Setting Up a Free WhatsApp OTP service with C#

Over time, One Time Password has become essential in building products that require users signing in and low-level user verification. Integrating this service is not only expensive but also might be difficult or unstable depending on the platform you decide to use.

They are various ways of achieving OTP service, but this writeup will be focusing on using WhatsApp and C#. The requirements for this exercise include

  1. A smartphone with WhatsApp
  2. Dotnet core SDK
  3. Visual Studio
  4. Basic knowledge of C#

Create a new .Net Core API Project

Create a “Model” folder with the following classes in  it

  1. Message
public class Message
   public string Contact { get; set; }
   public string Text { get; set; }
   public bool IsDigit()
       const string numbers = "0123456789";
       var count = (from ch in Contact
           where !numbers.Contains(ch)
           select ch).Count();
       var isNumber = (count == 0);
       return isNumber;
  1. WhatsAppMetadata
public class WhatsAppMetadata
   public static string MainPanel = "#pane-side";
   public static string SearchField = "._2S1VP";
   public static string UserChat = "#pane-side span[title=\"XXX\"]";
   public static string UserName = "#main > span[title=\"XXX\"]";
   public static string MessageInputField = "._2S1VP";

3. Install Puppeteer

Go to your nugget package manager and install PuppeterSharp. This package is used to crawl the web using C# and chro4me

4. Create a Service folder with a WhatsAppService class

  Add the following properties

This class have five important functions

  1. InitBrowser():  this function will download a chrome browser on the first run and start it
private async Task InitBrowser()
   Debug.WriteLine("Downloading. . .");
   await new BrowserFetcher().DownloadAsync(BrowserFetcher.DefaultRevision);
   _browser = await Puppeteer.LaunchAsync(new LaunchOptions()
       UserDataDir = Path.Combine(".", "user-data-dir"),
       Headless = false
   Debug.WriteLine("Browser downloaded. . .");
  1. InitWhatsApp(): This function will open a new tab and load the WhatsApp page requesting you to scan the barcode
private async void InitWhatsApp()
   var qrCodeAbsolutePath = _binPath.Replace("bin", "") + QrCodeImagePath;
   _whatsAppPage = await _browser.NewPageAsync();
   await _whatsAppPage.GoToAsync(WhatsAppUrl);
       await _whatsAppPage.WaitForSelectorAsync(WhatsAppMetadata.MainPanel);
   catch (Exception)
       Console.WriteLine("As a new User, you need to login");
       await _whatsAppPage.ScreenshotAsync(qrCodeAbsolutePath);
       Console.WriteLine($"send image in {qrCodeAbsolutePath} to your email && print to console");
       await _whatsAppPage.WaitForSelectorAsync(WhatsAppMetadata.MainPanel);
   await _whatsAppPage.ScreenshotAsync(qrCodeAbsolutePath);
  1. SendMessages(): This removes a message from or queue and sends with
private void SendMessages()
   while (_messages.Any())
       var message = _messages.Dequeue();
  1. SendMessage(Message message): This function is responsible for sending the selected message
private async void SendMessage(Message message)
  if (message.IsDigit())
       //load the new url and press send
       var url = NewContactUrl.Replace("<PHONE>", message.Contact).Replace("<MESSAGE>",message.Text);
       await _whatsAppPage.GoToAsync(url);
       await _whatsAppPage.WaitForSelectorAsync(WhatsAppMetadata.MessageInputField);
       await _whatsAppPage.Keyboard.PressAsync("Enter");
       //Message Sent
       Console.WriteLine("Message Sent");
   //Search complete
   var searchInput = await _whatsAppPage.QuerySelectorAsync(WhatsAppMetadata.SearchField);
   await searchInput.TypeAsync(message.Contact);
   await _whatsAppPage.WaitForTimeoutAsync(500);  
   var fiendLocation = WhatsAppMetadata.UserChat.Replace("XXX", message.Contact);
   var contactHandle = await _whatsAppPage.QuerySelectorAsync(fiendLocation);
   await contactHandle.ClickAsync();
   var messageSplit = message.Text.Split("\n");
   foreach (var word in messageSplit)
       await _whatsAppPage.Keyboard.DownAsync("Shift");
       await _whatsAppPage.Keyboard.PressAsync("Enter");
       await _whatsAppPage.Keyboard.UpAsync("Shift");
       await _whatsAppPage.Keyboard.TypeAsync(word);
   await _whatsAppPage.Keyboard.PressAsync("Enter");
  1. AddMessage(Message message): There’s is no direct method to call from an external class to send messages, instead this method is called to add a new message to an already existing queue.
public void AddMessage(Message message)
   if (_messages.Count == 1)

