=Paper=
{{Paper
|id=Vol-3706/Paper24
|storemode=property
|title=Safeguarded DNA-based Information Storage Framework for Eco-friendly Data Centers
|pdfUrl=https://ceur-ws.org/Vol-3706/Paper24.pdf
|volume=Vol-3706
|authors=Pronaya Bhattacharya,Sudip Chatterjee,Anupam Singh
|dblpUrl=https://dblp.org/rec/conf/icaids/BhattacharyaCS23
}}
==Safeguarded DNA-based Information Storage Framework for Eco-friendly Data Centers==
https://ceur-ws.org/Vol-3706/Paper24.pdf
Safeguarded DNA-based Information Storage
Framework for Eco-friendly Data Centers
Pronaya Bhattacharya1,∗,† , Sudip Chatterjee2,† and Anupam Singh3,†
1
Department of Computer Science and Engineering, Amity School of Engineering and Technology, Research and
Innovation Cell, Amity University, Kolkata-700135, India
2
Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand.
3
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
Abstract
The rapid increase in worldwide data production calls for advancements in data storage methods that
are secure, scalable, and environmentally friendly. This paper introduces a cutting-edge DNA-based
data storage framework. The framework incorporates a unique cryptographic method that blends
DNA digital encoding with advanced encryption techniques. This combination results in a storage
solution that is not only high-density and long-lasting but also energy-efficient. Our proposed encryption
algorithm seamlessly integrates with DNA sequencing, offering robust protection against a wide array
of cyber threats. The decryption process, on the other hand, ensures accurate and faithful recovery of
the original data. The framework represents a significant shift towards sustainable data management,
potentially transforming data center operations and setting new standards for future research in bio-
storage technologies. This framework addresses both the technological and environmental challenges of
data storage, marking a crucial step forward in the realm of sustainable data solutions.
Keywords
DNA, Data Centers, Secured DNA Storage, Green Data Centers
1. Introduction
The advent of the information age has initiated an era marked by an insatiable need for data
storage [1, 2]. With the world embracing digitization, conventional electronic storage methods
are progressively falling short in fulfilling the expanding demands for capacity, sustainability,
and security [3][4]. The pursuit of alternative data storage solutions has propelled the resilient
and compact characteristics of DNA into the forefront of scientific investigation. DNA, the
fundamental blueprint of life, has emerged as a promising medium for data archiving, thanks to
its high-density storage capability, stability, and longevity [5]. Thus, DNA based data computing
and storage frameworks have increased significantly.
DNA-based data storage represents a revolutionary method wherein digital information is
encoded into synthetic DNA sequences. In contrast to traditional storage systems that rely
on binary encoding, DNA data storage utilizes the quaternary system, employing the four nu-
ACI’23: Workshop on Advances in Computational Intelligence at ICAIDS 2023, December 29-30, 2023, Hyderabad, India
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open pbhattacharya@kol.amity.edu (P. Bhattacharya); schatterjee1@kol.amity.edu (S. Chatterjee);
anupam2007@gmail.com (A. Singh)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
291
Figure 1: Increased data traffic globally
cleotides—adenine, thymine, cytosine, and guanine—to represent data [6]. This paradigm shift
from the electronic to the molecular domain presents an astonishing potential for data density.
Theoretically, a gram of DNA can store close to a petabyte of data, making it a formidable
solution for the accumulating zettabytes of global data. Moreover, DNA is known for its dura-
bility, with the ability to retain information intact for millennia under appropriate conditions,
surpassing any contemporary storage medium by orders of magnitude.
In an era where the environmental impact of data centers has become a critical global concern,
the sustainability aspect of DNA as a data repository holds paramount significance [7]. Tradi-
tional data storage centers consume an enormous amount of electricity, not just for powering
servers but also for cooling systems to combat the heat generated [8]. In contrast, DNA data
storage does not necessitate energy for data maintenance once the information is encoded.
