Quantitative Analysis of the Reward Rate Disparity
Among Delegators in a DPoS Blockchain
Hidemasa Tanaka

Shiori Hironaka

Kazuyuki Shudo

Tokyo Institute of Technology
Tokyo, Japan

Kyoto University
Kyoto, Japan

Kyoto University
Kyoto, Japan

Abstract—In the blockchain architecture, some selected nodes
create blocks. Various consensus protocols, such as Proof ofWork (PoW), Proof of Stake (PoS), and Delegated Proof of Stake(DPoS), have been proposed to select nodes. DPoS protocol works
similarly to an election, where token holders vote for validator
candidates. Elected validators generate blocks and earn block
generation rewards. BNB Chain using DPoS has implemented
a leveling mechanism to prevent the concentration of votes ona particular validator. However, token holders who have voted
do not always act according to economic rationality. This causes
disparity in the rewards received by token holders. The electoral
process in DPoS causes this disparity; PoS does not have such a
disparity. To quantitatively assess the current state of the levelingmechanism, we investigated the reward rate for the top 1,000
addresses based on the number of tokens they had voted on
the BNB Chain. As a result, we found that token holders who
frequently generated delegate-related transactions earned up to
25% more rewards than those who did not. In addition, we founda greater disparity in reward rates between token holders who
have voted and keep a higher reward rate, compared to those
who did not.
Index Terms—Blockchain, Delegated Proof of Stake, BNB
Chain, reward disparity.

I. I NTRODUCTION
Since the debut of Bitcoin in 2008 [1], its core technology,
blockchain, has been used as a decentralized digital ledger
across various ﬁelds such as ﬁnance [2], healthcare [3], andIoT [4]. One of the fundamental features of blockchain is theconsensus protocol. The consensus protocol plays a role insetting the rules for transaction veriﬁcation and block creation
within the blockchain network. Bitcoin adopted the Proof ofWork (PoW) protocol. Since then, various protocols including
Proof of Stake (PoS) and Delegated Proof of Stake (DPoS)
have been developed [5].
In DPoS, token holders vote for validator candidates througha process similar to an election, selecting a few validators.
Validators generate blocks and earn block generation rewards.
Subsequently, validators distribute block generation rewards todelegators. In this paper, we use the term delegator to represent
the token holder who has voted, to distinguish them from token
holders.
Compared with PoW, DPoS reduces energy consumption [6], and increases the throughput of transactions [7].
Furthermore, DPoS also increases the transaction throughput
compared to PoS [8]. DPoS was proposed in 2014 [9],and since then, many successful blockchain projects such as

EOS.IO [10], TRON [11], and BNB Chain [12] have adopted
it. Sui [13] and Aptos [14], that were derived from Diem, havealso adopted DPoS.
The BNB Chain, one of the successful DPoS blockchains,
includes a mechanism to balance the number of votes toprevent the concentration of votes for a few validators. In thispaper, we refer to this mechanism as a leveling mechanism.
The leveling mechanism encourages delegators to vote forvalidators with fewer votes. This motivates all validators tocreate blocks with the same probability, and delegators to earn
rewards with the same probability. In theory, if all delegators
were perfectly economically rational, their reward rate would
be the same. However, the behavior of delegators is not always
economically rational, and there may be a disparity in rewards
among delegators. This difference is caused by an election-likeprocess that is unique to DPoS and not found in PoS.
In this study, we quantitatively analyze the current state of
the leveling mechanism, focusing on the disparity in rewards
among delegators in the BNB Chain. We show that thegroup of delegators who frequently generate delegate-relatedtransactions earns more rewards than the group of delegators
who do not. In addition, by examining the reward rates ofindividual delegators, we ﬁnd that there is a greater disparity
in reward rates between delegators who keep a higher reward
and who do not.
II. BNB C HAIN
In this section, we describe the foundational knowledge
related to the BNB Chain, which is the subject of our investigation into reward rates.
A. Overview of BNB Chain
As of July 2023, BNB Chain ranks among the world’s
most popular blockchains, with its native token BNB reachinga market capitalization of approximately 38 billion dollars1 .
The BNB Chain is highly active in Decentralized Finance. Its
trading volume on decentralized exchanges is second only to
Ethereum2 .
The BNB Chain consists of two main elements. The ﬁrst is
the BNB Beacon Chain (BC), which handles governance. The
second is the BNB Smart Chain (BSC), which offers features
1 https://coinmarketcap.com/ (accessed 2023-09-15)
2 https://deﬁllama.com/dexs/chains (accessed 2023-09-15)