Go to your Startup.cs class and inject WhatsAppService as a singleton class

var whatAppService = new WhatsAppService();

With all this done, you can go ahead and use the WhatAppService in your controllers to send Messages (including users One Time Password)

Privacy and Security issues in Smart Homes (IoT)


IOT – Internet of Things

SH – Smart Homes

SHS – Smart Home Systems

BCS – British Computer Society

IEEE – Institute of Electrical and Electronics Engineers

UK- United Kingdom

Table of Contents










There has been a remarkable increase in the number of digital devices in this present age, through the constant development of automated sensors, RFID Tags, and other digital-based components, that enable individuals or industries to perform activities with greater efficiency and also lead to enhanced productivity (Briere, 2013). This has resulted in a mutual dependency and interactivity between humans and computerized devices, for various purposes ranging from leisure, health, education, and business amongst others (Nagender, 2016). Therefore, this has also led to the increased popularity of Internet of Things (IoT), because individuals have not just realized the need for utilizing digital devices, but they also require all their digital devices to be interconnected and seamlessly interact with each for enhanced performance (Augusto, et al., 2006).

Borgeson (2013) defined Internet of Things (IoT), as the unified interconnection of embedded-computerized devices, within an internet (web-based) infrastructure. Internet of Things (IoT) empirically offers an advanced-interconnectivity of various digital systems, networks and devices with the primary objectivity of enhancing the interactivity between human and machines (McEwen, 2014 ). Smart Homes has been a major component of Internet of Things (IoT) and various smart homes elements such as Apple’s HomeKit, Samsung’s Smart-Things and Google’s Brillo have been widely recognized and accepted all over the world (Okadome, et al., 2003 ).

A smart home can be defined as a confined-physical environment which consists of digital devices such as actuators, sensors and other computerized elements that are all inter-connected to each other, and exchange information seamlessly in order to offer an optimized and personalized experience to users (Mokhtari, 2009). The potentials and benefits of Smart Home has been extremely huge as most medical agencies now design Smart Homes for elderly patients, through which it is capable of actively sensing and processing vital health data and then relaying it to the patients through the various integrated and inter-connected systems (Abowd, 2003).

This has presented individuals, businesses, industries and societies with immense benefits which is valued globally. According to Cisco, the global smart-home value is estimated to exceed $47billion by the year 2020. Despite the advantageous effects and value of Smart-Homes, some privacy and security issues have remained predominant, therefore putting the inhabitants of smart homes to be at high anonymity risk and security challenge which would be discussed in this report.

Finally, based on the objective and learning outcomes of this module which primarily includes educating students the foremost legal, ethical, social, and professional components of Information Systems, this intrinsic examination of Smart Homes was selected, being one of the vast information systems under IoT.  The privacy and security challenges relating to Smart Homes with all respective Ethical, Professional, Social, and Legal issues would be addressed in this report.