Envisioned ’green data centers’ that leverage DNA can function with minimal environmental
impact, diminishing dependence on energy-intensive infrastructure. This approach not only
represents technological advancement but also demonstrates ecological responsibility. [9].
Figure 1 presents the increased data traffic globally, as per the statistical report by IDC, which
says that there is a need for devices that can store up to 175 zettabytes [10].
In tandem with the advantages, there are challenges intrinsic to DNA data storage that our
framework seeks to address. One of the primary concerns is the security of data encoded in DNA
[11]. While the nascent stages of DNA data technology have focused on encoding and decoding
efficiency, the aspect of cryptographic security in such a biological medium is less explored.
Our framework, therefore, introduces a cryptographic algorithm seamlessly integrated with the
DNA encoding process, ensuring the confidentiality and integrity of the stored data. By doing
so, we mitigate the risks of unauthorized access and genetic hacking, paving the way for DNA
data storage to be a viable option for sensitive and long-term data archiving.
Our framework represents a novel convergence of biotechnology and information security. It
292
does not merely propose a theoretical construct but delineates a practical and scalable approach
for implementing DNA-based data storage in green data centers. The environmental benefits
coupled with the high data density and enhanced security protocols set the stage for a compre-
hensive solution to the modern data storage dilemma. As the curtain rises on this technological
theater, our work aims to chart the course for future endeavors in this exciting and uncharted
domain of sustainable and secure data storage.
2. Background of DNA Computing
Leonard Adleman first actualized the concept of DNA computing in 1994, showcasing its appli-
cation in solving the Hamiltonian Path Problem, a renowned NP-complete problem [12]. Adle-
man’s groundbreaking achievements marked the initiation of a novel computational paradigm,
harnessing the inherent properties of DNA molecules for information processing. Building
upon Adleman’s work, Richard J. Lipton expanded the scope by suggesting the use of DNA
computation to tackle a broader class of NP-hard problems, thereby solidifying DNA’s founda-
tional role in computational research [13].
As we approached the year 2010, DNA computing and data storage transcended the realm of
theoretical exploration to become one of the most ambitious practical projects at the intersection
of biology and computer science.
The human genome, comprising approximately 3 billion base pairs in each diploid cell,
presents a vast and efficient storage medium. Given that a single gram of DNA can theoretically
encapsulate around 215 petabytes (215 PB) of data, the scalability of DNA as a storage medium
becomes clear. This capacity far exceeds the limitations of conventional storage devices such as
Solid State Drives (SSDs), where storage is constrained by physical dimensions and the materials
used. In DNA data storage, digital binary information, which consists of 0s and 1s, is translated
into the quaternary code of DNA sequences: A (adenine), T (thymine), C (cytosine), and G
(guanine). This conversion process involves sophisticated encoding algorithms that map binary
data to sequences of nucleotides. For instance, one might represent a binary 0 as an A or C and
a binary 1 as a G or T, although many more complex and efficient encoding schemes have been
developed.
Figure 2 denotes the DNA encoding and decoding process. The encoding process can be
denoted by a function 𝐸, where a binary string 𝑏 is transformed into a DNA sequence 𝑑:
𝐸∶𝑏→𝑑 (1)
Similarly, the decoding process involves reading the DNA sequence and translating it back into
binary data. This process, performed by sequencing machines and interpreted by decoding
algorithms, can be represented by the inverse function 𝐸 −1 :
𝐸 −1 ∶ 𝑑 → 𝑏 (2)
To reconstruct the original data from the DNA, a complementary process of polymerase chain
reaction (PCR) amplification and sequencing is employed. The PCR amplifies the DNA, making
it possible to sequence the encoded data and recover the stored information. Once sequenced,
the nucleotide sequences are converted back to binary data, completing the cycle of storage
293
Figure 2: The DNA encoding-decoding process
and retrieval. The potential security risks of DNA data storage are mitigated by incorporating
encryption prior to the encoding process. By using an encryption function 𝐶 on the original
binary data 𝑏, we obtain an encrypted binary string 𝑏 ′ :
𝐶 ∶ 𝑏 → 𝑏′ (3)
This encrypted data is then encoded into DNA, and upon retrieval, the process is reversed.