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

like EVM compatibility, a consensus layer, and multi-chain
hub capabilities.
B. DPoS in BNB Chain
The selection of validators in BSC is implemented throughthe DPoS protocol. Token holders of BNB tokens can votefor validator candidates, and based on these votes, validators
are chosen to create blocks and earn rewards. Token holders
who have voted tokens for validators can receive rewards
distributed by validators.
Token holders can vote any amount of tokens for any
number of validator candidates. Votes are counted daily at
UTC 0:00. Based on the count, the top 21 validator candidates
become the Cabinet, while those ranked 22nd to 29th become
the Candidates. Note that here, Candidates are a speciﬁc groupmentioned in BEP-131, not the same as general validator
candidates [15]. For each epoch, members of the Cabinet are
more likely, while Candidates are less likely, to be selected as
one of the 21 validators. This results in higher rewards for the
Cabinet and lower rewards for the Candidates.
Blocks are generated every three seconds, and validators
receive BNB tokens as rewards, which are sourced from thetransaction fees contained within those blocks. Each validator
sets its commission rate for reward distribution, and the net
block generation rewards after commission are distributed todelegators on a daily basis. The reward for delegator i on
day d voting for validator v is calculated using the following
equation:()
Vi,dRi,d = (Rv,d × (1 − Cv,d )) ×(1)
Vv,d
where Rv,d is the block generation rewards for validator v on
day d, Cv,d is the commission rate of validator v on day d,
Vi,d is the amount of tokens voted by delegator i for validator
v on day d, and Vv,d is the total amount of tokens voted byany delegators for validator v on day d.
In the BNB Chain, there exists a leveling mechanism.
This ensures that validators within the Cabinet have an equalchance to earn block generation rewards, no matter how many
votes they have. Similarly, validators among the Candidates
also have an equal chance to earn block generation rewards
within their group. However, the chances of earning rewards
are not the same for validators in the Cabinet and those in
the Candidates. In other words, the value of Rv,d remains
constant either within the Cabinet or among the Candidates.
This mechanism activates an economic incentive to vote forvalidators with fewer votes within the same Cabinet or among
the Candidates. This is because, with Rv,d being constantand assuming equal commission rates Cv,d , delegators can
get higher rewards Ri,d by voting for validators with fewer
voted tokens Vv,d . This leveling mechanism aims to prevent
vote concentration on a few validators and to stop a few fromcontrolling elections due to large token holdings. Under thismechanism, assuming that delegators make rational economic
decisions, the rewards received by delegators should statisti-cally be the same regardless of which validator is chosen forvoting.
However, it is unclear how much delegators actually behave rationally. Even if the vote counts among validators
temporarily approach similar levels, various factors can stillcreate disparity in the vote counts between validators. These
include changes in the election process and new votes fromrecent token holders. Therefore, the presence of delegators
who switch their vote in response to imbalances in the numberof votes validators receive relative to the block generation
rewards they obtain, is essential. These delegators keep theirown rewards higher and help bring the vote counts amongvalidators closer to similar levels.
III. R ELATED W ORK
Since the development of DPoS, numerous studies havebeen conducted on attacks speciﬁc to DPoS blockchains.
Hasanova et al. [16] have studied speciﬁc vulnerabilities in
DPoS blockchains. They point out risks such as exploiting low
voter turnout by large token holders and the threat of block
producers colluding.
Against this backdrop, Liu et al. [17] investigated EOS.IO,
one of the DPoS blockchains. They revealed that voting power
is concentrated among a few voters and that there is suspicionof collusion between several validators.
Additionally, Wang et al. [18] proposed a method to reducethese risks by using clustering algorithms to categorize voting
nodes. Based on these results, they provide more rewards
to nodes that actively participate in voting. This approach
deliberately creates a reward disparity to encourage nodeparticipation in voting.
However, in the BNB Chain, vulnerabilities can arise not
only from the rate of voting participation but also from theimbalance of vote counts. Additionally, reward disparity canalso lead to further centralization of BNB tokens. Therefore,
the leveling mechanism for the number of votes is crucial. To
the best of our knowledge, this is the ﬁrst work to quantitatively investigate the current state of the leveling mechanism.
We quantitatively analyze the disparity in rewards in the BNB
Chain.
IV. DATATo analyze the disparity in rewards, we prepared data on
voted balance and rewards for each delegator. We collected
delegate-related transactions from BNB Beacon Chain Explorer3 . Delegate-related transactions refer to the following
three types:• Delegate Smart Chain Validator
Voting BNB tokens to validator candidates.
• Redelegate Smart Chain Validator
Changing the vote of BNB tokens from one validator
candidate to another.
• Undelegate Smart Chain Validator
Removing the vote of BNB tokens that are currently
voted.
3 https://explorer.bnbchain.org/ (accessed 2023-09-15)