Firstly, to put things into perspective, Privacy can be defined as a state of optimum liberty or solitude whereby an individual, their belonging or information is not being interrupted or interfered with in any form (Hubaux, et al., 2004). Security in simple terms, deals with the absence of dangers or threats, and this study would primarily focus of digital (data) privacy and security. Mohktari (2009) itemized that digital privacy and security helps to give individuals all the necessary freedom needed to conveniently perform their desired task or interact with others without any risk of external interference or danger. This helps to enhance reliability and dependability. Smart Homes falls under the field of Technology referred to as Internet of Things (IoT), which is gradually revolutionizing the way in which Humans and Machines or other computerized systems relate (Khurana, et al., 2010). The privacy and security issues relating to Internet of Things (IOT) largely involves data breaches, hacking and Denial of Service (DDOS) attack which most IOT Systems are prone to (Gaglio, 2014).

However, to be specific, the highly sensitive and heterogeneous nature of smart homes has resulted into some prevalent privacy and security concerns which would be the focal point of this report. This concerns primarily ranges from the ability of Smart Homes Sensors and other inter-connected devices to effectively predict the actions of individuals, as a result of retrieved data or constant interaction with humans (Miller, 2015). Additionally, the possibility of hackers to penetrate through Smart-Homes systems and retrieve confidential information relating to individuals has also created more privacy and security concerns (Borgeson, 2013). This is because, in some Smart Homes especially those made for old people or sick patients, the medical records to the Smart-Home occupants are embedded to the system to enable the various sensors and controllers to interact smartly with individuals (Knapp, et al., 2013). Going forward, the social, ethical, professional and legal issues with Smart-Homes would be elaborated in connection to the privacy and security concerns stated.


In the field of Information Technology, there has been an inherent and urgent quest to ensure that people make judicious use of computer technologies in a way that clearly guarantees respect of other individuals (Waradpande, 2015). Abowd (2003) itemized that the fundamental way through which an action can be decided on whether it brings respect and value for others is by judging on whether such action is ethical or unethical, and this can be done using standard ethical theories or principles such as the Deontological ethics or Utilitarianism.

An ethical issue relating to smart-homes is the collection of BIG DATA about the smart home inhabitant. It is a well-known fact that smart-homes consist of numerous interconnected sensors, networks and other digital components that are interconnected to function uniformly. These sensors or other smart home components are integrated at various locations in homes, offices or other IOT-Based environments and have the capability to sense the movement, temperature, health details and sensitive details of smart home occupants (Borgeson, 2013). However, Minoli (2016) explained that the collection of excessive data by smart home system is unethical based on the Kantian Ethics. This ethical theory primarily judges’ actions to be either ethical or unethical based on the level of respect and moral-esteem exhibited (Pathy, et al., 2017). In this case, the collection of excessive reflect disregard and respect for the privacy of the smart home inhabitants (Briere, 2013).

To further explain this point, Fiona (2016) explained that although social networks, cloud computing platforms and other technologies are criticized for collection of numerous personal data of users, however an argument can be made that no existing Information System obtains more Data about users than Smart Homes (Waradpande, 2015). This puts smart-homes inhabitants at high privacy risk. Also, the Fair Information Protection Policy (FIPP) which aims to enhance data privacy protection explains that Data Minimization is an essential component of enhancing privacy protection, however most smart home systems fall short of this (Briere, 2013).

On the other hand, Gaglio (2014) affirmed that the collection of Big data about users by Smart Homes Systems (SHS) and other IOT components is performed so as to make real-time decisions based on the information. The sensitive nature of smart homes which performs intimate actions such as medical prescription, food recommendations and many intimate functions based on the lifestyle or other records of users, makes it necessary for sufficient data about the smart home inhabitants to be obtained (Calvary, 2013). Therefore, the collection of excessive data by Smart Homes Inhabitants is ethical based on consequentialism which judges any action on the basis of its consequences (end-result) which the action presents. In this case, the end result/consequence of large dataset collection is the optimization and utmost improvement in the service delivery and effectiveness of smart home components (Helal, 2008).

