Iot related articles published in cs & it proceedings from january 2020 to december 2020

IOT RELATED ARTICLES PUBLISHED IN CS
& IT PROCEEDINGS FROM JANUARY 2020 TO
DECEMBER 2020
INTERNATIONAL JOURNAL OF UBICOMP (IJU)
ISSN: 0975 - 8992 (ONLINE) ISSN 0976 - 2213(PRINT)
http://www.airccse.org/journal/iju/index.html

AN INTERNET-OF-THINGS APPLICATION TO ASSIST THE DETECTION OF
FALLING TO THE GROUND
Yifei Yu1
, Yu Sun2
, Fangyan Zhang3
,
1
USA, 2
California State Polytechnic University, USA, 3
ASML, USA
ABSTRACT
As people get old, the risk of them falling increases; the fall will impact senior citizens more
negatively than younger people. My grandmother once fell and hit her when she was alone at home,
and she instantly became unconscious. Frequently, senior citizens are unable to help themselves after
they fall, even if they remain conscious. However, there isn’t a product that senior citizens can use to
notify their relatives right away if they fall, and this leads to the question of how we can bring
immediate aid to all senior citizens after they fall. This paper brings forward the product and software
that can solve this problem. The product is a small wristband that detects any falls or collisions and
notifies relatives right away. The software is an accompanying app that shows the data recorded from
those falls or collisions, specifically designed for family members to keep track of their elders. We
applied our application during our test sessions and conducted a qualitative evaluation of the approach.
The results show that this experiment is a great solution to our problem, but with a few limitations and
weaknesses.
KEYWORDS
Detection of falling, wristband, iOS, Android.
ABSTRACT URL: https://aircconline.com/csit/abstract/v10n12/csit101206.html
PAPER URL : https://aircconline.com/csit/papers/vol10/csit101206.pdf
PROCEEDING URL : http://airccse.org/csit/V10N12.html
International Conference on Machine Learning Techniques and NLP (MLNLP 2020), October 24-25, 2020,
Sydney, Australia

REFERENCES
[1] Important Facts about Falls. 10 Feb. 2017,
www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html. Lee, S.hyun. & Kim Mi Na, (2008)
“This is my paper”, ABC Transactions on ECE, Vol. 10, No. 5, pp120-122.
[2] Mubashir, Muhammad, Ling Shao, and Luke Seed. "A survey on fall detection: Principles and
approaches." Neurocomputing 100 (2013): 144-152.
[3] Wu, Falin, et al. "Development of a wearable-sensor-based fall detection system." International
journal of telemedicine and applications 2015 (2015).
[4] Mubashir, Muhammad, Ling Shao, and Luke Seed. "A survey on fall detection: Principles and
approaches." Neurocomputing 100 (2013): 144-152.
[5] Matias, Igor, Nuno Pombo, and Nuno M. Garcia. "Towards a Fully Automated Bracelet for Health
Emergency Solution." IoTBDS. 2018.
[6] Arduino, Store Arduino. "Arduino." Arduino LLC (2015).
[7] Levy, Paul Blain. "Thunkable implies central." (2020).
[8] Kitestring, www.kitestring.io/.
[9] Do, Thanh-Nghi, et al. "Classifying very-high-dimensional data with random forests of oblique
decision trees." Advances in knowledge discovery and management. Springer, Berlin, Heidelberg,
2010. 39-55.
[10] Burrell, Jenna. "How the machine ‘thinks’: Understanding opacity in machine learning
algorithms." Big Data & Society 3.1 (2016): 2053951715622512.
[11] Michie, Donald, David J. Spiegelhalter, and C. C. Taylor. "Machine learning." Neural and
Statistical Classification 13.1994 (1994): 1-298.
[12] Beta, Brodie. “Guardly: An IOS App That May Save Your Life.” The Next Web, 7 Apr. 2011,
thenextweb.com/apps/2011/04/08/guardly-an-ios-app-that-may-save-your-life/.
[13] IMANI, ANITA. "Design and development of a user interface for a mobile personal indoor
navigation assistant for the elderly." (2014).
[14] El-Bendary, Nashwa& Tan, Qing & Pivot, Frederique & Lam, Anthony. (2013). Fall detection
and prevention for the elderly: A review of trends and challenges. International Journal on Smart
Sensing and Intelligent Systems. 6. 1230-1266. 10.21307/ijssis-2017-588.
[15] Chaudhuri, Shomir et al. “Fall detection devices and their use with older adults: a systematic
review.” Journal of geriatric physical therapy (2001) vol. 37,4 (2014): 178-96.