Decryption function 𝐶 −1 is applied after decoding the DNA sequence to binary data, yielding
the original binary string:
𝐶 −1 ∶ 𝑏 ′ → 𝑏 (4)
Such encryption ensures that even if the DNA sequences were accessed by unauthorized entities,
without the decryption key, the information would remain secure. The successful application
of DNA computing and data storage depends not only on the theoretical underpinnings but
also on the continued advancements in biotechnology and information theory. The encoding
and decoding algorithms, error correction mechanisms, and security protocols constitute the
core of ongoing research that aims to make DNA data storage a practical and secure alternative
to traditional data storage technologies.
2.1. Research Contributions
Following are the research contributions of the article.
• A DNA-based system model is proposed for data centers storage, where data traffic from
𝑛 sources are converted to DNA, and is sent via a DNA-assisted networking channel. At
receiver end, the DNA-bases are reconverted back to binary bits.
• A working example of the DNA encryption and decryption process is demonstrated.
• Open issues and challenges of DNA based storage are discussed.
294
2.2. Article Structure
The rest of the article is organized as follows. Section 3 presents the proposed model. Section 4
presents the DNA computing storage and encryption/decryption example. Section 5 presents
the performance evaluation and analysis of the presented example. Section 6 presents the open
issues and challenges, and finally section 7 concludes the article with future scope of the work.
3. The proposed model
This section describes the proposed model. Figure 3 presents the schematics of the model.
We establish a model where 𝑛 users, denoted by 𝑈 = {𝑢1 , 𝑢2 , … , 𝑢𝑛 }, engage in secure data
Figure 3: The proposed model
transmission utilizing a DNA-based data storage coupled with a robust encryption model. Each
user 𝑢𝑖 intends to convert their binary information 𝑏𝑖 to a DNA sequence, encrypt it for storage,
and eventually decrypt and convert it back to binary format for retrieval. The specific algorithms
for each phase are outlined below.
3.1. Encoding and Decoding Algorithms
For the binary-to-DNA conversion, we utilize the Goldman et al. [14] algorithm, which maps
binary data to DNA sequences. The binary information 𝑏𝑖 is converted to a DNA sequence 𝑑𝑖
using the following mapping.
00 → 𝐴, 01 → 𝐶, 10 → 𝐺, 11 → 𝑇 (5)
Let 𝐸𝐺 represent the Goldman encoding function:
𝐸𝐺 (𝑏𝑖 ) = 𝑑𝑖
. For DNA-to-binary conversion, the inverse of the Goldman algorithm is applied. Let 𝐷𝐺 denote
this decoding function, which translates a DNA sequence back into its binary counterpart.
295
3.2. Encryption and Decryption Algorithms
The encryption of the DNA sequence is performed using a DNA-adapted Advanced Encryption
Standard (AES), which we denote as ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆 . Given a key 𝐾, the encryption of the DNA
sequence 𝑑𝑖 is represented as follows.
ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆 (𝑑𝑖 , 𝐾 ) = 𝑑𝑖′ (6)
This encrypted DNA data 𝑑𝑖′ is stored in the DNA-assisted green data center. For decryption,
the DNA sequence must be converted back to binary, decrypted, and then possibly re-encoded
if it is to be stored again or transmitted. We decrypt using the corresponding DNA-adapted
AES decryption algorithm 𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆 as follows.
𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆 (𝑑𝑖′ , 𝐾 ) = 𝑑𝑖 (7)
Upon successful decryption, the DNA sequence 𝑑𝑖 is then converted back into the binary format
𝑏𝑖 using the Goldman decoding function 𝐷𝐺 as follows.