Also, Wechsler (2012) also identified that another similar ethical issue relating to smart homes is Eavesdropping, which simply refers to the process whereby the manufacturers or developers of smart-homes components digitally invade smart-home environment. This is done so as to secretly examine the kind of cloths that people love, the TV Series they watch or other related information and this data is then transferred to third-parties primarily for commercial purposes, without any consent or knowledge of the users (Fiona, 2016).

Therefore, judging by the Kantian ethical principle, the Eavesdropping of smart home users’ activities is unethical. The Kantian ethical principle also known as Kantianism examines actions based on the level of respect which it offers other humans based on sound moral doctrines that promotes stability (Fiona, 2016). In this case, the eavesdropping of smart-homes users is morally wrong and portrays gross disrespect for the users, because the manufacturers prioritize business benefits rather than the privacy protection of Smart-Homes Inhabitants (Borgeson, 2013).

Correspondingly, Miller (2015) maintained that a core-part of privacy involves data confidentiality which basically prevents the disclosure of people’s information to unauthorized parties, irrespective of any situation. However, the eavesdropping of users’ data and then transferring it to other parties is morally noxious which indicates that the privacy of smart home inhabitants is the least priority of most smart home manufacturers or developers (Helal, 2008).


The advent of Internet of Things (IoT) and Smart Homes in particular, have been globally recognized as a key element for social development as a result of its evident impact on the society and the surrounding environment (Wechsler, 2012).

A primary social concern of smart homes and other related IoT-based system as stated by Helal (2008) is the resultant change or impact of smart homes systems on human behaviors. Smart Homes greatly influences on human behavior as it creates emotional distress or behavioral changes within humans due to the awareness of being surrounded by sensing technologies (Borgeson, 2013). Miller (2015) explained that normally a home is supposed to act as an intimate location whereby people could express their complete freedom and liberty as there is no external monitoring or intrusion (Wechsler, 2012).

However, this is not applicable to smart home occupants who tend to live by extreme caution and confinement knowing that they are literally being monitored, studied and recorded (Briere, 2013). Also, previous cases whereby smart homes systems such as the Netgear Arlo-Pro smart (home) security cameras were sabotaged, thereby various details/video footages of smart home users in Manchester, UK were leaked (Calvary, 2013). These incidences tend to create immense discomfort, fear and anxiety amongst inhabitants of smart homes, even elderly/sick people who are placed within smart spaces for medical purposes have to alter their normal lifestyle (Borgeson, 2013). In addition, Ecological (environmental) impacts of smart homes have equally generated some social concerns (Miller, 2015). The Environmental Protection Board (EPB, 2017) have reported that the operational interconnection of various Smart Homes constituents such as sensors, automated Smart-Lightings Systems, Heaters and other motion controls that control environmental forces such as Temperature and humidity usually affects the environment (Miller, 2015). This is through poisonous emission of used gases or biodegradable artifacts which is detrimental to the environment and other surrounding individuals (Miller, 2015).

However, on the opposing side, others authors such as Nagender (2016) explained that of all the modern technologies developed over the last decades, it could be said that Smart Homes and other pertinent IoT infrastructures have presented the most profound social benefits (Waradpande, 2015).

Smart Homes is presently used by most medical specialist and firms in order to optimize medical service delivery as sick people or elderly ones are placed within smart spaces, with all the relevant smart systems which could effectively sense their temperate and also perform actions such as adjusting the home temperature or lightings just to suite the health requirements of ill/elderly persons (Briere, 2013). Also, Moolayil (2016) further explained that other attributes of smart homes such as Smart fire detection, through which the various interconnected smart-home sensors or components could effective detect any incoming fire outbreak and then automatically relay this details to appropriate parties. This has severally help to prevent fire outbreak and other man-made hazards, thereby leading to safety of lives and properties in the society (McEwen, 2014 ).


Despite the importance or benefits of any technology such as Smart Home, it is important to ensure that all the actions or activities of such system is guided within the boundaries of the law, as that is the only way it can be certified as a legitimate technology (Fiona, 2016). Many UK legislations have been drafted to regulate how technological systems should operate, especially while interacting with humans (Helal, 2008).

