# Towards Green Metaverse Networking: Technologies, Advancements and Future
Directions

Siyue Zhang, Wei Yang Bryan Lim, Wei Chong Ng, Zehui Xiong, Dusit Niyato, ,
Xuemin Sherman Shen, , Chunyan Miao

###### Abstract

As the Metaverse is iteratively being defined, its potential to unleash the
next wave of digital disruption and create real-life value becomes
increasingly clear. With distinctive features of immersive experience,
simultaneous interactivity, and user agency, the Metaverse has the capability
to transform all walks of life. However, the enabling technologies of the
Metaverse, i.e., digital twin, artificial intelligence, blockchain, and
extended reality, are known to be energy-hungry, therefore raising concerns
about the sustainability of its large-scale deployment and development. This
article proposes Green Metaverse Networking for the first time to optimize
energy efficiencies of all network components for Metaverse sustainable
development. We first analyze energy consumption, efficiency, and
sustainability of energy-intensive technologies in the Metaverse. Next,
focusing on computation and networking, we present major advancements related
to energy efficiency and their integration into the Metaverse. A case study of
energy conservation by incorporating semantic communication and stochastic
resource allocation in the Metaverse is presented. Finally, we outline the
critical challenges of Metaverse sustainable development, thereby indicating
potential directions of future research towards the green Metaverse.

###### Index Terms:

Metaverse, Energy Efficiency, Sustainability, Digital Twin, Artificial
Intelligence, Blockchain, Extended Reality

##  I Introduction

The Metaverse is arriving at an inflection point of development thirty years
after the term was first coined in Neal Stephenson’s science-fiction novel
”Snow Crash”. While its definition is still fluid today, the Metaverse is
generally regarded as an embodied version of the Internet that seamlessly
synergizes virtual and physical worlds, and enables users to live and interact
in virtual worlds through controlling personalized avatars.

A growing amount of commitment, investment, and effort has been made to
realize the Metaverse across academia and industry. For the former, the number
of research publications related to the Metaverse in 2022 is five times more
than that in 2021. For the latter, Facebook was rebranded as Meta to shift
from a media company to a Metaverse company in 2021. Such excitement about the
Metaverse is driven by improved technical and marketplace readiness. For
example, the enabling technologies of the Metaverse have undergone rapid
advancements recently, including the Internet-of-Things (IoT), digital twin
(DT), 5G/6G, artificial intelligence (AI), blockchain, virtual reality (VR),
and augmented reality (AR). Moreover, the COVID-19 pandemic has accustomed
users to embracing digital life, e.g., online meetings and shopping.

As compared to the massively multiplayer online role-playing games (MMORPGs)
of today, the Metaverse is a far broader concept that features user-generated
content (UGC), a variety of use cases, decentralization, and interoperability
across platforms and devices. However, there are still technical deficiencies
in communication speed, computing power, data storage, etc., to fully realize
the envisioned Metaverse, not to mention social barriers. As the Metaverse is
relatively nascent, most research has been centered on Metaverse architecture,
enabling technologies, and applications. The study of [1] discusses the
Metaverse implementation at mobile edge networks at scale. It presents a
visionary four-layer architecture positioning enabling technologies in the
edge-enabled Metaverse. In addition to technology, [2] addresses social
aspects of the Metaverse, e.g., acceptance, security, and privacy.

While the sustainability aspect has been pointed out in the aforementioned
studies, no study has yet to evaluate the energy consumption, efficiency, and
sustainability of the Metaverse. The energy demand could easily escalate as
the Metaverse will require 1000-fold more computing power and push data usage
by 20 times in the next 10 years.
111https://www.intel.com/content/www/us/en/newsroom/opinion/powering-
metaverse.html,222https://techblog.comsoc.org/2022/02/20/credit-suisse-
metaverse-to-push-data-usage-by-20-times-worldwide-by-2032/ From our
perspective, Metaverse sustainability is the ability of the Metaverse to exist
and develop without depleting natural resources for the future, e.g., fossil
fuels for energy and raw materials for electronic devices. It is becoming
particularly critical due to increasing economic and environmental costs, user
awareness, and stringent sustainability targets such as the Net Zero
coalition.333https://www.un.org/en/climatechange/net-zero-coalition Therefore,
this article makes the first effort to optimize energy efficiencies of all
network components for realizing the Metaverse, i.e., Green Metaverse
Networking (GMN). The article provides an analytical framework in a
technology-based approach for evaluating energy efficiency. Specifically, our
framework centers around the four main groups of energy-hungry enabling
technologies namely:

  * •

Digital Twin about creating and operating the Metaverse,

  * •

Artificial Intelligence about endowing the Metaverse with autonomy and
intelligence,

  * •

Extended Reality about user interaction with the Metaverse, and

  * •

Blockchain about data storage and interoperability in the Metaverse.

The structure and key novelties of this article are as follows:

  1. 1.

We identify energy-hungry technologies and their application scenarios,
analyze their consumption, efficiency, and influencing factors, and outline
sustainability-related metrics, thereby providing an analytical framework for
assessing the Metaverse sustainability in Section II.

