Identifying Impacts of Protocol and Internet
Development on the Bitcoin Network
Ryunosuke Nagayama, Ryohei Banno† , and Kazuyuki Shudo
Tokyo Institute of Technology
Tokyo, Japan
nagayama.r.ac@m.titech.ac.jp

Abstract—Improving transaction throughput is an important
challenge for Bitcoin. However, shortening the block generationinterval or increasing the block size to improve throughput makes
it sharing blocks within the network slower and increases thenumber of orphan blocks. Consequently, the security of theblockchain is sacrificed. To mitigate this, it is necessary to reducethe block propagation delay. Because of the contribution of new
Bitcoin protocols and the improvements of the Internet, the blockpropagation delay in the Bitcoin network has been shortened inrecent years. In this study, we identify impacts of compact block
relay—an up-to-date Bitcoin protocol—and Internet improvement on the block propagation delay and fork rate in the Bitcoin
network from 2015 to 2019. Existing measurement studies could
not identify them but our simulation enables it. The experimental
results reveal that compact block relay contributes to shortening
the block propagation delay more than Internet improvements.
The block propagation delay is reduced by 64.5% for the 50th
percentile and 63.7% for the 90th percentile due to Internet
improvements, and by 90.1% for the 50th percentile and by
87.6% for the 90th percentile due to compact block relay.
Index Terms—Bitcoin, blockchain, propagation delay, simulation

I. I NTRODUCTION
Blockchain is a distributed system with Byzantine fault
tolerance. Because blockchain can manage a distributed ledger
without a centralized system and has difficulty for tamperingwith past data, blockchain is used as the core technology for
cryptocurrencies. However, Proof-of-Work (PoW), an algorithm used in many blockchains such as Bitcoin, has the limitation that it can process only a small number of transactions
at a given time.
One solution to this problem is to shorten the block generation interval; however, this sacrifices blockchain security[1]. If the block generation interval is shortened, it becomesdifficult to share the block in the network, and inconsistency
in the blockchain arises. Therefore, it is essential to reduce theblock propagation delay to share a block in a short interval.
According to a monitoring web site [2], the Bitcoin blockpropagation delay has been shortened in recent years. The
block propagation delay has decreased from more than 5seconds in 2015 (until 50% of peers received a block) to lessthan 1 second in 2019. Further, the 90th percentile decreased
from more than 15 seconds in 2015 to approximately 2 seconds
in 2019.
† Current affiliation as of June 2020: Kogakuin University

There are several reasons for the decrease in the blockpropagation delay [3]. The first reason is the use of relay
networks, such as Falcon [4] and FIBRE [5], which are
structured to efficiently propagate blocks and transactions.
Second, development of the Bitcoin protocol, such as compact
block relay (CBR) [6], reduce the block size and enablehigh-speed block transmission by sending only the transaction ID, not the entire transaction. Furthermore, improvements
to the Internet have reduced network latency between peers,
increased bandwidth, and reduced data communication time[7], [8].
Otsuki et al. [9] analyzed the impact of relay networks on
the block propagation delay and fork rate by simulating a relay
network on the Bitcoin network using the blockchain network
simulator SimBlock [10], [11]. However, there has been noquantitative analysis of the impacts of protocol development
such as CBR and Internet improvements on reducing theblock propagation delay. Existing measurement studies could
not identify impacts of them because measured bandwidths
and latencies reflect them together. In this study, we identify
impacts of compact block relay and Internet improvements on
the block propagation delay and fork rate in 2015 and 2019by simulating them.
The remainder of this paper is organized as follows. In
Section II, we provide an overview of Bitcoin, while in
Section III we describe a simulator, a procedure for calculating
network parameters, and CBR models. In Section IV, we
discuss our experimental results, and in Section V, we provide
conclusions and present ideas for future work.
II. B ITCOIN N ETWORK
A. Block generation
Bitcoin uses a data structure called a block to record transactions. Nodes store transactions broadcast to the network ina memory pool and generate blocks from them. The generated
blocks are broadcast and the receiving node validates andadds them to the blockchain. In addition to the transaction,a block contains the hash value of the previous block, whichmakes the blockchain a history of successive transactions.
A block also contains a value called a nonce. In the PoW
algorithm adopted by Bitcoin, generating a block is equivalent
to identifying a block whose total hash value falls below
a certain threshold while changing the nonce. This process
is called mining. Currently, the block generation interval is

Proc. 25th IEEE Symposium on Computers and Communications(IEEE ISCC 2020), July 2020

Node A

Node B

Node A

Node B

Node A

Block

Block

Node B

sendcmpct(1)sendcmpct(0)TABLE IPARAMETER SETTINGS OF BITCOIN NETWORK .