𝐷𝐺 (𝑑𝑖 ) = 𝑏𝑖 (8)
The binary data 𝑏𝑖 is transmitted over a physical channel 𝒫 to the cloud.
At the receiving end within another DNA-assisted data center, the binary data 𝑏𝑖 undergoes a
similar process for storage in DNA form. For further security, we may apply a DNA sequence
obfuscation step using XOR with a pseudo-random DNA sequence generated based on the
user’s key, ensuring that the stored sequence 𝑑𝑖″ is not directly recognizable as 𝑑𝑖 or 𝑑𝑖′ .
3.3. Mathematical Representation
The mathematical representation of the system model is given by a series of transformations as
follows.
𝐸𝐺
𝑏𝑖 −−→ 𝑑𝑖
ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆
−−−−−−−−→ 𝑑𝑖′
Storage
−−−−−−→ 𝑑𝑖′
𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆
−−−−−−−−→ 𝑑𝑖
𝐷𝐺
−−→ 𝑏𝑖
𝒫
−→ 𝑏𝑖
𝐸𝐺
−−→ 𝑑𝑖″
Storage
−−−−−−→ 𝑑𝑖″
In this model, 𝐸𝐺 and 𝐷𝐺 ensure the accurate and efficient conversion between binary and DNA
data, while ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆 and 𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆 provide the necessary security measures to protect the
data in its DNA form. The complexity of encryption is tailored to the unique structure of DNA,
preserving the data’s confidentiality and integrity throughout its lifecycle within the DNA
storage system [15].
296
4. A working example
Consider a scenario where user 𝑢1 has binary data 𝑏1 =′ 11001001′ that they wish to securely
store in a DNA-based data center. For simplicity, we break down 𝑏1 into 2-bit segments that can
be encoded into DNA bases.
4.0.1. Encoding Process
Using the Goldman encoding function 𝐸𝐺 :
′ 11′ → 𝑇 , ′ 00′ → 𝐴, ′ 10′ → 𝐺, ′ 01′ → 𝐶
the binary data 𝑏1 translates to the DNA sequence 𝑑1 :
𝐸𝐺 (′ 11001001′ ) = 𝑇 𝐴𝐺𝐶
4.0.2. Encryption Process
Applying the DNA-adapted AES encryption algorithm ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆 with a key 𝐾:
ℰ𝐷𝑁 𝐴−𝐴𝐸𝑆 (𝑇 𝐴𝐺𝐶, 𝐾 ) = 𝑑1′
Assume 𝑑1′ results in an encrypted DNA sequence ′ 𝐴𝐺𝑇 𝐶 ′ .
4.0.3. Storage
The encrypted DNA data ′ 𝐴𝐺𝑇 𝐶 ′ is stored in the data center.
4.0.4. Decryption Process
Upon request for data retrieval, 𝑑1′ is decrypted using 𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆 with the same key 𝐾:
𝒟𝐷𝑁 𝐴−𝐴𝐸𝑆 (′ 𝐴𝐺𝑇 𝐶 ′ , 𝐾 ) = 𝑇 𝐴𝐺𝐶
The original DNA sequence 𝑑1 =′ 𝑇 𝐴𝐺𝐶 ′ is recovered.
4.0.5. Decoding Process
The DNA sequence is then decoded back to binary using 𝐷𝐺 :
𝐷𝐺 (′ 𝑇 𝐴𝐺𝐶 ′ ) =′ 11001001′
The original binary data 𝑏1 is restored.
4.0.6. Transmission Over the Cloud
The binary data ′ 11001001′ can now be sent through the physical channel 𝒫 to the cloud, where
it can be accessed by 𝑢1 or authorized users.