  2. 2.

We review the forefront computing and networking advancements of these
technologies towards GMN in Section III. Through a case study in Section IV,
we illustrate how semantic communication and stochastic resource allocation
can contribute to energy conservation, thereby guiding the design of future
communication systems for the Metaverse.

  3. 3.

We outline the critical challenges and future research directions towards GMN
in Section V.

Figure 1: A four-tier Metaverse architecture and energy-intensive processes.

##  II Energy-Hungry Technologies, Energy Consumption, Efficiency and Metrics

Metaverse is made possible due to the development of enabling technologies.
But many of them are energy-hungry. Their sustainability-related metrics are
summarized in Table I.

###  II-A Digital Twin

The DT is a digital representation that accurately reflects the physical
world. It provides real-time visibility, data analytics, and the capability to
predict potential changes in the physical world. As the backbone of DT, IoT
collects physical data by networked sensors, derives insights from data by
edge processors, transmits insights by gateways, and proceeds Metaverse
commands by executors (Fig. 1). While IoT has already connected physical
objects to the Internet nowadays, the Metaverse will spur the demand for more
advanced and interconnected IoT devices (e.g., AR/VR head-mounted displays
(HMDs) and unmanned aerial vehicles (UAVs)) to enable seamless synchronization
between physical and digital worlds. Undoubtedly, the increasing quantity and
complexity of IoT devices would impose challenges on sustainable energy
supply, especially since most devices are wireless and powered by limited
batteries.

Network efficiency has a considerable influence on DT energy consumption,
including radio networks, IoT, wireless sensor networks (WSNs), etc. The
efficiency varies depending on the network device, communication mode,
protocol, and software implementation. Typically, network efficiency is
measured by the average power consumption at a certain level of service
quality. The Quality of Service (QoS) metrics such as throughput, latency, and
packet loss are well-defined to evaluate classical information-theoretic
network performance. However, the perceived quality by users could be of more
importance, especially for content providers. For example for video streaming,
users could be sensitive to quality degradation due to varying bit rates
although the throughput is high. Maintaining the bit rate at a lower but
stable level, i.e. lower QoS, could improve user experience and reduce energy
consumption at the same time. Therefore, a paradigm shift towards quality of
experience (QoE) is called by content-centric Metaverse applications to focus
on the overall acceptability of service perceived by the user.

For wireless communications, the energy efficiency is addressed by data rate
per power (in bit/J), which is currently at the order of 10 Mbit/J. Spectral
efficiency (in bps/Hz) measures the data throughput over a given spectrum for
a site. Area energy efficiency (in bps/Hz/m2/J) is the primary energy
efficiency metric for current wireless systems, which equals spectral
efficiency divided by area coverage and energy consumption. 5G pledges 10
times higher area energy efficiency than 4G. Along with the rapid emergence of
aerial nodes such as UAVs, volumetric energy efficiency (in bps/Hz/m3/J) would
become a more important metric in future systems.

Apart from networking, a significant amount of energy is required for
computation. For example, 3D modeling and simulation are essential for
creating avatars, DTs, and virtual worlds. To create immersive and realistic
experiences, 3D models are developed with computation-intensive simulations of
physics, lighting, sound, and other elements. Compared to creating virtual
worlds, around-the-clock server hosting and operations in data centers consume
far more energy because of numerous user connections, heavy data traffic, and
intensive computational load. Cloud-native solutions are required to offload
compute-intensive tasks (e.g., 3D rendering and AI training) to high-end
servers in remote data centers when the capability of the local computer is
limited. But the over-dependence on cloud computing could aggravate energy
consumption. Taking cloud gaming as an example, the power demand in the
upstream network together with data center graphic processing was estimated to
be markedly (e.g., 40-60% for desktops and 120-300% for laptops) higher than
that of local gaming [3].

Inevitably, the predominant cloud-based solutions of today lead to exacerbated
energy challenges. Three major components contribute to the energy usage of
data centers: physical servers, cooling systems, and network devices. They
usually consume 40–55%, 15–30%, and 10–25% of the total power respectively. To
offset the strong growth in energy demand, remarkable efficiency improvements
were implemented in servers, storage, network switches, and infrastructure
design. The most efficient data centers (e.g., Google) nowadays reach a power
usage effectiveness (PUE) of $\sim$1.1, while the global average of large data
centers is $\sim$1.57. 444https://www.google.com/about/datacenters/efficiency/

###  II-B Artificial Intelligence

AI plays an important role in endowing the Metaverse with autonomy and
intelligence, thereby improving the QoE of users. For example, Computer Vision
(CV), which derives meaningful information from images and videos, enables the
Metaverse to provide services such as object detection, activity recognition,
avatar navigation, and visual reasoning. Natural Language Processing (NLP)
enables conversational non-playable characters (NPCs) in the Metaverse and
also provides services including language translation, text summarization,
sentiment analysis, and speech recognition. Generative AI utilizes existing
text, audio files, or images to create 3D objects, digital assets (e.g.,
music, painting, article), user avatars, NPCs, and even whole virtual worlds.
Beyond creating innovative services, AI can be integrated with other
technologies to provide more intelligent services, e.g., fast 3D rendering,
efficient communication, and intelligent blockchains.

