GeoBlockchain: Geolocation Based Consensus Against 51% Attacks

Franco Moloche-Garcia, Pedro Bustamante-Castro and Willy Ugarte

Universidad Peruana de Ciencias Aplicadas, Lima, Peru

Keywords:

Blockchain, Consensus Algorithm, 51% Attacks, Geolocation, Mining Pools.

Abstract:

Currently, Blockchain technology has been involved in various areas such as medicine, environment, ﬁnance,

mining, etc., therefore, the rise of technology causes an increasing use among technology companies, devel-

opers and even malicious attackers. The latter, through 51% attacks, could have the ability to manipulate a

Blockchain network. The present proposal consists of a consensus algorithm for Blockchain networks based

on Proof-of-work (PoW) that, through the use of geolocation, aims to provide protection against 51% attacks.

With enough computational power on one of these Blockchain networks, an attacker could reverse transactions

and identiﬁcation might be impossible. It is the mining pools, which together, could have the capacity to carry

out these attacks. Using geolocation, it is intended to minimize the probability of generating mining pools,

making their formation unfavorable. In our experiments, the algorithm reduces the probability of 51% attacks

by an average of 29% compared to PoW, providing a new layer of protection when generating consensus

between participating Blockchain nodes.

1 INTRODUCTION

Due to the high popularity of Blockchain and the rise

of its use due to various factors such as cryptocur-

rencies, this growth could represent a danger for this

technology since, as there are more interested in us-

ing and implementing it, there are also greater desires

to attack and violate it. For example, according to

Forbes

there are currently around 20,000 cryptocur-

rency projects and where there are around 295 million

users in total. If we consider this number of users, we

could conﬁrm the great popularity of cryptocurrencies

today, but also the impact of being attacked. Since the

nodes are the ones that support the network, many of

them join in what are called mining pools.

From Figure 1, putting together 4 mining pools is

enough to meet the computational power needed to

carry out 51% attacks. It is something that should

be of concern, however, it is still believed that these

groups would not attack the blockchain since they

themselves would be harmed. Even so, it is a pos-

sibility for which there is no contingency. The use

of these mining pools leads to the problem of this re-

search: The 51% attack. Currently this type of attack

https://orcid.org/0000-0002-7510-618X

“10 Best Crypto Exchanges Of 2022” - Forbes -

https://www.forbes.com/advisor/investing/cryptocurrency/

best-crypto-exchanges/

Figure 1: Bitcoin mining pools

has not occurred, however, with the passage of time

and the growth of these mining pools, they could be

carried out with serious damage to cryptocurrencies

and other systems that use Blockchain.

A Blockchain is made up of blocks that are added

by mining them. Nodes can participate in the min-

ing process and only selected ones are used for val-

idation (Zheng et al., 2018). This mining process is

called Proof of Work (PoW) and it is the main cause

that could trigger the 51% attack. This attack consists

of several malicious nodes joining together so that be-

https://btc.com/stats/pool

GeoBlockchain: Geolocation Based Consensus Against 51 .

DOI: 10.5220/0011996600003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 255-262

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

255

ing the majority they can attack the Blockchain. To

achieve this, these nodes together must exceed 50%

of the total number of nodes used by the Blockchain

in order to violate it. If one of these attacks were to

happen on, for example, a cryptocurrency such as Bit-

coin, fraud or money loss could be generated by dif-

ferent users within the network. Solving this type of

problem is complicated because the larger the mining

pools, the greater the probability that they will come

together to generate the attack. On the other hand,

simulating this type of attack is also complicated due

to the need to have several nodes available to be able

to attack the Blockchain, consuming a lot of compu-

tational and economic resources. The use of PoW as

a consensus algorithm has been an advantage in the

adoption of blockchain technology. However, we also

consider it a limitation as it is an energy-intensive so-

lution that promotes centralization. With the develop-

ment of this work we proposed a decentralized solu-

tion based on the use of geographic validations, which

generates a more efﬁcient and secure system.