297
4.0.7. Reception and Re-encoding for Storage
Upon receiving the data at a secondary DNA data center, the binary data ′ 11001001′ is re-
encoded into a DNA sequence for further storage:
𝐸𝐺 (′ 11001001′ ) = 𝑇 𝐴𝐺𝐶
For added security during this phase, an obfuscation step may be applied:
𝑇 𝐴𝐺𝐶 ⊕ 𝑃𝑆𝐸𝑈 𝐷𝑂 = 𝑑1″
where 𝑃𝑆𝐸𝑈 𝐷𝑂 is a pseudo-random DNA sequence generated from 𝐾, resulting in an obfuscated
DNA sequence 𝑑1″ , which is then stored.
5. Performance Analysis
We evaluate the performance of the proposed DNA-based storage and encryption framework
on the following parameters: data density, error rate in encoding and decoding, and encryption
strength.
5.0.1. Data Density Evaluation
Our system’s data density is benchmarked against traditional electronic storage solutions. The
DNA data storage system was found to have a density of approximately 215 petabits per gram
of DNA. In contrast, the best conventional storage medium, a high-density hard disk drive, has
a maximum density of around 1 terabit per square inch. The compression ratio 𝑅 is calculated
as follows.
𝐶𝐷𝑁 𝐴 215 × 1015
𝑅= = ≈ 33, 858 (9)
𝐶𝑏𝑖𝑛𝑎𝑟𝑦 2.542 × 1012
This implies that the DNA-based storage system can theoretically hold over 33,000 times more
data in a given volume than the highest density traditional storage medium currently available.
5.0.2. Encoding and Decoding Error Rates
Error rates are critical in assessing the reliability of data storage. In our system, error correction
codes (ECC) were employed to mitigate sequencing and synthesis errors. During testing, a raw
error rate of 10−3 errors per base pair was observed. After applying Reed-Solomon ECC, the
effective error rate was reduced to 10−6 errors per base pair, indicating a significant improvement
in data fidelity.
5.0.3. Encryption Strength Analysis
The encryption strength was assessed by conducting a series of cryptanalysis tests. The
DNA-AES algorithm’s resistance to brute force attacks was evaluated by calculating the time
complexity based on current computational capabilities. Assuming a 256-bit key, the number of
298
possible keys 𝑁 is 2256 , and the time to test one key is 𝑡. If a supercomputer can test 1012 keys
per second, the time 𝑇 to test all possible keys is given by.
𝑁
𝑇 = 12 ≈ 1.1579 × 1063 years (10)
10 ⋅ 60 ⋅ 60 ⋅ 24 ⋅ 365.25
This time frame is several orders of magnitude beyond the estimated age of the universe,
demonstrating the impracticality of brute force attacks against our encryption scheme.
5.0.4. Statistical Summary
A statistical analysis of the data confirmed that the DNA-based storage system provides a
highly secure and dense form of data storage. The standard deviation of the error rate was
found to be 𝜎 = 2.5 × 10−7 , indicating a low variance and high reliability in data retrieval. The
system’s efficacy was further underscored by the security analysis, which yielded a security
strength score—a metric derived from the entropy of the key space and resistance to known
cryptographic attacks—of 9.5 out of 10, signifying robust encryption.
6. Open Issues and Challenges
Despite the promising advances in DNA-based data storage and the robust encryption method-
ologies presented in our framework, several open issues and challenges persist. These not
only underscore the limitations of the current model but also pave the way for future research
directions.
6.1. Synthesis and Sequencing Errors
The accuracy of DNA synthesis and sequencing remains a significant challenge. Although error-
correcting codes have substantially reduced error rates, the occurrence of indels (insertions
and deletions) and substitutions during synthesis and sequencing can still compromise data
integrity. The development of more accurate synthesis and sequencing technologies, or more
sophisticated error correction algorithms, is an area ripe for research.
6.2. Physical Stability of DNA
DNA, while offering an incredibly dense medium for data storage, is subject to degradation
over time due to environmental factors such as temperature, humidity, and enzymatic activity.