SMARTTANK: AN INTERNET-OF-THINGS (IOT) APPLICATION TO AUTOMATE
THE WATER TANK REFILLING USING COMPUTER VISION AND AI
Henry Hamilton1
, Yu Sun2
, Fangyan Zhang3
,
1
USA, 2
California State Polytechnic University, USA, 3
ASML, USA
ABSTRACT
This system provides a method of automatically keeping water bowls full and refilling every time it is
detected that they are not. This is highly useful for anyone who owns a pet, as it decreases the amount
of work the owner will need to do. The system uses an AI model, trained with over a thousand images
of water bowls. This allows it to accurately determine when a bowl needs filling. When an empty bowl
is spotted, a subsystem consisting of a valve and other electronic parts releases stored water into the
bowl. Through experimentation it has been shown the accuracy of the system is about 97% under
optimal lighting conditions. Without a light source, the system does not function. Currently, the
components are not of the highest quality and the system only works with the bowl used in testing.
There are future plans to train the model with new pictures featuring an assortment of bowls.
Additionally, an LED could be added to the system to solve the issue of it not working without
external light.
KEYWORDS
Artificial Intelligence, image detection, RPI system processor.
Sydney, Australia

REFERENCES
[1] Poffenroth, Kevin. "Animal drinking water supply apparatus." U.S. Patent 5,452,683 issued
September 26, 1995.
[2] Ewell, Anthony S. "Automatic pet food dispenser." U.S. Patent 5,433,171 issued July 18, 1995.
[3] Krishnamurthy, S. "Automatic pet waterer." U.S. Patent 6,928,954 issued August 16, 2005.
[4] King, Wayne. "Automatic waterbowl for pets." U.S. Patent 6,253,709 issued July 3, 2001.
[5] Graves, James. "Endless water bowl." U.S. Patent Application 10/367,019 filed July 28, 2005.
[6] Honeycutt, Jennifer A., Jenny QT Nguyen, Amanda C. Kentner, and Heather C. Brenhouse.
"Effects of water bottle materials and filtration on Bisphenol A content in laboratory animal drinking
water." Journal of the American Association for Laboratory Animal Science 56, no. 3 (2017): 269-272.
[7] Sexton, James E. "Pet food dish elevating assembly." U.S. Patent 5,584,263, issued December 17,
1996.
[8] Olde, Jarl Rune. "Automatic water dispenser." U.S. Patent 3,868,926, issued March 4, 1975.
[9] Norris, J. (2003). U.S. Patent Application No. 10/426,865.
[10] BENLİAY, A., & ALTUNTAŞ, A. (2019). Visual Landscape Assessment with the Use of Cloud
Vision API: Antalya Case. International Journal of Landscape Architecture Research (IJLAR) EISSN:
2602-4322, 3(1), 07-14.
[11] Othman, Z., Abdullah, N. A., Chin, K. Y., Shahrin, F. F. W., Ahmad, S. S., & Kasmin, F. (2018).
Comparison on Cloud Image Classification for Thrash Collecting LEGO Mindstorms EV3 Robot.
International Journal of Human and Technology Interaction (IJHaTI), 2(1), 29-34.