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

Delegate-related transactions contain a variety of information.
To analyze the voted balance, only the timestamp, delegator address, validator address, and delegation amount ofDelegate Smart Chain Validator and Undelegate Smart Chain
Validator transactions are used. This is because the voted
balance does not change by Redelegate Smart Chain Validator
transaction.
We collected delegate-related transactions between August
16, 2020 and May 31, 2023. As a result, we obtained atotal of 709,133 transactions. Based on these transactions, we
calculated the voted balances for each delegator.
The voted balance Vi,d for delegator i on day d is calculated
as follows:
Vi,d = Adelegate,i,d − Aundelegate,i,dwhere Adelegate,i,d is the total amount from the Delegate
Smart Chain Validator transactions generated by delegator i
before day d, and Aundelegate,i,d is the total amount from theUndelegate Smart Chain Validator transactions generated bydelegator i before day d. If tokens are voted for multiple
validator candidates, the sum of all such votes is considered.
Next, to calculate rewards, we collect data on delegatereward transactions. To determine the target delegators for datacollection, we calculated the voted balance for each delegatoras of May 31, 2023, based on the delegate-related transactions.
From these calculations, we identiﬁed the top 1,000 delegators
in terms of their voted balances as of May 31, 2023. We
then collected delegate-reward transactions for these top 1,000
delegators. As a result, we collected 1,616,939 delegate-reward
transactions.
Ri,m , the total reward for delegator i in month m, iscalculated as the total amount of rewards contained in thedelegate-reward transactions generated in month m.
V. A NALYSIS
We investigated the disparity in rewards between delegatorson the BNB Chain. Delegators who receive larger rewards
relative to their voted balances are likely employing strategies
to keep a higher reward rate. We then hypothesized thatsuch delegators would need to change their voting destination
frequently. Therefore, we examined the relationship between
the number of delegate-related transactions and the rewards
received by the delegators.
To evaluate the rewards relative to the voted balance,
we calculated the reward rate for each delegator. First, we
identiﬁed delegators and subsequently calculated their monthly
reward rates.
We identiﬁed delegators who maintained a voted balance ineach month from January 2022 to May 2023, among the top
1,000 delegators based on their voted balances as of May 31,
2023. Subsequently, we calculated the reward rate for thesedelegators based on their consistent votes.
The reward rate ri,m for delegator i in month m is calculated using the following equation:
(
)
365
Ri,m
×
× 100
(2)
ri,m =
Vi,mNm