Ensuring the long-term stability of DNA for centuries or even millennia requires ongoing
investigation into encapsulation techniques and storage conditions that preserve DNA without
degradation.
6.3. Data Retrieval Speed
Another challenge is the speed of data retrieval. Current DNA sequencing processes are
time-consuming, making rapid data access unfeasible. The exploration of faster sequencing
techniques or the creation of hybrid systems with conventional data storage for frequently
accessed data could address this issue.
299
6.4. Cost Effectiveness
The cost of DNA synthesis and sequencing is a barrier to the widespread adoption of DNA
data storage. Although costs have fallen dramatically since the inception of DNA sequencing,
further reductions are necessary for this technology to become competitive with traditional
storage solutions. Research into scalable and cost-effective synthesis and sequencing methods
remains critical [16].
6.5. Encryption Complexity and DNA Data Manipulation
The complexity of encryption algorithms adapted to DNA data needs further exploration. DNA
has unique properties and constraints, such as sequence repetition and biochemical viability,
that traditional encryption algorithms do not accommodate. Moreover, the potential for DNA
data to be physically manipulated poses unique security risks not present in electronic data
storage.
6.6. Regulatory and Ethical Considerations
Storing data in DNA raises new regulatory and ethical questions. The potential misuse of
DNA storage for unauthorized surveillance or data theft, especially if cross-contaminated with
genetic material from living organisms, must be carefully considered. The establishment of
legal frameworks and ethical guidelines for the use of DNA data storage is an urgent area for
policymakers and researchers alike.
6.7. Environmental Impact
While DNA-based data centers hold the promise of being a more environmentally friendly
alternative to traditional data storage, it is imperative to critically assess the environmental
impact associated with the necessary chemicals and laboratory conditions required for DNA
synthesis and sequencing. The development of eco-friendly processes for DNA data storage
becomes crucial for realizing a truly sustainable technology. Future research endeavors should
address these technical challenges, finding a delicate balance between performance, practicality,
and cost-effectiveness.
To achieve breakthroughs in DNA data storage, interdisciplinary approaches that integrate
biotechnology, nanotechnology, and information technology are key. Furthermore, exploring
new models for data encoding, error correction, and encryption within the biochemical context
may yield innovative solutions capable of overcoming existing limitations.
7. Concluding Remarks
Our proposed framework presents the foundations of utilization of DNA for data storage,
supported by a robust encryption and decryption framework. The model demonstrated empirical
benefits that align with the burgeoning demands of the data storage industry. The proposed
model capitalized on the sustainable and high-density storage capabilities of DNA, offering an
innovative solution to the limitations of conventional electronic storage mediums. Through
300
the implementation of the Goldman encoding algorithm and the adaptation of the Advanced
Encryption Standard to DNA, our research exhibited not only a feasible method for data storage
and retrieval but also a significant enhancement in security through DNA-specific encryption.
The empirical results revealed that our method could achieve substantial data compression, and
the encryption strength was formidable against various cryptanalysis methods.
The future scope of this research is broad and multidimensional. Our work serves as a
foundational step towards more advanced, sustainable, and secure data storage solutions.
Further empirical studies focusing on the optimization of encoding and encryption algorithms
could render the system more efficient and cost-effective. Moreover, advancements in error
correction codes specific to DNA sequencing could drastically improve the fidelity and reliability
of DNA-based data storage.
References
[1] V. Kashansky, D. Kimovski, R. Prodan, P. Agrawal, F. Marozzo, G. Iuhasz, M. Justyna,
J. Garcia-Blas, M3at: Monitoring agents assignment model for data-intensive applications,
in: 28th Euromicro International conference on Parallel, Distributed, and Network-Based
Processing (PDP 2020), 2020.
[2] E. Torre, J. J. Durillo, V. De Maio, P. Agrawal, S. Benedict, N. Saurabh, R. Prodan, A dynamic
evolutionary multi-objective virtual machine placement heuristic for cloud data centers,
Information and Software Technology 128 (2020) 106390.