Block
cmpctblock

Validation

getblocktxn
inv

inv

getdatagetdata

Blockcmpctblock

blocktxn

Number of nodes
Block generation interval
Block size
Hash power
Number of neighbors
Geographical distributionof nodesNetwork latency
Network bandwidth

6000 (2015) or 9000 (2019)10 min535 KB (2015) or 1.0 MB (2019)Gaussian distribution
Distribution according to Miller et al. [13]Distribution according to Bitcoin
Six regional network latencies
Six regional bandwidths

getblocktxn

TABLE II

blocktxn

Legacy Protocol

Low Bandwidth
Relaying

SOURCES OF NETWORK PARAMETERS .

High Bandwidth
Relaying

Fig. 1. Block propagation in legacy protocol and compact block relay.

adjusted to approximately 10 min according to the difficultythat determines the threshold.
However, the blockchain may be forked by generating
multiple blocks from the same parent block. In this case,receiving nodes select a block with the largest total difficultyof all blocks up to that block as the head block. A blockchainthat comprises ancestor blocks of the head block is considered
to be a legitimate blockchain.
If malicious nodes attempt to change an approved transaction in a past block, nodes must generate blocks until the totaldifficulty exceeds that of the current legitimate blockchain.
Therefore, the longer the chain is between the block containing
the transaction to be changed and the current head block, themore difficult it is to change the transaction.
B. Block propagation
When a node generates a block by mining, the node sends
the generated block to neighboring nodes. There are currently
two main protocols for block propagation in Bitcoin: thelegacy protocol developed for the first implementation of
Bitcoin and CBR.
In the legacy protocol, nodes propagate all transactions contained in a block, which requires significant network resources,
typically close to 1 MB (106 bytes) per block [12]. In contrast,
CBR is a protocol for reducing the amount of bandwidth
used to propagate new blocks to nodes. Fig. 1 displays theblock propagation flow in the legacy protocol and CBR. In
the legacy protocol, after receiving and verifying a new block,node A sends an inv message containing the block’s metadata
to node B. If node B has not received the block, it sends
a getdata message to node A requesting the entire blockincluding the approved transactions. In contrast, CBR only
sends a compact block containing block headers and approved
transaction indices. If the receiving node fails to reconstruct
a block from these data (i.e., if there is no transaction in thereceiving node’s memory pool), the receiving node requests
the missing transactions from the sender node.
CBR has two optional protocols: low bandwidth relaying
and high bandwidth relaying. In low bandwidth relaying, node

Geographical distributionof nodesNetwork latency
Network bandwidth

2015
Bitnodes [14]2019
Bitnodes [14]Verizon [15]testmy.net [8]WonderNetwork [7]testmy.net [8]A sends an inv message. In high bandwidth relaying, theconfirmation that node B has already received the block isskipped, and node A sends the compact block as soon as theblock is received before completing block validation.
III. S IMULATING B ITCOIN NETWORK
We measured the block propagation delay using a simulator.
A simulator was used rather than an actual environment for
the following three reasons:• The cost is lower than that of setting up nodes andbuilding a network.
• The parameters, such as the number of nodes, network
latency, and bandwidth can be easily changed.
• The impact of each factor on the block propagation delay
can be distinguished and evaluated.
In this study, we used the blockchain simulator SimBlock[10], [11]. Because SimBlock can simulate block propagation
between nodes, it can measure the block propagation delay.
Table I presents the Bitcoin network parameters used in our
simulation.
A. Network parameters
We used the network parameters presented in Aoki et al.
[10] as network parameters for 2015 and calculated new
network parameters for 2019. Table II presents sources of
network parameters for 2015 and 2019. The network parameters included the node distribution, network latency, andbandwidth. The calculation procedure of network parametersfor 2019 is as follows.
a) Node distribution: We used data on the number ofnodes in each country from Bitnodes [14]. Because SimBlock
has six regions (North America, Europe, South America, Asia,
Japan, and Australia), the node distribution was calculatedusing the number of nodes in each region.
b) Network latency: We selected a major city in the
country (one city each in the east and west only for the UnitedStates), and obtained data on the network latency between
the cities from WonderNetwork [7]. The weighted average ofCBR Utilization Rate
Compact Block Size
Ratio of Churn Node
Churn Node Block Reconstruction Failure Rate
Control Node Block Reconstruction Failure Rate