Despite widespread applications, AI comes at a high cost economically and
environmentally due to high energy consumption. In AI training, computers are
iteratively fed with training data and calculate the ML parameters
continuously. Around 1000 MWh of electricity is spent to train a single
language model like OpenAI GPT-3. An exponential increase of compute used in
the largest AI training was observed in the last decade with a 3.4-month
doubling time.555https://openai.com/blog/ai-and-compute/ Fortunately, unlike
cloud-centric AI training, AI inference is trending to be shifted to
distributed edge devices for higher scalability, lower operational risks, and
reduced latency. However, this results in the additional challenges of limited
processing power and energy supply of edge devices.

To evaluate AI technologies, both accuracy and energy should be considered.
Accuracy metrics are well-defined and easily computed for specific tasks.
However, energy is rarely measurable and difficult to compute. Hence, a few
alternatives have been used. The model size measures the ML model’s memory
consumption, which is strongly dependent on the algorithm and model
architecture. The total number of floating-point operations (FLOPs) is used to
approximate the number of multiply-and-accumulate (MAC) operations, indicate
the computational workload, and imply energy consumption. Nonetheless, both
metrics do not consider the impact of data movement between the memory unit
and the processing unit, which may dominate the energy consumption in the
computation [4]. Thus, a few energy estimation methodologies were developed
for precise energy estimation for specific ML models and hardware devices. The
trade-offs between accuracy and energy are necessary for AI training
considering the diminishing marginal return of accuracy when increasing the
dataset size and model complexity. It is especially true for distributed AI
training, e.g., federated learning (FL), when increasing the concurrency.

TABLE I: Summary of Metaverse Sustainability-Related Metrics Digital Twin | Consumption Point | Unit | Description
---|---|---|---
Power utilization effectiveness (PUE) | Data Center | $\mathbb{R^{+}}\in(1,\infty)$ | ratio of total facility energy to IT equipment energy
Data center energy productivity (DCeP) | Data Center | $\mathbb{R^{+}}\in(0,1)$ | ratio of useful work produced to total energy
IT equipment utilization (ITEU) | Data Center | $\mathbb{R^{+}}\in(0,1)$ | ratio of actual IT power to total rated IT power
Carbon usage effectiveness (CUE) | Data Center | kgCO2/kWh | ratio of CO2 emissions to IT equipment energy
Green energy coefficient (GEC) | Data Center | $\mathbb{R^{+}}\in(0,1)$ | ratio of green energy to total energy consumption
Power at QoS/QoE/QoPE | Wireless system | W | average power at a certain level of quality
Energy efficiency (EE) | Wireless system | bit/J | ratio of data rate to required power
Energy consumption rating (ECR) | Wireless system | W/Gbps | ratio of aggregated energy consumption to capacity
Area Power Consumption (APC) | Wireless system | W/m2 | ratio of power consumption to served area
Spectral efficiency | Wireless system | bps/Hz | ratio of information transmission rate to bandwidth
Area energy efficiency | Wireless system | bps/Hz/m2/J | ratio of area spectral efficiency to energy consumption
Volumetric energy efficiency | Wireless system | bps/Hz/m3/J | ratio of space spectral efficiency to energy consumption
Network level performance indicator | Wireless system | subscribers/W | ratio of number of subscribers on busy hours to power
Artificial Intelligence | Consumption Point | Unit | Description
Model size | ML model | bytes | storage space taken by ML model
Number of parameters | ML model | $\mathbb{N^{+}}\in(1,\infty)$ | number of learnable parameters in ML model
Elapsed time | ML model | s | running time for generating an AI result
FLOPs | ML model | FLOP | number of floating-point operations
FLOPS per watt | Processor | FLOPS/W | ratio of number of floating-point operations to energy
Blockchain | Consumption Point | Unit | Description
Overall annual energy | Blockchain | TWh/yr | overall yearly energy consumption of the network
Energy per transaction | Blockchain | kWh/txn | ratio of overall energy to the number of transactions
Coin carbon footprint | Blockchain | kgCO2/coin | ratio of carbon emissions to number of coins mined
Transaction carbon footprint | Blockchain | kgCO2/txn | ratio of carbon emissions to number of transactions
Extended Reality | Consumption Point | Unit | Description
Power at QoS/QoE/QoPE | HMD | W | average power at a certain level of quality

###  II-C Extended Reality

XR extends physical reality into virtual reality to different extents: AR
overlays virtual objects on the physical world and VR simulates a fully
virtual world. The emergence of XR revolutionizes the Metaverse by creating
brand-new virtual experiences in any imaginable domain. To have an immersive
sensory experience, users wear a standalone HMD (e.g., Oculus Quest) or an HMD
connected to a computer (e.g., Oculus Rift). High-end VR systems include
sensors like accelerometers, gyroscopes, and magnetometers, which can collect
more physical world data. 360∘ 2D videos rendered from 3D models are
transmitted to the edge device or HMD for local processing before displaying
behind the eyes.