According to Satoshi Nakamoto in the original

Bitcoin paper: The system is secure as long as the

honest nodes collectively control more CPU power

than the attacking nodes (Nakamoto, 2009). There-

fore, with the current popularity of Blockchain, this

type of attack could be possible and harmful, es-

pecially in networks with few nodes. To make a

Blockchain network more resistant to this problem,

we have modiﬁed the PoW consensus algorithm to

add geographical restrictions. The project is limited

to the simulation of coordinates (longitude and lati-

tude) given an IP, due to the complexity involved in

implementing a technology such as Octant. The hash

rate represents the computational power that is used

in a blockchain for mining. Having more than 50% of

the hash rate in a blockchain allows its owner to carry

out dishonest actions. With the use of geographical

validations, it is possible to proportionally and impar-

tially discriminate said hash rate and its inﬂuence and

to increase the security and trust in the network, as

well as reducing the centralization of power.

The use of geographical validations can help to

create a more secure and trust blockchain network

by reducing the centralization of power. This vali-

dation process can help to ensure that no one entity

has control over the majority of the hash rate, and that

all users are able to participate in the network fairly.

Since this vulnerability (51% of attacks) could go un-

noticed, it is difﬁcult to have a metric. For this project

we counted the ownership percentage of the last 100

blocks mined by dishonest nodes. This metric rep-

resents the distribution of power in the network at a

given time and can be used as a detection system for

these types of attacks.

Our main contributions are the following:

• We have developed a consensus algorithm with

geographical validations on a GeoBlockchain

• We have developed an unpredictable map gener-

ator of geographic zones for the restrictions and

validations of the GeoBlockchain.

• We have proposed a metric for the comparison of

the probability of attacks of 51

This paper is organized as follows. Similar solu-

tions currently implemented in the literature will be

explained in Section 2. In Section 3, the technolo-

gies related to our proposal will be explained, such as:

Blockchain, consensus algorithm and 51% attacks.

Finally, Section 4 will detail the experiments and their

results. To conclude with Section 5

2 RELATED WORKS

Now, the different existing solutions using various

consensus mechanisms for the blockchain protocol

will be brieﬂy discussed. It is worth mentioning that

to the best of our knowledge, none of the related

works use geographical validations and do not nec-

essarily seek to protect themselves from 51% attacks.

In (Nakamoto, 2009), part of the author’s proposal

is the called PoW algorithm, it is intended to be a

source of truth where only the largest chain will be the

chosen one. The blockchain is secure if honest nodes

have most of the participation (Yan, 2022). Also, the

protocol makes the attackers less favored if they have

enough power to support the chain and get more re-

wards. Instead, in our work we have mentioned that

this process is based on trusting potential attackers

and can lead to a 51% attack and we use geograph-

ical validation, restricting an attacker to be enough

distributed around the globe to achieve this attack.

Another related work is ReCon (Sybil-resistant

consensus) (Biryukov and Feher, 2020), a consensus

mechanism that takes external reputation ranking as

input. The consensus can tolerate larger threshold of

malicious nodes compared to another’s. The main dif-

ference with our work is that ReCon is aimed at pro-

tecting the blockchain from sybil attacks, while ours

seeks to add protection against 51% attacks. This type

of attack is known to require a signiﬁcant number of

nodes in the network, regardless of the computational

power of the attacker. This proposal could be inte-

grated with the present project with the intention of

having a more protected network.

Proof-of-Activity is a fork-free hybrid consensus

algorithm (Liu et al., 2019). It is based on PoW and

256

(a) A example of blockchain from (Zheng et al., 2018).

(b) Taxonomy of consensus properties (Ferdous et al., 2021).

the increase in energy cost in PoW from (Wendl et al., 2023).

Figure 2: Examples of blockchain and the taxonomy of consensus.

Proof-of-Stake (PoS). Initially a node can use PoW to

mine a block consisting of meta information, which is

then used to choose a set of validators via PoS (Fer-

dous et al., 2021). The algorithm is characterized by

protecting itself from selﬁsh mining attacks and has a

fairer committee of validators. As with ReCon, this

proposal can be implemented with our work. How-

ever, our project is also indirectly protected from self-

ish mining attacks, as it could not be done without

most of the computing power on the network.

In (Xue et al., 2018), they propose Proof-of-

Contribution (PoC), a consensus algorithm that has

lower power consumption than PoW. This consen-

sus, in addition to rewarding the node that man-

aged to mine a block, also rewards the difﬁculty ex-

erted by the other miners who did not win the block.

Another variant is Delegated-Proof-of-Contribution

(DPoC) (Song et al., 2021), in which the main con-

tribution is the support of intellectual property. Un-

like these proposals, our project is not intended to re-

duce energy consumption directly. However, the fact

of blocking a portion of the miners leads to lower en-

ergy consumption indirectly.