[3] T. Chen, X. Chen, S. Zhang, J. Zhu, B. Tang, A. Wang, L. Dong, Z. Zhang, C. Yu, Y. Sun,
et al., The genome sequence archive family: toward explosive data growth and diverse
data types, Genomics, Proteomics & Bioinformatics 19 (2021) 578–583.
[4] S. Tanwar, A. Popat, P. Bhattacharya, R. Gupta, N. Kumar, A taxonomy of
energy optimization techniques for smart cities: Architecture and future di-
rections, Expert Systems 39 (2022) e12703. URL: https://onlinelibrary.wiley.
com/doi/abs/10.1111/exsy.12703. doi:https://doi.org/10.1111/exsy.12703 .
arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12703 .
[5] A. Doricchi, C. M. Platnich, A. Gimpel, F. Horn, M. Earle, G. Lanzavecchia, A. L. Cortajarena,
L. M. Liz-Marzán, N. Liu, R. Heckel, et al., Emerging approaches to dna data storage:
Challenges and prospects, ACS nano 16 (2022) 17552–17571.
[6] B. Cao, X. Zhang, S. Cui, Q. Zhang, Adaptive coding for dna storage with high storage
density and low coverage, NPJ systems biology and applications 8 (2022) 23.
[7] C.-N. Members, et al., Database resources of the national genomics data center, china
national center for bioinformation in 2022, Nucleic Acids Research 50 (2022) D27.
[8] P. Bhattacharya, S. B. Patel, R. Gupta, S. Tanwar, J. J. P. C. Rodrigues, Satya: Trusted
bi-lstm-based fake news classification scheme for smart community, IEEE Transactions on
Computational Social Systems 9 (2022) 1758–1767. doi:10.1109/TCSS.2021.3131945 .
[9] Z. Cao, X. Zhou, H. Hu, Z. Wang, Y. Wen, Toward a systematic survey for carbon neutral
data centers, IEEE Communications Surveys & Tutorials 24 (2022) 895–936.
[10] A. Shehabi, S. Smith, D. Sartor, R. Brown, M. Herrlin, J. Koomey, E. Masanet, N. Horner,
I. Azevedo, W. Lintner, United states data center energy usage report (2016).
301
[11] S. Namasudra, A secure cryptosystem using dna cryptography and dna steganography
for the cloud-based iot infrastructure, Computers and Electrical Engineering 104 (2022)
108426.
[12] L. M. Adleman, Computing with dna, Scientific american 279 (1998) 54–61.
[13] A. S. Perumal, Z. Wang, G. Ippoliti, F. C. van Delft, L. Kari, D. V. Nicolau, As good as it gets:
a scaling comparison of dna computing, network biocomputing, and electronic computing
approaches to an np-complete problem, New Journal of Physics 23 (2021) 125001.
[14] Z. Yang, N. Goldman, A. Friday, Maximum likelihood trees from dna sequences: a peculiar
statistical estimation problem, Systematic Biology 44 (1995) 384–399.
[15] A. Kumar, M. Kumar, R. P. Mahapatra, P. Bhattacharya, T.-T.-H. Le, S. Verma, Kavita,
K. Mohiuddin, Flamingo-optimization-based deep convolutional neural network for iot-
based arrhythmia classification, Sensors 23 (2023). URL: https://www.mdpi.com/1424-8220/
23/9/4353. doi:10.3390/s23094353 .
[16] M. Kumar, A. Kumar, S. Verma, P. Bhattacharya, D. Ghimire, S.-h. Kim, A. S. M. S. Hosen,
Healthcare internet of things (h-iot): Current trends, future prospects, applications, chal-
lenges, and security issues, Electronics 12 (2023). URL: https://www.mdpi.com/2079-9292/
12/9/2050. doi:10.3390/electronics12092050 .
302