VR video rendering is fulfilled mainly in data centers and edge computers due
to the stringent requirements of processing hardware. It is reported to
consume twice as much power as conventional planar video rendering in [5]. In
HMDs, video processing expends the majority of the power budget. Memory access
and computation are two main contributors to video processing energy
consumption. Moreover, VR systems are increasingly used to execute additional
functions, e.g., sensing data collection and processing, AI training and
inference, and caching, further increasing the energy demand.

VR QoS metrics mainly include latency, resolution, field of view (FOV), and
frame per second (FPS). The power at QoS is typically used to compare energy
efficiency. Nevertheless, a device with better QoS does not necessarily
provide better visual quality. For example, perceptually high-quality VR
videos do not require uniformly high-quality pixels (i.e., full resolution)
with a wide FOV. QoE-based energy efficiency metrics encourage the development
of VR systems with better visual quality and less power required. Detailed
examples are illustrated in Section III-C.

###  II-D Blockchain

As a distributed database, the blockchain can be used to store the massive
amount of data generated in the Metaverse, e.g., UGC and IoT data. It
decentralizes storage, improves scalability, reduces data transmission, and
ensures data security and privacy. The development of cross-chain protocols
allows data exchange among blockchains and improves Metaverse
interoperability. Non-Fungible Tokens (NFTs) are unique cryptographic tokens
on blockchain indicating the ownership of digital or real-world items as well
as transaction history. NFTs can ensure uniqueness, track ownership, automate
trading through smart contracts, and establish trust among parties, which
highly motivates the creation of UGC. In contrast, cryptocurrency is adopted
as a fungible digital currency with economic value. It eliminates the need for
central authorities, reduces transaction costs, enhances security and privacy,
and simplifies fund transfers.

Nevertheless, most of the existing blockchain applications are suffering
deficiencies in operation efficiency and energy consumption. Annually, Bitcoin
is estimated to consume 60-125 TWh of electrical energy, which is comparable
to the yearly consumption of countries like Austria and Norway. A single
transaction of Bitcoin requires enough electricity to supply an average size
household in Germany for weeks, which is multiple orders of magnitude higher
than traditional systems. Two factors largely determine blockchain energy
consumption.

Consensus Mechanism: Although there is a growing variety of consensus
mechanisms, the majority of contemporary blockchain applications are based on
Proof-of-Work (PoW) like Bitcoin. PoW involves network nodes solving
computationally intensive puzzles and competing to create a new block, namely
mining. Because miners with more powerful hardware have a higher probability
of success, it creates an arms race among miners and generates electronic
waste.

Figure 2: Emerging and evolving Green Metaverse Techniques in history. (EE:
energy-efficient, EA: energy-aware, Com: communication)

Operation Redundancy: Regardless of the type of consensus mechanisms, the
block is continuously replicated across blockchain network nodes. The required
computation and thus power consumption for data operations could be
problematic when the number of nodes in the blockchain network rises rapidly
resulting in a high degree of redundancy. Compared to centralized databases
with a designed level of redundancy, a blockchain generally consumes much more
than a corresponding centralized solution (by a factor of around 50) [6].

In a holistic view, the idle power consumption of nodes should also be
considered apart from the above factors. To evaluate overall energy
efficiency, energy consumption per transaction is commonly used to compare
blockchains to other technologies. Based on this metric, most contemporary
blockchain applications (e.g., Bitcoin) still spend a mammoth amount of energy
compared to traditional payment systems (e.g., credit cards). Notably, this
metric can be misleading because blockchain energy consumption largely depends
on consensus mechanisms (e.g., mining in PoW) rather than transaction volume.
In a nutshell, the overall energy consumption is still significant for current
blockchains. The situation is expected to change after the world’s second-
largest cryptocurrency, Ethereum, shifts from PoW to a greener proof-of-stake
(PoS) system in Q4 2022.

Lessons learned: Employing energy-inefficient technologies is detrimental to
the Metaverse scalability and results in profound negative impacts on the
environment. The metrics in Table I are of great use to identify inefficient
components in Metaverse networks. In many cases, trade-offs must be well-
balanced between higher energy consumption and improved user experience. As
the Metaverse will be human-centric, QoE-based energy-efficiency measures must
be designed to directly link user experience with energy usage.

##  III Recent Technological Advancements in Energy Efficiency

Numerous research efforts have been put into individual technology for
improving performance, potentially contributing to the Metaverse
sustainability. The most significant improvements and their applications in
the GMN are selected for discussion. A summary is presented in Fig. 2.