A SMART INTERNET-OF-THINGS APPLICATION FOR SHOE RECOMMENDATIONS
USING PRESSURE SENSOR AND RASPBERRY PI
Yutian Fan1
, Yu Sun2
and Fangyan Zhang3
, 1
Milton Academy, USA, 2
California State Polytechnic
University, USA 3
ASML, USA
ABSTRACT
Running is one of the most important and simple sports spanning various ages, which can train
throughout body and muscle. For running, proper shoes not only improve runners’ performance but
also protect them from injury to some extent. However, runners have difficulty in finding a pair of
shoes which fit runners’ gait patterns and feet shape very well. The process of selection of shoes is not
effective and necessarily accurate. In this paper, we propose a new tool which facilitates the process by
employing electronic sensors to the insoles of shoes and collecting feet information for runner
accurately. It is helpful for runners to find the best fit shoes.
KEYWORDS
Machine learning, Firebase, Mobile application, Model fitting.
Sydney, Australia

REFERENCES
[1] Young, W. B., R. James, and I. Montgomery. "Is muscle power related to running speed with
changes of direction?" Journal of Sports Medicine and Physical Fitness 42, no. 3 (2002): 282-288.
[2] McKenzie, D. C., D. B. Clement, and J. E. Taunton. "Running shoes, orthotics, and injuries."
Sports medicine 2, no. 5 (1985): 334-347.
[3] Wezel, Frank V., and Terry Mackness. "Running shoes." U.S. Patent 4,624,061, issued November
25, 1986.
[4] Richards, Craig E., Parker J. Magin, and Robin Callister. "Is your prescription of distance running
shoes evidence-based?." British journal of sports medicine 43, no. 3 (2009): 159-162.
[5] Alsalemi, Abdullah, Yahya Al Homsi, Mohammed Al Disi, Ibrahim Ahmed, FaycalBensaali,
Abbes Amira, and Guillaume Alinier. "Real-time communication network using firebase cloud IoT
platform for ECMO simulation." In 2017 IEEE International Conference on Internet of Things
(iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical
and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 178-182. IEEE, 2017.
[6] Ferdoush, Sheikh, and Xinrong Li. "Wireless sensor network system design using Raspberry Pi and
Arduino for environmental monitoring applications." Procedia Computer Science 34 (2014): 103-110.
[7] Upton, Eben, and Gareth Halfacree. Raspberry Pi user guide. John Wiley & Sons, 2014.
[8] Butler, Margaret. "Android: Changing the mobile landscape." IEEE Pervasive Computing 10, no. 1
(2010): 4-7.
[9] Seabrook, Heather J., Julie N. Stromer, Cole Shevkenek, AleemBharwani, Jill de Grood, and
William A. Ghali. "Medical applications: a database and characterization of apps in Apple iOS and
Android platforms." BMC research notes 7, no. 1 (2014): 573.
[10] Janssen, Mark, JeroenScheerder, Erik Thibaut, AarnoutBrombacher, and Steven Vos. "Who uses
running apps and sports watches? Determinants and consumer profiles of event runners’ usage of
running-related smartphone applications and sports watches." PloS one 12, no. 7 (2017): e0181167.
[11] Mial, Yurri. "Shoe sole." U.S. Patent Application 29/664,224 filed May 14, 2019.
[12] What It’s Like To Run In Under Armour’s HOVR Sonic Running Shoes
https://www.besthealthmag.ca/best-you/running/hovr-sonic/