3 MAIN CONTRIBUTION

3.1 Preliminary Concepts

In the blockchain protocol, the consensus algorithm

is involved, which is the main support to maintain

the correct chain. Speciﬁcally the proof-of-work al-

gorithm is involved in issues like mining pools and

51% attacks. Approaches adapted by other research

on these terms are presented below.

3.1.1 Blockchain

According to Z. Zheng et al. (Qiao et al., 2021),

blockchain is the key technology in the digitization

of cryptocurrency systems such as bitcoin. Within the

development of this technology, its consensus mecha-

nism and distributed storage technology are involved.

In addition, it provides an effective solution to trust

problems in open networks and the security of data

storage in centralized institutions. Figure 2a depicts

an example of blockchain (Zheng et al., 2018).

3.1.2 Consensus Algorithm

Figure 2b depicts the taxonomy of consensus proper-

ties (Ferdous et al., 2021).

Proof-of-Work (PoW): Being one of the ﬁrst con-

sensus algorithms, its main operation lies in what is

deﬁned as mining (Qiao et al., 2021). In order for a

node to add a block in this system, it must ﬁrst solve

a puzzle that has a dynamic difﬁculty that depends

on the network computational power. The node that

manages to resolve and distribute the fastest gets a

reward. Bitcoin is credited with an environmental im-

pact whose annual emissions in 2021 are responsible

for around 19,000 future deaths (Truby et al., 2022).

Figure 2c depicts the quantity of Terahashes

per second to prove the increase in energy cost in

PoW (Wendl et al., 2023). Although an attack on

large blockchain networks such as Bitcoin has yet

to be orchestrated, the likelihood of one happen-

ing in the near future is increasingly high. This is

mainly due to the growth that blockchain is having

due to the arrival of Web3, decentralized applications

(dapps) and the growing interest in mining pools.

However, there are currently very small blockchain

applications that can be quickly compromised with

the 51% attack or immutability attack if the neces-

GeoBlockchain: Geolocation Based Consensus Against 51

257

Figure 3: Categorisation of attacks on identity-augmented

Proof-of-Stake systems from (Platt and McBurney, 2021).

sary security measures are not considered. Regional

blockchains are created to be able to create small de-

centralized networks according to the region where it

is located (Shrestha and Nam, 2019). The relationship

between regional blockchain networks and the 51%

attack are more likely, therefore, this research could

be perfectly suited to this type of blockchain today.

Proof-of-Stake (PoS): This algorithm, unlike PoW,

does not require mining, therefore, its environmental

impact is less. Its operation is to randomly choose a

node to validate the next block, to be eligible as a val-

idator it is necessary to deposit a number of coins in

the network (Qiao et al., 2021). The more coins are

deposited, the chances of being chosen increase, this

being its main disadvantage since it promotes cen-

tralization. Below is a diagram of vulnerabilities in

PoS. Figure 3 depicts the categorisation of attacks on

identity-augmented Proof-of-Stake systems (Platt and

McBurney, 2021).

3.1.3 Mining Pool

Blockchains have a degree of decentralization, how-

ever, it is possible that the nodes decide to join to-

gether to form what is known as a mining pool. This

is due to the fact that the rewards end up being dis-

tributed in the network, this favors nodes with lit-

tle computational capacity, increasing the proﬁts they

could obtain. Unfortunately, this represents a secu-

rity risk since gathering enough computational power

makes it possible to perform dishonest or fraudulent

actions (Zheng et al., 2018).

3.1.4 51 % Attack

Computational Power: The hash rate is the num-

ber of hashes that a computer can calculate per sec-

Figure 4: Example of the double spending problem

from (Pinz

on and Rocha, 2016), a problem that can be done

through the 51%.

ond (Kausar et al., 2022). With a sufﬁcient hash rate,

fraudulent attacks and actions can be carried out on

the network, such as 51% attacks. The only protection

you have against this “power” is to trust that some-

one who has this ability is honest, since, otherwise,

he would disadvantage himself (Nakamoto, 2009).

Accessibility: Although reaching sufﬁcient com-

puting power for this type of attack is directly com-

plicated, it is possible to achieve it through other

such attacks such as: Sybil attacks, DNS attacks,

BGP hijacking and spatial partitioning, double spend-

ing attacks, Finney attacks, Selﬁsh mining attacks,

etc (Kausar et al., 2022). Also, it opens the doors

to other attacks like DDoS. Figure 4 depicts an exam-

ple of the double spending problem from (Pinz

on and

Rocha, 2016).