0.964
18 KB
0.976
0.27
0.13

TABLE IV
50 TH AND 90 TH PERCENTILE OF BLOCK PROPAGATION DELAY
IN REAL NETWORKS AND A SIMULATION .

50% delay measured
50% delay simulated
90% delay measured
90% delay simulated

2015 Bitcoin network
7,988 ms
9,673 ms
16,835 ms
14,056 ms

2019 Bitcoin network
401 ms
1,340 ms
2,353 ms
2,364 ms

the network latencies including the number of nodes in eachcountry was used as the network latency between regions.
c) Bandwidth: We used the bandwidth data of eachcountry from testmy.net [8]. The weighted average of the
bandwidths, taking into account the number of nodes in eachcountry, was used as the bandwidth of each region.
B. Compact block relay model
Table III presents the parameters related to CBR. We
obtained the protocol version of each node from Bitnodes
and adopted the percentage of nodes using the version of the
protocol implementing CBR as the percentage of nodes using
CBR.
Nodes using CBR should only behave as low bandwidth
relaying.
According to Imtiaz et al. [16], the failure rate of blockreconstruction differs between the control node (the node thatis always connected to the network) and the churn node (thenode that repeats connection and disconnection). The ratioof the control node and churn node and the failure rate ofblock reconstruction in low bandwidth relaying are based on
the paper [16]. Fig. 2 shows the cumulative distribution of
the failure block size. The failure block size, i.e., the data
size to be downloaded upon reconstruction failure, follows thefollowing cumulative distribution calculated from the numberof missing transactions measured in the paper [16]. Pchurnand Pcontrol represent the probability that the failure block
size is larger than a given rate r of the block size for churnnode and control node.
3

Pchurn = e−2.12×10 r(r ≥ 0) (1)4

Pcontrol = 1 − 0.0964 log(2.89 × 10 r + 1)

(r ≥ 0) (2)IV. R ESULTS AND D ISCUSSION
Here, we present the results of our evaluation.
A. Simulator validation
To experimentally validate our simulation, we compared the
Bitcoin network in 2015 and 2019 with their respective simulated counterparts. In the 2015 Bitcoin network simulation, thePchurn, Pcontrol (Failture Block Size / Block Size > r)

TABLE III
PARAMETER SETTINGS OF COMPACT BLOCK RELAY (CBR).

1.0

Control Data
Control Fitted
Churn Data
Churn Fitted

0.8
0.6
0.4
0.2
0.0
0.0

0.2

0.4

r

0.6

0.8

1.0

Fig. 2. Cumulative distribution of the failure block size.

number of nodes was 6000, the block size was 535 KB, andthe Internet parameters from 2015 were used. In the simulationof the 2019 Bitcoin network simulation, the number of nodes
was 9000, the block size was 1.0 MB, and CBR on Internet
from 2019 was simulated.
Table IV presents the simulation results and the measurement results of the block propagation delay obtained from amonitoring web site [2]. The measured values are the monthly
averages for July 2015 and October 2019.
Both 90th percentile simulated results were similar to themeasurement results. However, the 50th percentile results werelarger than the measurement results.
This is likely due to the impact of the relay network. A relay
network can perform block propagation efficiently to relay
network participants using a relay server. In our simulation,
the network is a random network without a relay network;
therefore, it takes several hops to reach 50% of the networkfrom the block generation nodes. When using a relay network,
the block generation node sends the block to the relay server
and propagates it quickly to participating nodes, including
nodes in other continents. Thus, the measured propagation
time to reach 50% of the nodes is shorter than the simulated
propagation time. However, because nodes not participatingin the relay network propagate blocks on a random network,
if the participation rate in the relay network is not high, the90th percentile of the propagation delay is not affected by therelay network as much as the 50th percentile. The participation
rate in Falcon, a relay network, was 2.65% as of January 27,
2019 [4].
Therefore, it is reasonable that the 50th percentile simulated
results were larger than the measured results and the 90th
percentile measured results were similar to the simulated
results. This means that our model correctly simulates Internet
improvement and CBR.
B. Block propagation delay
Here, we discuss the impact of Internet improvements and
CBR on the block propagation delay.