AN INTERNET OF THINGS (IOT) SOLUTION TO OPTIMISE THE LIVESTOCK FEED
SUPPLY CHAIN
David Raba1
, Salvador Gurt2
, Oriol Vila2
and Esteve Farres2
, 1
Universitat Oberta de Catalunya, Spain
and 2
Insylo Technologies Inc., Spain
ABSTRACT
The animal feed supply chain to farm, mainly represented by the feed suppliers and livestock farmers,
currently faces great inefficiencies due to outdated supply chain management. Stakeholders struggle
with the timing and quantity evaluation when restocking their feed bins, significantly affecting cost
and labour efficiency. However, the lack of accurate and cost-effective sensors to measure stock levels
of solid materials stored in containers and open piles is preventing the implementation of these
strategies in a large number of industrial sectors. In these cases, traditional technologies cannot offer a
convenient solution due to an inevitable trade-off between accuracy and cost. This work develops an
integral feedstock management system to optimise the entire supply chain. A new monitoring system
based on an RGB-D sensor is presented as well as the data processing pipeline from raw depth
measurements to bin specific daily consumption rates.
KEYWORDS
Inventory management, Vendor Managed Inventories, Internet of Things
PAPER URL : http://aircconline.com/csit/papers/vol10/csit100409.pdf
6th
International Conference on Natural Language Processing (NATP 2020), April 25 ~ 26, 2020,
Copenhagen, Denmark

REFERENCES
[1] The Food and Agriculture Organization (FAO), “FAOSTAT: Food balance sheet,” 2011.
[2] D. Tilman, C. Balzer, J. Hill, and B. L. Befort, “Global food demand and the sustainable
intensification of agriculture,” Proceedings of the National Academy of Sciences, vol. 108, no. 50, pp.
20260–20264, 2011.
[3] R. Cutler, “Managing pig health: a reference for the farm. 2nd edn. [m muirhead and t alexander]
edited by j carr (editor). 5m publishing, united kingdom, 2013. 654 pages. a$270.00. isbn
9780955501159.,” Australian Veterinary Journal, vol. 92, no. 10, pp. 388–388, 2014.
[4] The European Commission, Feed for food producing animals. The European Commission, 2018.
[5] J. Christensen, “Binmaster: 3d level sensors can solve the toughest food storage
challenges,” 2019.
[6] J. W. Carson, “Feeding of Bulk Solids: A Review. Bulk Solids Handling,” Bulk Solids
Handling, vol. 20, no. 3, pp. 179–282, 2000.
[7] S. S. Chandra, G. S. Sravanthi, B. Prasanthi, and V. R. R, “Iot based garbage monitoring system,”
in 2019 1st International Conference on Innovations in Information and Communication Technology
(ICIICT), pp. 1–4, April 2019.
[8] M. A. A. Mamun, M. A. Hannan, and A. Hussain, “Real time solid waste bin monitoring system
framework using wireless sensor network,” in 2014 International Conference on Electronics,
Information and Communications (ICEIC), pp. 1–2, Jan 2014.
[9] F. Folianto, Y. S. Low, and W. L. Yeow, “Smartbin: Smart waste management system,” in 2015
IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information
Processing (ISSNIP), pp. 1–2, April 2015.
[10] M. Attaran and S. Attaran, “Collaborative supply chain management: The most promising
practice for building efficient and sustainable supply chains,” Business Process Management Journal,
vol. 13, pp. 390–404, 06 2007.
[11] I. Hunt, J. Browne, and P. Higgins, “Supporting the animal feed industry in an ebusiness
environment using simulation,” E-Business Applications: Technologies for
Tomorrow’s Solutions, pp. 171–190, 2003.
[12] S. Krˇco, B. Pokri´c, and F. Carrez, “Designing iot architecture (s): A european perspective,” in
Proceedings of the 2014 IEEE World Forum on Internet of Things, (Piscataway, New Jersey), pp. 79–
84, doi:10.1109/WF–IoT.2014.6803124, Institute of Electrical and Electronics Engineers, Inc, 2014.
[13] F. Ramparany, F. G. Marquez, J. Soriano, and T. Elsaleh, “Handling smart environment devices,
data and services at the semantic level with the FI-WARE core platform,” in Proceedings of the 2014
IEEE International Conference on Big Data, (Piscataway, New Jersey), pp. 14–20,
doi:10.1109/BigData.2014.7004417, Institute of
Electrical and Electronics Engineers, Inc, 2014.
[14] M. Bauer, E. Kovacs, A. Sch¨ulke, N. Ito, C. Criminisi, L. Goix, and M. Valla, “The context api
in the oma next generation service interface,” in 2010 14th International Conference on Intelligence in
Next Generation Networks, pp. 1–5, Oct 2010.