3.2 Method

For the development of a consensus algorithm capable

of disfavoring the formation of mining pools, we pro-

posed a dynamic mining restriction in geographical

areas to the proof-of-work algorithm. Zones which

are randomly generated based on the hash of the pre-

vious block, this allows nodes to verify if an incoming

block comes from a node in a valid mining zone.

3.2.1 Generation of Mining Zones

Due to the need for each node in the network to have

access to a canvas with valid mining areas. It was

proposed to use a seed-based gradient noise random

number generator, since using a seed in this type of

method allows all nodes to generate the same can-

vas. This allows to generate a 2-dimensional canvas

258

in which the axes represent the longitude and latitude

of the planet. Using the hash of the previous block

as a seed, it is possible for any participant in the net-

work that wishes to mine to generate such a canvas

and verify if its location (longitude and latitude) is in

a valid mining area. If it is in a valid area, then the

mining process would continue; otherwise it would

be in standby mode.

In Algorithm 20, in the 1st and 2nd lines the con-

ﬁguration for the generation of random zones is de-

clared. In the 3rd line, the seed is declared by adding

the characters of the hash of the previous block. In

the 4th line, an array is initialized with the dimen-

sions corresponding to the available longitudes and

latitudes (rounded). On line 5, the 2-dimensional ran-

dom number generator is initialized with the Open-

Data: Previous block hash

Result: Boolean matrix with valid mining

zones by longitude and latitude

1 noiseScale ← 0.03;

2 gap ← 0.2;

3 seed ←SumChars( prevHash);

4 canvas ←Matrix(180, 360);

5 Generator ←OpenSimplex(seed);

6 xo f f ← 0;

7 for x ← 0 to 360 do

8 yo f f ← 0;

9 for y ← 0 to 180 do

10 n ← Generator([xo f f , yo f f ]);

11 if n > gap then

12 canvas[y, x] ← True;

13 else

14 canvas[y, x] ← False;

15 end

16 yo f f ← yo f f +noiseScale;

17 end

18 xo f f ← xo f f +noiseScale;

19 end

20 return canvas;

Algorithm 1: Mining Zones Generator.

Figure 5: Canvas with blank valid mining areas generated

with 42 as a seed.

Simplex algorithm. In line 6 and 8 the offsets for

the generation of random numbers are initialized. Be-

tween lines 7 and 19 the longitudes and latitudes to be

evaluated are iterated. In line 10 the random number

n is generated based on the offsets. Between lines 11

and 15, if n is greater than the gap, True is assigned to

that zone, otherwise, False is assigned. In line 16 and

18 the offsets are increased respectively by the nois-

eScale. Finally, on line 20 the canvas is returned with

the valid mining regions set to True. For the develop-

ment of this mining zone generator, a gradient noise

function called OpenSimplex was used. Speciﬁcally

version 0.7.0 implemented in Rust. Up to 4 dimen-

sions are allowed, however only 2 are needed.

Figure 5 depicts a canvas with blank valid mining

areas generated with 42 as a seed. The use of a seed-

based random number generator was chosen to take

advantage of the hash of the previous blocks. How-

ever, it was also considered to generate gradient ran-

dom numbers, since mining zones will be more sim-

ilar to regions and give nodes more chances of being

inside one.

3.2.2 Consensus Algorithm

For the proposed consensus algorithm, it is taken into

account that it is capable of doing the following: gen-

erate unbiased mining zones, restrict mining in said

zones and validate that the nodes comply with the re-

strictions. On line 1 of our consensus algorithm, we

get g, which represents if a node is in a mining zone

(based on the previous block hash and the node’s IP).

In line 3, if you are not in a valid mining zone and

if you have not exceeded the tolerance limit in line 2,

the mining process is stopped. In Algorithm 9, from

lines 4 to 9, the traditional PoW algorithm is contin-

ued. The situation where a node tries to mine before

the tolerance time is not in scope. Furthermore, it is

not considered to reliably validate the location of a

node due to the complexity involved in developing it.