###  III-A Digital Twin

####  III-A1 Sensor, User and Edge

In IoT systems, data collected by IoT devices are centralized either in an IoT
gateway connected to the cloud or in a local IoT hub. The primary type of
energy reduction measures focuses on IoT sensors. Because idle sensors consume
unnecessary energy, an efficient duty scheduling algorithm is required to
minimize power consumption while satisfying QoS requirements. The duty cycle
can be designed beforehand based on predictions of communication periods. It
can also be dynamically adjusted based on the real-time priority of traffic,
device energy, and required power. 30-50% energy reduction is obtained by
optimizing the scheduling and workload distribution in [7]. Moreover, novel
sensing concepts including selective sensing, approximate sensing, and
compressed sensing offer additional efficiency improvement in data
transmission.

IoT sensors can be powered by ambient free energy such as light, radio
frequency, temperature difference, and kinetic energy, which is called energy
harvesting. The research community has also worked towards developing energy-
efficient routing protocols, especially energy harvest-based protocols for
WSNs to cope with intermittent renewable resources. The energy-harvesting-
aware routing algorithm proposed by [8] consumes 40-80% less power and reduces
40% packet loss compared to other algorithms by selecting the node with the
highest residual energy level and the minimum cost link. Alternatively, edge
devices can harvest energy provided by a wireless power transfer (WPT) system
when ambient energy is not available. A novel algorithm developed by [9]
jointly optimizes the performance and energy consumption of the WPT-powered FL
system with a significantly reduced computation time.

####  III-A2 Communication Network

Unique features of accessibility, mobility, and scalability make wireless
communication indispensable in the Metaverse. A comprehensive review of future
green radio networks has been conducted by [10].

Heterogeneous networks (HetNets) engage diverse networks in a hierarchical
structure to provide the service to the same user. Macrocells with a higher
capacity provide services for the larger area coverage while small cells with
better efficiencies are used to reduce energy consumption and enhance the
network throughput. Optimizing cell selection and deployment promotes the
HetNet energy efficiency gain. Cell zooming dynamically varies the cell size
based on the traffic load, state of the channel, etc. When the cell is
underutilized, it can transfer the traffic to nearby cells through cell
zooming and enter sleep mode for energy conservation. Beamforming employs an
array of antennas to focus electromagnetic radiation (EMR) toward the user’s
direction. So, the signal strength in a specific direction maximizes, EMR
exposure and transmit power reduces [10]. Massive multiple-input and multiple-
output (mMIMO) is developed based on MIMO by packing more antennas into a
small area. Compared to MIMO, mMIMO improves the capacity by 5 times and
energy efficiency by 100 times due to the focus of radiated energy on user
equipment [10]. Device-to-Device (D2D) communication is a promising technique
that enables direct communication between devices without traversing the
network. Lastly, semantic communication is envisioned as a key component of 6G
for reducing the transmission volume, facilitated by AI technologies. The
extracted meanings of the information are transmitted rather than information
symbols. A semantic communication case study is presented in Section IV.

####  III-A3 Data Center and Cloud

An idle or underutilized server consumes two-thirds of the energy when it is
fully utilized. With virtualization, a physical machine (PM) can host multiple
virtual machines (VMs) independently to avoid under-utilization, which
enhances energy efficiency at the same time. The approach of combining
underutilized servers is called server consolidation. However, non-negligible
energy overhead is introduced for the hypervisor software to manage VMs and VM
migration. Efficient strategies have been proposed for server consolidation,
e.g., a virtual world zone-based strategy for MMORPGs and a resource demand
prediction-based strategy for cloud computing. In general, optimization of the
mapping between PMs and VMs is formulated as the VM placement problem. Job
scheduling determines how efficiently the data center resources are utilized.
Joint optimization of VM placement and job scheduling could lead to more
significant improvement. Apart from IT equipment, the cooling system also
evolves rapidly. For example, the Google AI-empowered cooling system reduced
its energy consumption by 40%.

###  III-B Artificial Intelligence

####  III-B1 Machine Learning Model

An efficient model design contributes to reduced computation and considerable
energy savings. Certain models inherently have lower power requirements, e.g.,
spiking neural networks (SNNs) leverage sparse event-driven spikes and
MobileNet adopts depthwise convolution for reducing computation. Compact
architecture involves trade-offs on architecture hyperparameters. Increasing
complexity results in diminishing marginal returns, and even sometimes hurts
performance. Methods, e.g., Neural Architecture Search (NAS), have been
developed to automate the design for optimal architectures.

Model compression provides an effective way to reduce model sizes and
computation with minimal impact on performance for AI inference. For example,
pruning prunes out the non-critical sections (e.g., connections, neurons, and
layers) in the neural networks. The energy-aware pruning reduces the AlexNet
and GoogLeNet energy consumption by 3.7 and 1.6 times [11]. Quantization
approximates floating-point numbers by low bit-width numbers for ML model
parameters. Model distillation, also called knowledge distillation, trains a
small-size ”student” model to mimic a large-size ”teacher” model. The pruned,
quantized, or distilled model is best suited for AI inference at scale.
Moreover, dataset distillation distills a large dataset into a synthetic
smaller dataset, which provides computation saving for repeated AI training
experiments.