Fig. 1: Average reward rate of delegators grouped by monthly
delegate-related transaction frequency from January 2022 to
May 2023. This shows that delegators with zero delegaterelated transactions had a lower average reward rate than thosewith one or more delegate-related transactions for all periods,
except for May 2022.
TABLE I: Average reward rates of delegators from January2022 to May 2023, grouped by monthly delegate-relatedtransaction frequency.
Date2022-012022-022022-032022-042022-052022-06
2022-072022-082022-092022-102022-112022-122023-012023-022023-032023-042023-05

Tx : 0
9.61
8.81
7.53
7.43
6.96
5.86
5.44
4.63
4.32
4.37
4.09
2.90
2.69
3.26
2.96
3.12
3.09

Tx : 1
9.75
9.03
7.60
7.51
6.91
6.00
5.60
4.73
4.40
4.47
4.30
3.05
2.80
3.49
3.37
3.38
3.35

Tx : 2
10.06
9.15
7.80
7.66
7.25
6.30
5.61
4.79
4.41
4.57
4.61
3.15
2.80
3.49
3.60
3.64
3.43

Tx : 3
9.80
9.46
7.96
7.68
7.24
6.22
5.82
4.85
4.47
4.67
4.57
3.10
2.92
3.50
3.70
3.77
3.38

Tx : 4+
10.07
9.58
7.96
7.86
7.37
6.30
5.81
4.82
4.48
4.69
4.79
3.13
2.89
3.60
3.75
3.69
3.40

Fig. 2: Average reward rate ratios of delegators grouped bymonthly delegate-related transaction frequency from January2022 to May 2023. This shows that the average reward rate
for delegators with four or more delegate-related transactions
was up to approximately 25% higher than that for delegatorswith zero transactions.

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

where Ri,m is the total rewards earned by delegator i in monthm, calculated from the delegation amount in the delegatereward transactions. Vi,m is the sum of the daily voted balances
Vi,d for each day of month m for delegator i. Nm is thenumber of days in month m. The reward rate ri,m is calculated
under the assumption that both the rewards and voted balance
for delegator i would continue for a year, thus the formula
365
includes a multiplication by N.
m
To analyze the relationship between the number of delegaterelated transactions generated and the average reward rate ofdelegators, we categorized delegators into ﬁve groups based on
their transaction frequency: 0, 1, 2, 3, and 4 or more times.
We then calculated the average reward rate for each group.
Fig. 1 shows the average reward rate of delegators groupedby the number of generated transactions. See Table 1 for detailed values. Fig. 1 shows that delegators with zero delegaterelated transactions had a lower average reward rate thanthose with one or more delegate-related transactions for all
periods, except for May 2022. This suggests that delegators
who frequently generate delegate-related transactions tend to
get their rewards effectively.
Fig. 1 also shows that the decline in the average reward rate
throughout 2022. This results from the decrease in the overall
transaction volume and fees on the BNB Chain, as well as
the stability in the total number of tokens voted. Transaction
fees on the BNB Chain are shown in Fig. 3. The total numberof BNB tokens voted is shown in Fig. 4. This has made itless clear how reward rates differ among different transaction
frequency groups.
To clarify the differences in monthly delegate-related transaction frequencies, we calculated the ratio between the reward
rate for other transaction frequencies (rother ) to the reward
rate for the group with zero transactions (r0 ). The ratio ρ is
calculated using the following equation:ρ=rother.
r0(3)

The results are shown in Fig. 2. We observed that the
average reward rate for delegators with four or more delegaterelated transactions was up to approximately 25% higher thanthat for delegators with zero transactions. This result is shownin March 2023, the number of votes for different validators
varied signiﬁcantly. Further analysis of Fig. 2 suggests that the
reward disparity between frequent and infrequent delegators
was widening over time. If this trend continues, it could
increase the risk of attacks.
From Figs. 1 and 2, it is clear that there exists a differencein average reward rates between delegators who generated
delegate-related transactions frequently and those who did
not. However, not all delegators in the group with frequentdelegate-related transactions are employing a strategy to keepa higher reward rate. Therefore, an analysis of individual
delegators’ reward rates is needed to understand the disparity
in rewards between those employing a strategy and those who
do not.

