ICT infrastructures. High-quality energy-efficient services require manufacturing equipment remote control, integrated network and computer infrastructure management, and 6G higher-capacity data transmission. End-to-end optimized network infrastructure power consumption can be attained by centralized end-to-end monitoring and IT system and network resource integrated management in 6G communications and wireless access systems.

To build energy-efficient networks, Fujitsu advances network topologies by mobile base station and mesh network sharing technologies. Dynamically coordinated 6G network topology and disaggregated computing technologies can be leveraged in cloud computer-based mass data storage and processing, network traffic load cloud-based-software-controlled base station and mobile terminal flexible coordination, and radio access network-greening-based energy and ICT-related power consumption reduction. Machine-learning-based photonics network monitoring technology reduces power consumption by network configuration optimization. Sixth-generation virtualized radio access network and end-to-end network operation technologies cut down CO2 emissions and network operations costs, ensure low-latency connectivity and computing resource optimization, and further network resource allocation optimization and communication network green technologies for energy efficiency.

Fujitsu develops 6G network green and AI autoscaling technologies that enable/disable network traffic load-driven cloud-based software controlled base station automation. AI and extended-reality-powered multi-access edge computing can achieve ultra-low latency communications, enhance data-visibility-based situational awareness, and enable virtualized base station parallel processing and sustainable digital transformation. Multi-access edge-computing-based low-latency communications integrate high-load data and wireless base station system processing management. Photoelectric fusion and network valorization technologies enable reduced environmental impact across sustainable digital infrastructure, optimize computing resources, ensure high-quality connectivity, and enhance network resource utilization.

Through digital twin software-defined base station virtualization and quantum-inspired computing technologies, Fujitsu aims to build sustainable 6G network infrastructures while decreasing the environmental impact in terms of carbon emissions output by connected IoT devices. Industrial generative AI and knowledge graph technologies can augment productivity in business operations by wireless base station communication processing and virtualized 6G base station software for energy consumption reduction. Augmented and virtual reality technologies can be harnessed in stable demand forecasting and management decisions for enterprise-wide scale productivity and organizational effectiveness by big-data-based knowledge graph generation automation. Generative AI amalgamation technologies can optimize supply chain and enterprise value proposition, performing energy-efficient sustainable business transformation in agile organizations by integrating mobile network connectivity, cloud-based AI services, and predictive analytics tools. Industry 4.0 generative AI systems can increase high-computing-power-based business process and data connection in smart factory and manufacturing environments.

10. Conclusions

Sixth-generation-network-based energy efficient systems integrate energy harvesting and efficient computing techniques, fog and edge device computation resources, and low power communications [12] through sensing and localization accuracy, data sharing devices, and connectivity and computing capabilities optimization. Sixth-generation communications can deploy fog nodes to facilitate ubiquitous connectivity and attain low latency. Energy-efficient 6G IoT fog computing technologies shape task offloading, latency reduction, and resource allocation. Energy-efficient environmentally aware wireless communication technologies and 6G sensor-based IoT networks [28] can reduce carbon emissions. IoT devices can gather accurate and detailed smart sensory data, and unmanned aerial vehicles serve as edge computing nodes for input processing, analysis, and aggregation, resulting in IoT energy efficiency and power optimization. Green IoT and cloud-based computing technologies can be deployed in aerial imagery and monitoring, being instrumental in transmission energy efficiency and connection accuracy through efficient IoT data collection and transmission. Edge-intelligence-based high-reliability real-time service delivery can decrease energy consumption by autonomous trajectory design and planning techniques and smart solar-powered sensor nodes. Wireless power transfer and coordinated charging scheduling technologies can enhance vehicular mobility and dynamic traffic-based collaborative energy management [29] across green 6G IoV infrastructures. Software-defined networking distributed edge computing nodes can gather traffic data and enable vehicle-to-everything communication by green IoV systems, Non-orthogonal Multiple Access (NOMA) technologies and 6G-enabled vehicular networks, while increasing spectrum efficiency and connectivity. Energy-efficient collaboration and resource allocation develop on multi-radio access technologies and on vehicular communication mechanisms in dynamic traffic environments and across green IoV and distributed vehicle-to-everything networks. Energy-efficient resource allocation can improve green vehicle-to-everything communication and vehicular network routing performance by integrating real-time traffic data and varying vehicle connectivity computation resources. This leads to reduced dynamic transmission and communication energy consumption across IoV infrastructures.

Contextual awareness-based adaptive intelligence 6G technologies can optimize wireless connectivity and efficient resource utilization. Adaptive machine-learning-based transceiver chains can enhance the signal-to-noise ratio, furthering broader coverage and 6G network performance and efficiency for fault-resilient network operation across integrated industrial IoT autonomous robotic environments by extended reality and metaverse technologies. Adaptable code rate designs assist 6G wireless communications, cutting down signal distortions by digital twin extended reality and sensor fusion technologies. Generative AI and extended reality-based 6G device adaptive capabilities can optimize resource allocation and predict network performance by generative AI 6G digital twin integrated sensing and communication technologies.