[15] K. Khoshelham and S. O. Elberink, “Accuracy and resolution of kinect depth data for indoor
mapping applications,” Sensors, vol. 12, no. 2, pp. 1437–1454, 2012.
[16] P. Rosin, Y.-K. Lai, L. Shao, and Y. Liu, RGB-D Image Analysis and Processing. 01 2019.
[17] J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and T. R.
Evans, “Reconstruction and representation of 3d objects with radial basis functions,” in Proceedings of
the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01,
(New York, NY, USA), pp. 67–76, ACM, 2001.
[18] S.-W. Cheng, T. K. Dey, and J. Shewchuk, Delaunay Mesh Generation. Chapman & Hall/CRC,
1st ed., 2012.
[19] E. Haque, “Estimating bulk density of compacted grains in storage bins and modifications of
janssen’s load equations as affected by bulk density,” Food Science & Nutrition, vol. 1, no. 2, pp. 150–
156, 2013.
[20] R. Bhadra, M. Casada, A. Turner, M. Montross, S. Thompson, S. McNeill, R. Maghirang, and J.
Boac, “Stored grain pack factor measurements for soybeans, grain sorghum, oats, barley, and wheat,”
Transactions - American Society of Agricultural Engineers: General Edition, vol. 61, pp. 747–757, 1
2018.
[21] O. Willems, S. Miller, and B. Wood, “Aspects of selection for feed efficiency in meat producing
poultry,” World’s Poultry Science Journal, vol. 69, no. 1, pp. 77–88, 2013.
[22] S. Giancola, M. Valenti, and R. Sala, A Survey on 3D Cameras: Metrological Comparison of
Time-of-Flight, Structured-Light and Active Stereoscopy Technologies. SpringerBriefs in Computer
Science, Springer International Publishing, 2018

DATA PREDICTION OF DEFLECTION BASIN EVOLUTION OF ASPHALT
PAVEMENT STRUCTURE BASED ON MULTI-LEVEL NEURAL NETWORK
Shaosheng Xu, Jinde Cao and Xiangnan Liu, Southeast University, China
ABSTRACT
Aiming at reducing the high cost of test data collection of deflection basins in the structural design of
asphalt pavement and shortening the long test time of new structures, this paper innovatively designs a
structure coding network based on traditional neural networks to map the pavement structure to an
abstract space. Therefore, the generalization ability of the neural network structure is improved, and a
new multi-level neural network model is formed to predict the evolution data of the deflection basin of
the untested structure. By testing the experimental data of RIOHTRACK, the network structure
predicts the deflection basin data of untested pavement structure, of which the average prediction error
is less than 5%.
KEYWORDS
multi-level neural network, Encoding converter, structural of asphalt pavement, deflection basins,
RIOHTRACK.
International Conference on Big Data, IOT and Blockchain (BIBC 2020), October 24-25, 2020, Dubai, UAE