Fig. 3: Total amount of BNB tokens paid as transaction fees oneach day for the BNB Smart Chain network. The transaction
fees have been decreasing throughout 2022. Data sourced from
BscScan4 .

Fig. 4: Total number of BNB tokens voted across the BNB
Chain. The voted balance has been largely stable throughout
2022. Data sourced from BscScan4 .

We used the latest data to investigate the difference inreward rates among individual delegators. Speciﬁcally, weused data from February 2023 to May 2023. The delegators
investigated in Fig. 5 is the same as those in Figs. 1 and
2, meaning they maintained a voted balance in the month.
We then calculated the reward rates for each delegator andclassiﬁed them based on the frequency of delegate-relatedtransactions. Fig. 5 shows the number of delegators by reward
rate, with data from February 2023 to May 2023, respectively.
The dotted lines in Fig. 5 indicate the average reward rate
for each frequency of delegate-related transactions, consistent
with the values reported from February to May 2023 in Fig. 1.
Fig. 5 shows that the delegators’ reward rates are widelydistributed regardless of the frequency of delegate-relatedtransactions. This indicates that the majority do not necessarily
employ a strategy to keep a higher reward rate, even within thegroup of delegators with frequent delegate-related transactions.
4 https://bscscan.com/charts (accessed 2023-07-15)

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

(a) February 2023.

(b) March 2023.

(c) April 2023.

(d) May 2023.

Fig. 5: Number of delegators by reward rate, grouped by different counts of monthly delegate-related transactions. The dottedlines indicate the average reward rate for each frequency of delegate-related transactions. These ﬁgures show that the delegators’
reward rates are widely distributed regardless of the frequency of delegate-related transactions.

Fig. 5 also shows that delegators who generate a lower
number of transactions can get higher rewards. In fact, in
April 2023, the delegator who got the highest reward rate did
not generate delegate-related transactions. In other months,
the difference in the highest reward rate between the groupwith zero delegate-related transactions and other groups wasless than 0.15%. This suggests that generating delegate-relatedtransactions and optimizing the votes does not necessarily leaddelegators to a higher reward rate than those who do not. In
other words, not changing the votes can sometimes reduce
the reward rate. This is because delegators need to change
their vote to maintain their reward rate only when there is
an imbalance in the number of votes received by validators
relative to the block generation rewards. Such imbalances
occur when there is a signiﬁcant inﬂux of votes or when
there are changes in the status of validator candidates, such as
cabinet, candidate and another. However, these opportunities
are infrequent. Consequently, most delegators do not need tochange their vote frequently to achieve a higher reward rate.
Furthermore, the highest reward rate achieved by a delegatorin a month was at most 5.1–5.2% in Fig. 5b and at least 4.0–
4.1% in Fig. 5d. The lowest reward rate in Fig. 5 was between
0.0–0.1%. This implies that delegators employing a strategy

to keep a higher reward rate likely earned rewards aroundthese highest percentages, whereas those not doing so had aminimum reward rate of 0.0–0.1%. From these ﬁndings, it
is clear that there is a signiﬁcant disparity in reward rates.
While Fig. 2 showed an average reward rate difference of up
to approximately 25% between groups segmented by delegaterelated transactions as of March 2023. Therefore, the data from
Fig. 5 suggests that reward rate disparity is even larger at theindividual level.
Note that while this paper uses data from February 2023 to
May 2023 as examples, signiﬁcant disparities in reward rates
were observed among delegators in other months from January2022 to March 2023.
Moreover, in May 2023, the top 29 validator candidates
maintained their positions as top candidates, and no other candidates managed to break into the top 29 rankings. Therefore,
we created Fig. 6, which indicates the number of validators
by the reward rates they would offer if continuously voted
throughout May 2023. Fig. 6 shows that no validator candidates offered a reward rate between 0.0–1.3%. Consequently,
among the delegators with zero delegate-related transactions,as indicated in Fig. 5d, those who received a reward rate
between 0.0–1.3% were likely to have continuously voted forProc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