####  III-B2 Computing Paradigm

Computation can be performed in different manners, e.g., distributed computing
and cloud computing. In distributed computing, AI training or inference
workload is fulfilled by multiple worker nodes, which reduces the processing
time and mitigates the single point of failure. Software (e.g., DeepSpeed by
Microsoft) has been developed to reduce computing power and memory use for AI
training.666https://www.deepspeed.ai/ Another advantage of distributed
computing is that it can leverage diverse local resources. For example, FL can
enable workload allocation based on the available intermittent renewable
energy to improve overall sustainability. Edge computing pushes the
computation towards data sources. As only processed data or extracted
information needs to be transmitted, it significantly reduces data volume in
the transmission. However, centralized cloud computing possesses the advantage
of superior energy efficiency from high-end hardware and centralized facility
management. Therefore, the most efficient design could vary from application
to application, depending on the factor (e.g., data transmission or
computation) that is more dominating in the overall energy consumption. Last
but not least, open-sourced pre-trained AI models (e.g., ModelScope by
Alibaba) largely encourage the adoption of Pre-train and Fine-tune Paradigm
and continual learning, which significantly avoids the energy waste in
repeated AI training.777https://modelscope.cn/

###  III-C Extended Reality

####  III-C1 Video Processing

Software and algorithmic improvements play a key role in improving video
processing efficiency. For instance, data reuse is exploited in the
computation of 360∘ VR pipeline. By memorizing head orientation and
establishing a relation between left and right eye projection, some
computations can be skipped. Another improvement opportunity lies in video
encoding, which is performed before videos are transmitted to compress the
video. It effectively reduces the amount of data for storage and transmission.
360∘ VR videos are currently encoded in three ways: equirectangular
projection, cubemap projection, and pyramid projection. Compared to
equirectangular projection, cubemap and pyramid projection can save 25% and
80% file sizes.

####  III-C2 Human Visual Perception

Characteristics and limitations of human visual perception provide
opportunities to improve XR efficiency. Firstly, the fovea, the central area
of the human eye, has a substantially higher visual acuity than the rest of
the retina. In light of this fact, foveated rendering is developed to render
the gazed area of the image at a higher resolution and the peripheral area at
a lower resolution. According to [12], it can achieve 3 times speedup and 70%
pixel reduction. The same concept can also be incorporated with video
compression to reduce the bandwidth and energy required during data
transmission. Secondly, the human visual system has limited capability to
perceive details of high spatial and temporal frequencies. Rather than
rendering all video frames at full resolution, if the resolution of every
other fame is reduced, the perceived quality could be still maintained.
Multiplex video resolution can also reduce both rendering workload and data
transmission. Furthermore, the dark adaptation is another feature of the human
eye that increases visual sensitivity in a dark environment. By smoothly
decreasing the brightness level of the screen, a significant amount of energy
can be saved while keeping the same brightness perception.

###  III-D Blockchain

Figure 3: System model in the virtual transportation network.

####  III-D1 Consensus Mechanism

The first type of green advancement lies in alternative energy-efficient
consensus mechanisms, e.g., PoS, Delegated PoS (DPoS), and Proof-of-Authority
(PoA). PoS replaces miners with validators to validate transactions and add
new blocks to the blockchain. Instead of expending assets up-front as energy
expenditure, validators stake crypto assets as collateral against dishonest
behavior. DPoS, a notable iteration of PoS, allows participants to elect
delegates with voting power proportional to their stakes. Only a capped number
of delegates are chosen for transaction validation and block production. Thus,
it makes consensus more efficient to reach, at the expense of decentrality.
While the above mechanisms are open to the public, PoA pre-approves
participants with trustworthy identities. Participants stake identities and
voluntarily disclose themselves to become validators. The simplicity of
consensus-reaching processes and a smaller number of validators make PoA
require minimal computational effort. In short, eliminating the mining process
(e.g., The Merge by Ethereum) significantly reduces energy consumption by
$\sim$99.95%.888https://ethereum.org/en/upgrades/merge/

####  III-D2 Operation Redundancy

The second type of advancement focuses on reducing operation redundancy,
especially for non-PoW blockchains. Reducing the number of nodes to process
and the workload in the process can both contribute to redundancy reduction.
The sharding technique splits the blockchain network into smaller independent
partitions, i.e., shards, and processes each transaction only on nodes in one
shard rather than the whole network. Therefore the number of redundant
operations can be reduced as well as the energy consumption.

Layer 2 solutions aim to reduce the workload or complexity of the transaction
operation by building a second-layer protocol on top of the foundational
blockchain. For example, the off-chain payment protocol (e.g., Lightning for
Bitcoin and Raiden for Ethereum) creates a direct channel between two
participants and enables instantaneous bidirectional transfers, as long as the
net sum of transfers does not exceed the deposit. Because the blockchain is
involved only when the channel is created or closed, the intensive and
redundant computation required for blockchain operations is largely avoided.
Zero-knowledge rollups (ZK-rollups) bundle transactions into batches and
execute them off-chain. An easily verifiable proof is generated and submitted
back to the blockchain, which proves the correctness of changes after
executing a batch of transactions. All blockchain participants validate the
proof instead of raw transactions, obviously simplifying the workload.
Slightly offset by proof generation, ZK-rollup adoption is estimated to reduce
98.5% of energy demand [13].