REFERENCES
[1] E. A. P. Association et al., (2007) “Long-life asphalt pavements–technical version”.
[2] M. Nunn, A. Brown, D. Weston, and J. Nicholls, (1997) “Design of long-life flexible pavements
for heavy traffic”, TRL Limited.
[3] J. Yu, B.-W. Tsai, X. Zhang, and C. Monismith, (2012) “Development of asphalt pavement fatigue
cracking prediction model based on loading mode transfer function”, Road Materials and Pavement
Design, Vol. 13, No. 3, pp. 501–517.
[4] J. Jiang, F. Ni, Q. Dong, F. Wu, and Y. Dai, (2018) “Research on the fatigue equation of asphalt
mixtures based on actual stress ratio using semi-circular bending test”, Construction and Building
Materials, Vol. 158, pp. 996–1002.
[5] Z. Wang and F. Ye, (2020) “Experimental investigation on aging characteristics of asphalt based
on rheological properties”, Construction and Building Materials, Vol. 231, p. 117158.
[6] I. Gschwendt, (2018) “Extending the service life of pavements”, Slovak Journal of Civil
Engineering, Vol. 26, No. 1, pp. 25–32.
[7] S. Islam, A. Sufian, M. Hossain, R. Miller, and C. Leibrock, (2020) “Mechanistic-empirical
designof perpetual pavement,” Road Materials and Pavement Design, Vol. 21, No. 5, pp. 1224–1237.
[8] O. C. Assogba, Y. Tan, X. Zhou, C. Zhang, and J. N. Anato, (2020) “Numerical investigation of
the mechanical response of semi-rigid base asphalt pavement under traffic load and nonlinear
temperature gradient effect”, Construction and Building Materials, Vol. 235, p. 117406.
[9] S. Yang, P. Li, M. Guo, S. Liao, and H. Wu, (2020) “Study on dynamic load monitoring of an
enhanced stress absorption layer”, Frontiers in Materials, Vol. 7, p. 148.
[10] H. Ceylan, M. B. Bayrak, and K. Gopalakrishnan, (2014) “Neural networks applications in
pavement engineering: A recent survey”, International Journal of Pavement Research & Technology,
Vol. 7,No. 6, pp. 434–444.
[11] F. Gu, X. Luo, Y. Zhang, Y. Chen, R. Luo, and R. L. Lytton, (2018) “Prediction of
geogridreinforced flexible pavement performance using artificial neural network approach”, Road
Materials & Pavement Design, Vol. 19, No. 5–6, pp. 1147–1163.
[12] S. Tapkin, A. Cevik, and U. Usar, (2010) “Prediction of marshal test results for polypropylene
modified dense bituminous mixtures using neural networks”, Expert Systems with Applications, Vol.
37, No. 6, pp. 4660–4670.
[13] A. Qadir, U. Gazder, and K. U. N. Choudhary, (2020) “Artificial neural network models for
performance design of asphalt pavements reinforced with geosynthetics”, Transportation Research
Record, Vol. 4, p. 0361198120924387.
[14] X. D. Wang, (2017) “Design of pavement structure and material for full-scale test track”, Journal
of Highway and Transportation Research and Development, Vol. 34, No. 6, pp. 30–37.
[15] X. Wang, (2015) “Discussion of asphalt pavement deflection indicator,” Journal of Highway and
Transportation Research and Development, Vol. 32, No. 1, pp. 1–12.

[16] J. Liao, et al. (2019) “A Correction Model for the Continuous Deflection Measurement of
Pavements Under Dynamic Loads”, IEEE Access, Vol. 7, pp. 154770-154785.
[17] C. Wu, H. Wang, et al., (2020) “Asphalt pavement modulus back calculation using surface
deflections under moving loads”, Computer-Aided Civil and Infrastructure Engineering,
doi:10.1111/mice.12624
[18] C. Wang, S. Pan, R. Hu, et al., (2019) “Attributed graph clustering: A deep attention embedding
approach,” arXiv preprint arXiv:1906.06532.

THE TEMTUM CONSENSUS ALGORITHM – A LOW ENERGY REPLACEMENT TO
PROOF OF WORK
Richard Dennis and Gareth Owenson, University of Portsmouth, United Kingdom
ABSTRACT
This paper presents a novel consensus algorithm deployed within the Temtum cryptocurrency network.
An overview of the proof of work consensus algorithm is presented, and gaps in the research are
outlined. The Temtum consensus algorithm's unique components, including the Node Participation
Document (NPD) and the use of the NIST randomness beacon, are outlined and explained.
Comparisons on the cost to attack the consensus algorithm and energy consumption between the
Temtum consensus algorithm and Bitcoin’s proof of work is presented and evaluated. We conclude
this paper summarising the findings of the research and presenting future work to be conducted.
KEYWORDS
Blockchain, Peer-to-Peer Networks, Crypto currencies, Consensus, Byzantine Fault Tolerance.
PAPER URL : http://aircconline.com/csit/papers/vol10/csit101301.pdf
International Conference on Big Data, IOT and Blockchain (BIBC 2020), October 24-25, 2020, Dubai, UAE