A fundamental legal concern in smart homes and other IoT based components is the transfer of the personal or confidential information of smart-home residents or users to third parties (Lobaccaro, 2016). Smart homes systems which usually comprises of numerous interconnected systems typically involves the transfer of information relating the smart-home inhabitants via various third party networks (Fiona, 2016). However, a critical assessment of this action indicates that it contradicts and legally violates the Data Protection Act (1998). This is fundamentally because sections eight (8) of the Data Protection Act imposes that it is illegal for the confidential or any form of user’s data to be given out to third parties without the due consent of the users (Mokhtari, 2009).  Likewise, the Freedom of Information Act of 2000 also states that users or customers are entitled to explicitly know why their personal information is being used, and also the users could stop the collection or transfer to their information (Moolayil, 2016). However, most smart home systems are not designed to comply with the edicts from this legislations because smart home inhabitants are not given adequate explanation of why and how their data would be processed (Moolayil, 2016). This is therefore regarded as a violation of the fundamental human rights of smart home inhabitants, as prescribed by Article eight (8) of the Human Rights Act of 1998 which enforces the need for the privacy of all humans to be maximally respected either by firms or even the government (Fiona, 2016).

Also, in order to further dissect this issue, the Freedom of Information Act 2000, summarizes that the basic goals or characteristic of every reliable Information System or digital platform is to promote Confidentiality, Integrity and Availability (CIA) of the users’ data. However, it is practically obvious that most smart homes systems and IOT components in general largely compromise both the integrity and confidentiality of users’ information which is a violation of the Freedom of Information Act (2000) and the Data Protection Act (1998).

However, to objectively view this case for the legal perspective, the illegal access or sabotage of smart home networks by hackers is an illegal act that violates the sections four of the Computer MisUse Act of 1990 which primary states that computer technologies should never be used to perform illicit or mischievous activities which include the unauthorized access to third-party networks (Augusto, et al., 2006).


The usage of smart-homes and other analogous Internet of Things (IoT) based components, have presented some professional issues which is worth examining and discussing. In this case, professional issues simply deal with the impact of IoT and Smart-Homes, in relation to professional codes of conducts which was formed to guide computer professionals and other specialists (Moolayil, 2016). 

The main observation of the Information Security Risk Analysis (ISRA) agency relating to Smart-Homes indicates that hacking or sabotage of smart homes or other IOT-Based components can be fundamentally traced down to the lapses in the design or network configuration of this systems, which the Hackers take advantage of (Briere, 2013). However, this is a serious professional issue because sections 2a of the BCS Code of Conduct advocates that both firms and developers should only undertake or provide services which are have adequate technical and legislative knowledge about. This in other words emphasizes that IT Professionals should never indulge in developing any system or providing services which is beyond their abilities or competence-level. In respect to this therefore, it could be ascertained that most companies that either manufacture or develop smart home system do not have the sufficient technical knowledge or infrastructural resources to ensure total security of the smart home systems without any form of leakage or backdoors (McEwen, 2014 ). This therefore, is a contradiction of the section 2A of the BCS Code OF Conduct.

However, from the opposing perspective, most manufacturers of Smart Home Systems such as  Amazon, have all highlighted the fact that most of the fundamental issues relating to smart homes deals with the illicit hacking and sabotaged of this systems (Abowd, 2003).This is not solely the fault of the Smart Home Manufacturers as various parties such as Internet Service Providers (ISP) are also involved in the complete functioning of smart homes (Schwartz, 2016). However, to further put things into perspective, Abowd (2003) referenced Sections 2F of the BCS Code of Conduct. This states that both computer professionals/specialists or other users should never make or sabotage the works of others through malicious acts such as illicit hacking which is professionally prohibited (Moolayil, 2016). Therefore, proper adherence to section 2F of the BCS code of conduct (either by professionals or users) would eliminate enormous issues relating to Smart Homes.