Lessons learned: Few industries such as telecommunication and data center
operators have been intensively working on sustainable development. Green AI
and blockchain have taken off in recent years given more widespread adoption.
For XR, research on performance improvement, rather than green technologies,
still dominates.

##  IV Case Study: Stochastic Resource Allocation for Semantic Communication
in the Metaverse

Figure 4: The energy consumption of semantic data and non-semantic data.

This section presents a case study of the virtual transportation network (VTN)
in the Metaverse [14]. For example, a transport company can create a VTN to
provide on-the-job training for its drivers and to conduct large-scale user
studies in a safe but realistic environment. A realistic VTN is developed
through the synchronization of sensing data from edge devices in the physical
world and DTs of the virtual world. To keep DTs updated, sensing data is
regularly transmitted to virtual service providers (VSPs). Fig. 3 illustrates
the system model.

Energy-saving semantic communication: It is energy-consuming to transmit data
from edge devices to VSPs. Specifically, the edge device transmits the
original image to VSPs, while ignoring the semantic importance of the bit
flow. The objective of semantic communication is to be user-oriented, i.e.,
transmit only the part of semantic data interested by VSPs. For example, an
autonomous driving company requires images (vehicles that are driving on the
road) to train detection models in the Metaverse. Therefore, instead of
transmitting the entire captured images which involve other objects, the
semantic extraction module outputs only the segmented objects of interest for
transmission. With the help of YOLO, semantic data such as snapshots can be
extracted from the captured image. For example, after an image is captured by
an edge device, a pre-trained YOLO is used to process and obtain each snapshot
of the corresponding object in the image. However, it is unrealistic for the
VSPs to search manually to identify which semantic data is within their
interest. Therefore, BERT, a powerful pre-trained machine-learning model, is
used to convert the objects’ labels as well as the interest of the VSPs into
vectors according to different contexts. Then, cosine similarity is applied to
measure the similarity between the two vectors. With the similarity score,
resource allocation can be performed to allocate the resources. We can
validate in Fig. 4 that the energy consumption for semantic data transmission
only requires 0.896J as compared to 111J for non-semantic data. Moreover,
inferences on pre-trained semantic extraction model such as YOLO requires very
little energy [15]. Therefore, with semantic communication, edge devices can
reduce power consumption during transmission as well as storage costs.

Figure 5: SIP comparing with EVF and random schemes.

Stochastic resource allocation for semantic communication: To maintain DTs for
realistic VTNs, VSPs may tap into data marketplaces that aggregate data from
edge devices and sell to interested buyers. We assume that the cost of data is
proportional to the energy cost incurred by edge devices in the case study.
There are two types of subscription plans in data marketplaces, namely
”reservation” and ”on-demand”. The reservation plan allows the VSPs to choose
the edge device and purchase the number of data transmissions in advance. We
consider two VSPs and three edge devices (smartphones) around Singapore. The
reservation cost consists of two components, the membership cost and the
transmission cost of one bundle. The VSPs can purchase the number of
transmission bundles only when the membership cost is paid. We consider the
daily rental cost of smartphones as the membership cost, and there are $n$
semantic data transmissions in one bundle. The on-demand plan is an ad-hoc
plan that is only used when needed, so it is more expensive than the
reservation plan. However, the demands of VSPs are constantly changing as it
is highly dependent on the users’ requests. Furthermore, since the reservation
plan is subscribed in advance, the demand of the VSPs may not be the same
after the subscription. For example, the demand is initially the vehicles on
the road. After a while, the detection system may not be able to detect
pedestrians very well under extreme weather conditions. Hence, the on-demand
plan may be triggered to obtain more data to update the AI model. However, if
an incorrect plan is used, e.g., reserve the edge device that transmits
semantic data irrelevant to the interest of VSP, energy is wasted from
unwanted data transmission, and additional energy is required for re-
transmission from other edge devices.

To account for the demand uncertainties of VSPs, we proposed a two-stage
stochastic integer programming method to minimize the operation cost of VSPs
as well as transmission energy. The first stage is the reservation stage,
which consists of two decision variables, i) membership and ii) bundle
variables. The membership variable is a binary variable and it has a
corresponding constraint to ensure that the VSP has to pay the membership fee
to the edge device owner before the VSP can purchase any bundle from the
respective edge device. The bundle variable is a positive variable to indicate
the number of bundles to purchase. The second stage is the on-demand stage,
with an on-demand decision variable. It indicates the number of semantic data
transmissions that VSP is requested on-demand from the edge device after the
demand is observed. A demand constraint is used to ensure that the demand is
met. Additional semantic data transmission is needed from the on-demand plan
(a correction action) whenever the reservation plan has insufficient semantic
data transmission to cover the demand. Using VSP historical demand data, our
resource allocation scheme achieves a much lower cost than other schemes that
do not consider the probability distribution of VSP demand (Fig. 5).