Fig. 6: Number of validators by the reward rate they would
offer if continuously voted throughout May 2023. This showsthat no validator candidates offered a reward rate between 0.0–
1.3%.

validator candidates who distributed no rewards in May 2023.
Additionally, a signiﬁcant portion of delegators had nodelegate-related transactions: 59% in February 2023, 54% in
March 2023, 59% in April 2023, and 64% in May 2023,
according to Fig. 5. Considering that our study focuses on
the top 1,000 delegator addresses by voted balance, these
individuals are likely to have a stronger motivation to keepa higher reward rate than addresses with fewer voted tokens.
For this reason, the fact that a majority of delegators had zerodelegate-related transactions is surprising.
We discuss the inﬂuence of reward rate disparity. In the
short term, an imbalance in the number of votes for validator
candidates and the disparity in reward rate can increase the riskof centralizing control over the election process in the hands ofa few token holders with large numbers of tokens. In the longterm, the reward rate disparity can lead to the centralization of
BNB tokens. In PoS, token centralization remains a concern
although staking rewards are distributed almost uniformly [19].
Therefore, more intense token centralization can occur in the
BNB Chain, where signiﬁcant disparities in reward rate among
delegators exist.
VI. C ONCLUSIONBNB Chain using DPoS has implemented a leveling mechanism to balance the number of votes to prevent the concentration of votes for a few validators. Theoretically, if
all delegators behave perfectly economically rational, theirreward rate would be the same. However, in practice, thebehavior of delegators is not always economically rational,
and there is a reward rate disparity among delegators. This
study quantitatively investigated the disparity in rewards in
the BNB Chain.
We focused on the number of delegate-related transactions
generated as an indicator for evaluating the economic rationality of delegators. We compared monthly average reward rates of the delegators by counting delegate-relatedtransactions from January 2022 to May 2023. The results

showed that delegators who generated four or more delegaterelated transactions earned up to 25% more rewards thanthose who generated no transactions on a monthly average.
In other words, we found a 25% difference in reward rates
between delegators who were considered to be acting in an
economically rational manner, and those who were not. To
further examine the differences in reward rates for individual
delegators, we calculated the number of delegators by reward
rate from February to May 2023. The data showed a wide
distribution of delegator reward rates. Across all months, theminimum reward rate was consistently between 0.0–0.1%,
while the maximum reward rate varied each month, generally
around 5.0%. This result indicates that even though the numberof delegate-related transactions is the same, there is a large
disparity in reward rates among delegators. This suggests thatsome delegators have a strategy to keep a higher reward rate
and others do not.
An imbalance in the number of votes for validator candidates and the disparity in reward rates can increase the risk ofcentralizing control over the election process in the hands ofa few token holders with large numbers of tokens in the shortterm. In the long term, this reward rate disparity can lead tofurther centralization of BNB tokens.
We evaluate the impact of the disparity in reward rate on
the blockchain network. Firstly, disparity in reward rate andthe number of votes can lead to variations in the difﬁcultyof becoming part of the same Cabinet or Candidates. This increases the risk of large token holders controlling the elections.
Secondly, tokens may concentrate in delegators who keep
higher rewards. This concentration beneﬁts the blockchain
network by increasing the voting power of delegators who
contribute to the election. However, this concentration also
reduces the distribution of token balances, which reﬂects thedecentralization of DPoS blockchains [20].
Our study offers the ﬁrst quantitative analysis of reward rate
disparity, and we anticipate that these ﬁndings will contributeto the design and improvement of DPoS.
In future work, we plan to compare the current numberof votes with the theoretical optimal number of votes. The
optimal number of votes could be determined based on avalidator’s current ranking and commission rate. This will
provide direct insight into the current state of the levelingmechanism.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Number JP21H04872.
R EFERENCES[1] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2008.
[2] Li Zhang, Yongping Xie, Yang Zheng, Wei Xue, Xianrong Zheng,
and Xiaobo Xu. The challenges and countermeasures of blockchain
in ﬁnance and economics. Systems Research and Behavioral Science,37(4):691–698, 2020.
[3] Mohsen Attaran. Blockchain technology in healthcare: Challenges
and opportunities. International Journal of Healthcare Management,
15(1):70–83, 2022.