3.2.3 Smart Contracts

Additionally, the blockchain was conﬁgured to sup-

port smart contracts. This is possible thanks to the

fact that Substrate, the framework used, supports inte-

gration with this technology. Added Substrate Smart

Contracts module. This module is in charge of being

able to enable Smart Contracts in the Blockchain. For

this, the Substrate library called pallet-contracts had

to be added. Smart Contracts are developed using the

Ink! language. This Rust-based language allows you

to create Smart Contracts in a simpliﬁed way and us-

ing the advantages of Rust such as memory optimiza-

tion. For the validation of the blockchain, a Smart

GeoBlockchain: Geolocation Based Consensus Against 51

259

Data: Block info

Result: Nonce that solves the mining puzzle

1 g ←IsOnMiningZone(prevHash, IP);

2 if not g and currentTime < timestamp + tolerance then

3 return NULL

4 b ←SHA256HashFunction(txRoot,timestamp, prevHash, IP);

5 nonce ← 0;

6 while SHA256HashFunction (b, nonce) < targetDi f ficylty do

7 nonce ← nonce+1;

8 end

9 return nonce

Algorithm 2: Consensus algorithm for GeoBlockchain inpired by PoW from (Juriˇci´c et al., 2020).

Contract oriented to clinical records was developed.

In this Smart Contracts patients are stored along with

their medical records. Substrate has a tool to be able

to develop Smart Contracts called Contracts UI that

allows you to add the Smart Contract to a Blockchain

with the pallet-contracts module. This UI made it

easy for us to deploy the Smart Contract and test it

during development.

Once the blockchain is conﬁgured with the mod-

iﬁed consensus algorithm, it is compiled using cargo

build command and the generated compilation is exe-

cuted. This compilation lifts the genesis block of the

blockchain, lifts the endpoints so that clients connect

to the blockchain and can consume data through RPC

API, and also enables the websocket that allows com-

munication with a telemetry server.

4 EXPERIMENTS

In this section we will discuss the experiments our

project has undergone, as well as what is needed to

replicate said experiments and a discussion of the re-

sults obtained after this process.

4.1 Experimental Protocol

To recreate the process of building, deploying, and

testing the blockchain used in our project, we start by

describing what it took to accomplish that task.

4.1.1 Development Environment

Being a blockchain, the development environment is

variable, 3 devices were used that ran Linux either

directly or through WSL2. The blockchain binary

weighs around 3 4 GB, however, a minimum of 16

GB of RAM was required for development.

Figure 6: Substrate architecture from Substrate.io.

4.1.2 Blockchain

The blockchain was developed based on the Sub-

strate SDK, a framework designed for the creation

of blockchains. Substrate has a template called

substrate-node-template which was used as the basis

for developing the blockchain.

Figure 6 depicts the substrate architecture from

Substrate.io. This default template has the PoS con-

sensus algorithm. The PoW consensus algorithm im-

plemented in substrate was modiﬁed to transform it

into the one proposed for GeoBlockchain. In addi-

tion, a library with OpenSimplex support in rust was

used for the generation of regions. The blockchain

was conﬁgured with the library sp-consensus-pow

and sc-consensus-pow from Substrate and updated to

the latest version to ensure compatibility with the

same framework and other libraries from Substrate

and Rust. The update to the latest version was nec-

essary because Substrate is a new framework which

does not have much documentation and the previ-

260

ous pre-release versions deprecate quickly. After the

conﬁguration of the blockchain, a mining script was

added for the nodes. The mining algorithm invokes

the algorithm proposed in this research when calling

the worker’s submit method. This algorithm has been

added as one more library to order the logic that we

have from the independent blockchain to the proposed

algorithm. This library is implemented in the main

blockchain and is called every time the mining pro-

cess is carried out.

4.1.3 Testing Environment

Docker was used to test the blockchain, creating vir-

tual nodes simulating a computer. The containers can

be conﬁgured as it would be with a real computer in

such a way that each one can have assigned amount

of resources, IP address, etc. The use of containers

was necessary to be able to simulate a blockchain due

to lack of resources to be able to have several com-

puters and carry out a real simulation. After gener-

ating the blockchain binaries, an image was created

that was uploaded to different devices to simulate a

network. Substrate has a telemetry application where

connected nodes, blocks, etc. are displayed. When

orchestrating the attack in this application, it is shown

that the dishonest nodes are the ones that take the lead

in the blockchain, even propagating the blocks that

they are mining. Showing that the dishonest nodes are

the ones that have priority because they have greater

computational power than the honest ones. Thanks to

this, dishonest nodes have a greater chance of mining

the block belonging to a mining area. Honest nodes at

times also manage to propagate blocks, however, the

trend is the blocks of dishonest nodes.