REFERENCES
[1] S. Nakamoto, “Bitcoin P2P e-cash paper,” 2008 October 2008. [Online]. Available:
https://bitcoin.org/bitcoin.pdf.
[2] P. Cuccuru, “Beyond bitcoin: an early overview on smart contracts,” International Journal of Law
and Information Technology, Volume 25, Issue 3, p. 179–195, 2017.
[3] J. Bohr and M. Bashir, “Who Uses Bitcoin? An exploration of the Bitcoin community,” in 2014
Twelfth Annual International Conference on Privacy, Security and Trust, 2014.
[4] A. Biryukov, D. Khovratovich and I. Pustogarov, “Deanonymisation of Clients in Bitcoin P2P
Network,” in CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and
Communications Security, 2014.
[5] J. Soria and V. Savolainen, “Too Big to Cheat: Mining Pools' Incentives to Double Spend in
Blockchain Based Cryptocurrencies,” in SSRN Electronic Journal, 2019.
[6] M. Romiti, A. Judmayer, A. Zamyatin and B. Haslhofer, “A Deep Dive into Bitcoin Mining Pools:
An Empirical Analysis of Mining Shares,” 2019.
[7] L. Lamport, R. Shostak and M. Pease, “The Byzantine generals problem.,” Concurrency: the
Works of Leslie Lamport., p. 203–226, 2019.
[8] Y. Chen and J.-S. Chou, “ID-Based Certificateless Electronic Cash on Smart Card against Identity
Theft and Financial Card Fraud,” in The International Conference on Digital Security and Forensics,
2014.
[9] A. Back, “Hashcash - Amortizable Publicly Auditable Cost-Functions,” 2003.
[10] D. M. A. Cortez, A. M. Sison and R. P. Medina, “Cryptographic Randomness Test of the
Modified Hashing Function of SHA256 to Address Length Extension Attack,” 8th International
Conference on Communications and Broadband Networking, pp. 24-28, 2020.
[11] D. Bradbury, “The problem with Bitcoin,” Computer Fraud & Security, pp. 5-8, 2013.
[12] A. Lamiri, K. Gueraoui and G. Zeggwagh, “Bitcoin Difficulty, A Security Feature,” Information
Systems and Technologies to Support Learning, pp. 367-372, 2018.
[13] S. M. Werner, D. I. Ilie, I. Stewart and W. J. Knottenbelt, “Unstable Throughput: When the
Difficulty Algorithm,” 2020.
[14] E. Budish, “The Economic Limits of Bitcoin and the Blockchain,” NBER Working Paper, 2018.
[15] B. Kaiser, M. Jurado and A. Ledger, “The Looming Threat of China: An Analysis of Chinese
Influence on Bitcoin,” 2018.
[16] A. Vries, “Bitcoin's Growing Energy Problem,” Joule, pp. 801-805, 2018.
[17] Visa, “Annual report 2019,” Visa, 2019.

[18] A. l. o. o. panelJonTruby, “Decarbonizing Bitcoin: Law and policy choices for reducing the
energy consumption of Blockchain technologies and digital currencies,” Energy Research & Social
Science, pp. 399-410, 2018.
[19] C. Ye, G. Li, H. Cai, Y. Gu and A. Fukuda, “Analysis of Security in Blockchain: Case Study in
51%- Attack Detecting,” in 2018 5th International Conference on Dependable Systems and Their
Applications (DSA), 2018.
[20] N. Shi, “A new proof-of-work mechanism for bitcoin,” 2016.
[21] H. Chena, T. N. Conga, W. Yang, C. Tan, Y. Li and Y. Ding, “Progress in electrical energy
storage system: A critical review,” Progress in Natural Science, pp. 291-312, 2009.

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