Also, it is impossible to explicitly discuss the professional issues relating to Smart Homes and other IoT based components without making reference to the excessive collection of data relating to Smart Home occupants, as this has been a prolonged professional concern (Moolayil, 2016). In respect this issue, Gaglio (2014) explained that the First Section (1a) of the BCS Code of Conduct emphasizes the need for firms or professionals to respect the autonomy and privacy of others within their environment. However, this author further explained that most Smart Home Systems and other Internet of Things (IoT) are designed to extract and amass excessive private data about individuals and this does not depict that the privacy of users (smart-home inhabitant) are being respected in any form just as stated by the section 1A of the BCS Code of Conduct (Gaglio, 2014).

Although, most professionals that manufacture smart home systems might argue that it is impossible for the devices to function adequately with sufficient information about users. Borgeson (2013) reaffirmed that section 4B of the BCS code of conduct emphasizes that need for professionals to continuously seek development and optimization of their product and services either through trainings or other forms of professional development. In this respect, the smart home system developers should constantly try to make professional modifications to their systems so as to enable it function effectively even without obtaining excessive data from individuals (Helal, 2008). Finally, Wechsler (2012) explained that even after the personal data of Smart-Home Inhabitants have been obtained, it is essential that Section 1.8 of the Applied Computing Machinery (ACM) Code of conduct should be upheld. This code simply enforces the need for enforcing confidentiality of all personal data of users obtained, and a definite way of maintaining this is to avoid leaking users information to any third-party mediums without adequate (due) consent of the users (which in this case is the smart-home inhabitants) (Briere, 2013).


Smart Homes and other related Internet of Things (IoT) Systems have continued to experience constant reformations and upgrades which has resulted into monumental enhancement in its performance and operational capabilities (Abowd, 2003). However, some common limit and vulnerabilities exist in most smart home systems which creates substantial security and privacy risk, therefore making it essential to address it in this study.

A significant limit and vulnerability in this case, deals with the a lot of data breaches associated with most smart home system (Augusto, et al., 2006). Internet of Things (IoT) and Smart-Homes in particular comprises of various digital components which are interconnected to a central network within the internet (Wootton, et al., 2006). This might include specialized integrated networks, commercial servers or the cloud. However, hackers presently take advantage of the security gaps and loopholes of IoT networks, by deciphering encrypted data relating to smart home inhabitants (Borgeson, 2013).

However, security experts such as Okadome (2003) highlighted that this issue is largely as a result of the negligence of Smart Home System developers to adequately stiffer encryption and security measures.  In addition, an intrinsic research conduct by MIT Lab practically indicated that asides the infiltration of smart home networks by hackers, even Internet Service Providers (ISPs) could passively assess and analyses the IoT mainstream networks (Borgeson, 2013). This therefore makes the confidential information of smart home inhabitants vulnerable to data breaches.

Also, another common vulnerability that tends to limits the effective functioning of Smart Homes is the Denial of Service (DOS) attacks, which usually occurs due to the injection of destructive Trojans, Botnets or Malware into IoT Networks (Hubaux, et al., 2004). This tends to complete alter the operations of the central networks which facilitates data transfer and interconnectivity of all the digital components connected in smart home or IOT ecosystem (Moolayil, 2016).


Internet of Things (IoT) and Smart Homes, are both regarded as one of the most influential technologies, and probably one of the few technologies which has had the most impact on Society (The developed World). However, as just as the utilization and proficiency of smart homes have continued to increase, so has the privacy and security risks associated with this technology.

A Smart home comes loaded with benefits such as: Managing all of your home devices from one place, Flexibility for new devices and appliances, Remote control of home functions, increased energy efficiency and Home management insights.

The excessive gathering of user data has raised a lot of ethical, social, and legal concerns as this has led to privacy evasion and behavioral changes of smart home occupants, Authors such as Knapp and Schmitt (2013) explained that despite various technologies such as social media platforms and augmented reality frameworks that extensively work with people’s data, it can never be compared to the magnitude of information which smart home components generate & gather.