##  V Key Challenges and Future Research Directions

Challenge 1 – QoE-Driven Green Metrics: From Table I, we can observe that most
of the sustainability-related metrics are based on conventional QoS factors.
In contrast, the recent paradigm shift towards the human-centered design of
communication systems will shift the focus from QoS to QoE. Unfortunately, QoE
is highly subjective based on user preferences. This is exacerbated by the
fact that the Metaverse will feature a diverse range of applications and
involve new modalities of user data. As such, before we can design systems to
optimize QoE-driven green metrics, it is important to redefine QoE through
large-scale user studies.

Challenge 2 – Metaverse Sustainability Index: Such index is beneficial for
Metaverse providers to plan an improvement roadmap, for users to choose green
Metaverse products, and for governments to regulate the healthy development of
the industry. However, the enormous amount of technologies and stakeholders
involved makes it challenging to assess the overall sustainability. Although
technologies can be evaluated individually using the metrics in Table I, their
contributions to the Metaverse are still vague currently. Practical frameworks
and tools will be needed in the future to integrate diverse technological
metrics and guide the overall assessment.

Challenge 3 – Trade-Off Balancing System Performance-Energy Consumption: While
research typically focuses on maximization of system performance, e.g.,
attaining the highest accuracy/fastest inference of AI models or highest FPS
in rendering, it is often the case that some applications may not require such
extents of high performance. In this case, the system performance can be
slightly sacrificed to reduce the carbon footprint or energy per transaction.
However, the trade-off between system performance and energy consumption has
to be better studied.

Challenge 4 – New Incentive Schemes for Green Technology Adoption: Incentive
mechanisms designed to encourage green technology adoption, e.g., through tax
credits or grants, have successfully encouraged the adoption of green office
buildings. The next challenge will therefore be how can incentive mechanisms
be designed to extend the coverage to the virtual worlds and the enabling
technologies of the Metaverse.

Challenge 5 – System-Level Design: Diverse technologies are interconnected
together in the Metaverse. On one hand, improving one technology might cause
additional consumption to other technologies, e.g., compressed sensing and
video encoding reduce the data transmission but increase the computation
workload. On the other hand, the full-system design could exploit a larger
energy-saving potential than focusing on individual subsystems, e.g.,
synergistically approximating sensing, computing, memory, and communication.
Therefore, the system-level design is pivotal for orchestrating technological
advancements towards the GMN.

##  VI Conclusion

In this paper, we first present the energy-hungry technologies of the
Metaverse. Then, we discuss recent technological advances for the GMN. We
subsequently present a case study of the semantic communication-enabled
Metaverse development. Finally, we discuss the key challenges and future
research directions.

## Acknowledgement

This research is supported in part by the National Research Foundation (NRF),
Singapore and Infocomm Media Development Authority under the Future
Communications Research Development Programme (FCP), and DSO National
Laboratories under the AI Singapore Programme (AISG Award No:
AISG2-RP-2020-019), under Energy Research Test-Bed and Industry Partnership
Funding Initiative, part of the Energy Grid (EG) 2.0 programme, and under
DesCartes and the Campus for Research Excellence and Technological Enterprise
(CREATE) programme. The research is also supported by the SUTD SRG-
ISTD-2021-165, the SUTD-ZJU IDEA Grant (SUTD-ZJU (VP) 202102), and the
Ministry of Education, Singapore, under its SUTD Kickstarter Initiative (SKI
20210204).

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## Biography

SIYUE ZHANG is currently pursuing a Ph.D. degree with Alibaba Group and
Alibaba-NTU Joint Research Institute, Nanyang Technological University (NTU),
Singapore. His research interests include sustainability, the Metaverse, and
artificial intelligence.

WEI YANG BRYAN LIM is currently Wallenberg-NTU Presidential Postdoctoral
Fellow. His research interests include edge intelligence and resource
allocation.

WEI CHONG NG is currently pursuing a Ph.D. degree with Alibaba Group and
Alibaba-NTU Joint Research Institute, Nanyang Technological University,
Singapore. His research interests include the Metaverse, stochastic integer
programming, and edge computing.

DUSIT NIYATO [IEEE Fellow] is currently a Professor with the School of
Computer Science and Engineering and, by courtesy, School of Physical and
Mathematical Sciences, Nanyang Technological University, Singapore. His
research interests are sustainability, edge intelligence, decentralized
machine learning, and incentive mechanism design.

XUEMIN (SHERMAN) SHEN [IEEE Fellow] is currently a university professor with
the Department of Electrical and Computer Engineering, University of Waterloo.
His research focuses on network resource management, wireless network
security, social networks, 5G and beyond, and vehicular ad hoc networks.

CHUNYAN MIAO [IEEE Fellow] is currently a professor in the School of Computer
Science and Engineering, Nanyang Technological University (NTU), and the
director of the Joint NTU-UBC Research Centre of Excellence in Active Living
for the Elderly (LILY).