11. Theoretical Contributions to the Literature

Our systematic review clarifies that 6G green mobile network architectures can provide significant quality of service, persistent 3D coverage, and energy efficiency [42] through pervasive artificial intelligence technologies. Energy harvesting technologies can optimize system energy efficiency and ambient backscatter communication, articulating sustainable green networks. Green 6G networks can configure energy-efficient unmanned-aerial-vehicle-based wireless connections across mobile communication systems. Vehicular mobile networking and energy-efficient computing services can cut down energy consumption across distributed edge and cloud nodes [29] in IoV communication infrastructures. Vehicular routing energy efficiency can be enhanced by relaying node selection and resource allocation optimization in vehicular network environments by green vehicle-to-everything communication. Green vehicular cloud and mobile edge computing and routing technologies and roadside sensors can shape system and computation energy efficiency and real-time traffic management and cooperative resource allocation decisions. Large-scale heterogeneous intelligent navigation-based distributed vehicular networks in dynamic traffic environments are decisive in this respect. Traffic signal management, vehicular computation task processing, and driving speed control can optimize green transportation system energy efficiency. Wireless power transmission and route planning techniques can bring about road congestion alleviation and carbon emission minimization. Sixth-generation wireless networks can monitor and control smart energy grid management for dynamic spectrum access [76], enabling sustainable seamless blockchain and cybertwin integration for scalability, resilience, and reliability.

12. Practical Contributions to the Literature

Renewable-energy-source-based distributed generation integrates 6G wireless networks so as to process collected sensory data swiftly [38], reducing power consumption by optimized wireless energy transfer, smart grid and batteries, and energy management and cyber–physical systems. Low-latency ultra-fast 6G wireless communications assist big-data-driven sensor connectivity in reducing carbon emissions. Industry 4.0 automation, connected IoT devices, and cloud computing and augmented reality technologies can focus on renewable and sustainable energy sources. Smart UAV technologies can optimize collaborative green IoT and sustainable Industry 4.0 environments by collected data sharing through fog nodes [28], saving energy and ensuring battery longevity by distributed device operation mode control. UAV-technology-based green IoT facilitate energy efficiency, enhance real-time sensor node transmission power, and reduce carbon footprint. UAV technologies can reduce IoT device power consumption by close reliable data transfer in smart environments, while extending the coverage area by green wireless communication technologies and clustering techniques. Dynamic wireless charging infrastructures, vehicle-to-everything (V2X) communications, battery swapping stations, smart green Internet of Vehicles, and fast charging stations [77] can enable electric grid renewable energy resource integration while preventing grid overload, decrease greenhouse gas (GHG) emissions and oil consumption, and improve air quality and sustainable vehicular connectivity and networking. Energy-aware scheduling algorithm-based 6G green communication systems [78] can be deployed for optimal resource sharing and scheduling decision management in terms of energy consumption efficiency. Non-orthogonal multiple access (NOMA), 6G IIoT communications, and statistical signal transmission [79] can be harnessed for ubiquitous connectivity and detection performance due to monitoring service sensing accuracy.

13. Limitations and Further Directions of Research

A quantitative literature review was performed only across ProQuest, Scopus, and the Web of Science, and for original and review research published between 2019 and 2024 covering energy-efficient IIoT in green 6G networks. Future research should further investigate energy-efficient algorithm and green computing technologies, distributed artificial intelligence green 6G pervasive edge computing communication networks, IoT decision support and smart environmental systems, and decentralized network control and power grid system monitoring. Distinct study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software can show how edge-based sustainable IoT intelligent urban computing [55] develop on energy harvesting management. Deep, machine, and federated learning techniques; smart environmental systems; and IoT device energy management can cut down energy and power consumption. Such cutting-down technologies can enhance sustainable intelligent urban computing performance and green energy harvesting and consumption management design. Traffic signal timing, road conditions, vehicle routing planning, power transmission efficiency, and driving behavior control [29] articulate green IoV energy-efficient traffic and charging management. IoV infrastructures require massive amounts of energy in data gathering and processing by renewable energy harvesting technologies and management mechanisms. Cooperative traffic scheduling, real-time traffic condition monitoring, and mobile vehicle computation services can be pivotal in energy utilization efficiency and environmental sustainability, enabling vehicle-to-everything communication in green IoV systems. Wireless power transfer systems and renewable resource-based energy harvesting techniques can reduce energy consumption across 6G green IoV infrastructures and in dynamic intelligent traffic environments by resource coordination mechanisms and vehicular edge computing.