All this has led to genuine calls for enhancement of smart-home architectures through stiffer encryption protocols, strict adherence to data protection policies and also forensic techniques, in order to protect the privacy and security of smart-home inhabitants.


Abowd, G., 2003. Smart homes or homes that smart?. ACM SIGCHI Bulletin, 12(12), pp. 123-221.

Augusto, J. C., Nugent, C. & Dricker, D., 2006. Designing Smart Homes. IV Edition ed. Berlin: Springer.

Borgeson, S. D., 2013. Targetted Efficiency. 1st Edition ed. Berkeley, CA: Springer.

Briere, D. H., 2013. Smart Homes for Dummies. 2nd Edition ed. Hoboken: New Jersey.: John Wiley & Sons.

Calvary, G., 2013. Computer science and Ambient Intelligence. 3rd Edition ed. London, United Kingdom: Iste.

Fiona, N. F.-H., 2016. HCI in Business, Government, and Organizations: Information Systems. 1st ed. Cham Palace, United Kingdom: Springer International Publishing.

Gaglio, S., 2014. Advances onto the Internet of Things (IOT). 3rd Edition ed. Cham: Springer International Publishing.

Helal, A. A., 2008. Smart homes and health telematics. 1st Edition ed. Berlin: Springeer.

Hubaux, J., Capkun, S. & Luo, J., 2004. The security and privacy issues in Smart Vehicles. IEEE Security & Privacy Magazine, 3(2), pp. 49-55.

Khurana, H., Hadley, M., Ning, L. & Frinckle, D., 2010. Smart-Grid Security Issues. IEEE Security & Privacy Magazine, 8(1), pp. 81-85.

Knapp, E., Schmit, D. & Samani, R., 2013. Applied Cyber Security and Smart Grid. 1st Edition ed. Burlington: Elsevier Science Press.

Lobaccaro, G., 2016. A Review of Systems and Technologies for Smart Homes and Smart Grids. Energies, 9(5), p. 348.

McEwen, A., 2014 . Designing the Internet of Things. 3rd Edition ed. Chichester: Wiley.

Miller, M., 2015. The Internet of Things. 12th Edition ed. New York City: Que Corporation.

Mokhtari, M., 2009. Ambient assistive health and wellness management in the heart of the city. 2ND EDITION ed. Berlin Heildelberg,: Springer.

Moolayil, J., 2016. Smarter Decisions – The Intersection of Internet of Things and Decision Sciences. 2nd Edition ed. Birmingham, UK: Pack Publishing.

Nagender, K. S., 2016. Smart Homes. IV Edition ed. Chicago: Springer.

Okadome, T., YAMAZAKI, T. & Makhtari, M., 2003 . Pervasive computing for quality of life enhancement. IV Edition ed. Amsterdam: IOS Press.

Pathy, M. S., Sinclair, A. & Morely, J. E., 2017. Principles and practice of geriatric medicine. 2nd Edition ed. Chichester: United Kingdom.

Schwartz, M., 2016. Internet of Things with ESP8266. 1st Edition ed. Orlando: Packt Publishing.

Waradpande, P., 2015. Activity recognition using radars. 5th Edition ed. Oakland, CA: Springer Publishers.

Wechsler, H., 2012. Biometric Security and Privacy Using Smart Identity Management and Interoperability: Validation and Vulnerabilities of Various Techniques. Review of Policy Research, 29(1), pp. 63-89.

Wootton, R., Dimmick, S. L. & Kvedar, J. C., 2006. Home Telehealth. 3rd Edition ed. Los Angeles, California.: Hodder Education.


Bitcoin is seen as many things to different people.  Some people see it as a currency used for value exchange, some see it a volatile digital asset that “is not backed by anything”, some see it as a brilliant use case of blockchain technology.

The bitcoin is simply a ‘crypto’currency built on the blockchain to allow person to person value transfer without restrictions from any central body.