Proc. 25th IEEE Symposium on Computers and Communications(IEEE ISCC 2020), July 2020

25
20 18.180
15
9.424
6.434

10
5
0

3.0

50% Delay
90% Delay

25.956

3.222
1.791

1.3412.365

2015 Internet 2015 Internet 2019 Internet 2019 Internet
without CBR with CBR without CBR with CBR

Fig. 3. 50th and 90th percentile of block propagation delay.

In this experiment, simulations with and without CBR
were performed with 2015 and 2019 Internet parameters. The
number of nodes was 9000, and the block size was 1.0 MB.
Fig. 3 illustrates the 50th and 90th percentile of the blockpropagation delay measured. By comparing the result of
simulation with 2015 Internet without CBR and the resultof simulation with 2015 Internet and CBR, the propagation
delay is reduced by 90.1% for the 50th percentile and 87.6%
for the 90th percentile. Furthermore, comparing the result of
simulation with 2015 Internet without CBR and the result of
simulation with 2019 Internet without CBR, the propagation
delay is reduced by 64.6% for the 50th percentile and by
63.7% for the 90th percentile. These results reveal that CBRgreatly reduces the propagation delay.
In terms of Internet improvements from 2015 to 2019,
network latency became 0.889 times shorter on average, andbandwidth became 2–3 times wider. In CBR, the compact
block size was 0.018 times the block size of the legacy protocol. The product of the data size and bandwidth accounts formost of the propagation delay; thus, CBR has a greater impact
on the block propagation delay than Internet improvements.
Finally, the propagation delay was shorter in the simulation
with 2019 Internet using CBR than in the simulation with 2019
Internet without CBR. This is because the failure block size
was still large; thus, the communication time was shortenedby improving the bandwidth and the network latency was also
shortened.

C. Fork rate
We also investigated the impact on fork rate. Fig. 4 shows
the measured fork rates, which represent the ratio of orphan
blocks among all generated blocks. The fork rate is improvedby shortening the block propagation delay; therefore, thesecurity of the 2019 Bitcoin is superior that of the 2015 Bitcoin
network.

2.79

2.5
Fork Rate [%]Block Propagaton Delay [s]

30

2.0
1.5
0.95

1.0
0.5
0.0

0.28

0.19

2015 Internet 2015 Internet 2019 Internet 2019 Internet
without CBR with CBR without CBR with CBR
Fig. 4. Fork rate.

V. C ONCLUSION
In this study, we investigated the impacts of Internet improvements and CBR on the block propagation delay and fork
rate by simulation. The results indicate that CBR contributes
to shortening the block propagation delay more than Internet
improvements. This is because the rate of block size reduction
by CBR is larger than the rate of network latency reduction
and bandwidth increase by Internet improvements.
In addition, in the simulation of the Bitcoin network in 2015
and 2019, the 90th percentile of the block propagation delay
was similar to the measurement results. However, the value ofthe 50th percentile was larger than the measured value. This isbecause the simulation assumed a random network; however,part of the actual Bitcoin network is a structured network suchas a relay network.
Implementation of the topology and the node behavior ofrelay networks on SimBlock should improve the accuracy ofthe simulation. This is left for future work and should makeit possible to simulate a more realistic Bitcoin network.
ACKNOWLEDGMENT
This work was supported by SECOM Science and Technology Foundation.
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Proc. 25th IEEE Symposium on Computers and Communications(IEEE ISCC 2020), July 2020