4.1.4 Source Code

Our source code for building the blockchain is

publicly available at https://github.com/magpex13/

geoblockchain.

4.2 Results

Eight nodes were deployed for the orchestration of

the 51% attack. For this, 4 containers were used for

the honest nodes and 4 for the dishonest ones. To

simulate a 51% attack, the computational power of the

nodes was distributed 80% to the dishonest ones and

20% to the honest ones. For this, the percentage of

CPU per container was conﬁgured in such a way that

the dishonest ones have greater computational power.

Figure 7 depicts the percentage of the last 100

nodes that were mined by an attacker (PoW in blue

and ours in red). Considering that the probability that

Figure 7: Percentage of the last 100 nodes that were mined

by an attacker.

Table 1: Average of the last 100 blocks mined by dishonest

nodes.

Consensus

Percentage of blocks mined

by dishonest nodes

PoW 94.10%

GeoBlockchain PoW 64.70%

Difference 29.40%

a node mines a block in PoW depends on its compu-

tational power, for our experiment this situation was

conditioned 19.63% of the time. Since, generating

mining areas with a seed of 42 to 420, on average

that percentage of valid areas is obtained. In addi-

tion, the computational power of the attacking nodes

was set to 80%, while the remaining 20% went to the

honest nodes. The results shown in the Table 1 indi-

cate that, indeed, the probability of a 51% attack can

decrease depending on the different conﬁgurations of

the mining zones generator. On average, PoW had a

94.10% probability of attacks of 51%, compared to

ours, which obtained 64.7%. A difference of 29.40%.

As can be seen, the probability of a 51% attack on a

PoW blockchain is much higher.

4.3 Discussion

As presented above, the results of our experiments in-

dicate that the probability of an attack varies with dif-

ferent settings of our algorithm. In our opinion, this

is a very important aspect to take into account when

designing a blockchain. However, it is important to

keep in mind that in case no nodes are found in valid

mining zones (80.37% of the times) the block will be

mined by the one that managed to distribute it faster

after the tolerance time. Our proposal has been de-

signed to achieve a higher degree of security in the

network, since a malicious node would need to be able

to predict the area of a valid mine in advance, and be

able to deliver the block to the network faster than the

GeoBlockchain: Geolocation Based Consensus Against 51

261

other nodes. The probability of a successful attack is

proportional to the number of miners that are in the

same mining zone at the same time, since in our sys-

tem the mining zone is generated randomly. There-

fore, the probability of an attack is greater the greater

the number of miners in valid zones from which they

cannot be predicted. We propose to decrease the size

of the mining area to decrease the probability of an

attack, however we consider it important to keep in

mind that if the size of the mining areas is too small,

the block will take longer to be distributed.

5 CONCLUSION

We conclude that adding geographical validations to

the PoW algorithm can add a 51% layer of protec-

tion against attacks. Despite not prioritizing the most

optimal conﬁgurations, 29% protection was achieved

with our consensus algorithm compared to PoW us-

ing our metric of the average percentage of the last

100 blocks mined by attacking nodes.

We have analyzed the 51% attack on a blockchain

network and its possible countermeasures. The attack

is a serious problem in the world of cryptocurrencies,

as it allows the attacker to manipulate transactions and

even block them, which could lead to double spend-

ing. This shows how important it is to have protection

against this type of attack and the advantages that the

use of geographical validations offers. Considering

that the veriﬁcation of locations was simulated due

to the complexity of its implementation, it is possible

to develop a system that, through a network, allows

knowing the geographical location of a node with so-

lutions such as Octant. A framework with which the

geographical position of a node can be determined

with great conﬁdence, simply by measuring its la-

tency with reference points (Wong et al., 2007).

In a future work we will seek to implement a ge-

ographic location validator, since our consensus algo-

rithm continues with PoW as there are no valid nodes

in the mining zones, and try it with different kinds of

data such as healthcare data (Arroyo-Mari

nos et al.,

2021) or Wood supply chain (Cueva-S

anchez et al.,

2020). With this validator it is possible to prioritize

the distance from a node to a mining area, which pro-

motes lower energy expenditure.

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