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024

[4] Qin Wang, Xinqi Zhu, Yiyang Ni, Li Gu, and Hongbo Zhu. Blockchain
for the IoT and industrial IoT: A review. Internet of Things, 10:100081,
2020.
[5] Seyed Mojtaba Hosseini Bamakan, Amirhossein Motavali, and Alireza
Babaei Bondarti.
A survey of blockchain consensus algorithms
performance evaluation criteria. Expert Systems with Applications,154:113385, 2020.
[6] J. Sedlmeir, H.U. Buhl, G. Fridgen, et al. The energy consumption ofblockchain technology: Beyond myth. Business & Information Systems
Engineering, 62:599–608, 2020.
[7] Qian Hu, Biwei Yan, Yubing Han, and Jiguo Yu. An improved delegatedproof of stake consensus algorithm. Procedia Computer Science,
187:341–346, 2021.
[8] Sheikh Munir Skh Saad, Raja Zahilah Raja Mohd Radzi, and Siti Hajar
Othman. Comparative analysis of the blockchain consensus algorithm
between proof of stake and delegated proof of stake. In 2021 International Conference on Data Science and Its Applications (ICoDSA),
pages 175–180, 2021.
[9] D. Larimer. Delegated proof of stake (DPoS) white paper by daniel
larimer, 2014. https://example.com/whitepaper.pdf.
[10] Block.one.
Eos.io technical white paper v2, 2018.
https://github.com/EOSIO/Documentation/blob/master/TechnicalWhitePaper.md.
[11] T. Foundation. Tron: Advanced decentralized blockchain platform, 2018.
https://tron.network/static/doc/white paper v 2 0.pdf.

chain,
2020.
[12] Binance.
Binancesmart
https://github.com/binance-chain/bsc.
[13] Sui Documentation. Sui: Concepts. https://docs.sui.io/concepts, 2023.
[14] Aptos Documentation.
Aptos developer documentation.
https://aptos.dev/, 2023.
[15] Bep-131: Introduce candidate validators onto BNB Smart Chain.
https://github.com/bnb-chain/BEPs/blob/master/BEPs/BEP131.md.
[16] Huru Hasanova, Ui-jun Baek, Mu-gon Shin, Kyunghee Cho, and MyungSup Kim. A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. International Journal of Network Management,
29(2):e2060, 2019.
[17] Jieli Liu, Weilin Zheng, Dingyuan Lu, Jiajing Wu, and Zibin Zheng.
Understanding the decentralization of DPoS: Perspectives from datadriven analysis on EOSIO. CoRR, abs/2201.06187, 2022.
[18] Lijun Wang, Pinghai Xu, Wei Su, Yang Li, and Xianzhong Chen.
Research on improvement of blockchain dpos consensus mechanism
based on HK clustering. In 2021 China Automation Congress (CAC),
pages 1167–1172, 2021.
[19] Yuming Huang, Jing Tang, Qianhao Cong, Andrew Lim, and Jianliang
Xu. Do the rich get richer? fairness analysis for blockchain incentives.
In Proceedings of the 2021 International Conference on Managementof Data, pages 790–803, 2021.
[20] Yongpu Jia, Changqiao Xu, Zhonghui Wu, Zichen Feng, Yaxin Chen,
and Shujie Yang. Measuring decentralization in emerging publicblockchains. In 2022 International Wireless Communications andMobile Computing (IWCMC), pages 137–141, 2022.

Proc. 2024 IEEE Int'l Conf. on Big Data and Smart Computing(IEEE BigComp 2024), pp.136-142, February 2024