We might have also heard the term “Decentralization” used a lot. This simply means that there is no central authority governing the bitcoin network, its governance is overseen by anyone who cares to join the governing network.

This story will start from breaking up the word cryptocurrency into two distinct parts:

  1. Crypto: Crypto comes from the word cryptography which is the art of securing communication in the presence of enemies or adversaries 
  2. Currency: Currency is simply a medium of exchange of goods and services.

Combining both definitions in layman terms we can simply say cryptocurrency is the technique of exchanging goods and services in a secure way in the presence of enemies.

Anyone can read this story but it can get quite technical at some point. At any point you are unable to understand the technical parts, please go and learn about that topic and come right back. I will try my best to add links to resources where you can learn and practice each technical concept.

I will start my journey by focusing on the Crypto part of the Cryptocurrency. A complete understanding of this part is sufficient to describe how the cryptocurrencies work and why the idea is brilliant.


Cryptography is not a modern technique. It is as old as man. In the ancient times, cryptography mostly focused on message confidentiality(or encryption). For example rearranging “Hello Ugo, how are you?”  to “llHeo oUg, ouy rea woh”? Was a common cryptographic technique employed in the past. See more about history of cryptography here

Cryptography is very important to the study of cryptocurrencies because it provides the following services

  1. Confidentiality – Assurance that information is only available to authorized people.
  2. Integrity – Assurance that information is only modifiable by the authorized person
  3. Authentication – Assurance about the identity of a person or the validity of the source of a message
  4. Non-Repudiation – Assurance that someone can’t deny something

Mathematics sits at the core of cryptography and I will be introducing a few mathematical concepts below. Don’t be scared I have added links to resources that will make learning these concepts fun.

We will be looking at:

  1. Prime Numbers
  2. Sets
  3. Groups(Abelian Groups)

Please make sure you study these concepts before you proceed so my story can be more exciting to you.

So because I don’t trust you. I want you to prove to me you studied the topics above by solving the questions below. Share your answer on the google form(code_name, picture of your solution and goofy picture of you holding your solution). Best goofy picture with a correct solution gets $10 worth of Bitcoin and txId will be shared on a list of My Bitcoin stories beneficiaries. Please try and provide proofs where necessary.

Prime Number Questions
  1. Question 1
  2. Question 2
Set Questions
  1. { Question 1, Question 2, Question 3, Question 4 }
  1. Pick any question online and solve it. Then share with me.


Partnership with Wicrypt, a service that enables users to get paid while sharing their Wifi. Wicrypt uses a proprietary peer to peer internet sharing protocol and leverages the power of the blockchain to enable Individuals(Agents) to resell data providing internet within certain areas (residential and commercial locations).

To use this service, the users can download the app from Google Playstore and Apple app store and top up their wallets via bank transfer, USSD, or cards. Users who can’t download the app can also connect to the service using a voucher.

Once a user is in a location where the Wifi is available (Enugu Wifi Zone), they will be able to connect to the wifi instantly using a router code generated from their Wicrypt mobile app or using a voucher code purchased from an Enugu Wifi Agent or from the Wifi Home page.

Enugu SME Center will be the super-agent with other agents under the Enugu SME Center. From the Dashboard, the Enugu SME Center will know what each Enugu Wifi Agent makes monthly and deducts from the source to repay the loans given to them to buy the router, the range extender, power bank and initial 100GB of data for reselling. We believe that on average each Enugu Wifi Agent can make between ₦150,000 – ₦200,000 in a month.

Machine Learning vs Deep Learning

Artificial Intelligence is the study and practice that enables machines to solve problems like a human (i.e. solving problems intelligently). The broad field of AI is a superset that includes the field of machine learning.

In contrast to traditional programming where explicit steps to achieve a task are provided by the programmer, machine learning enables a machine to perform a task optimally by learning from examples (i.e. through analysis of input data and its relationship with the desired output.) Using the cake baking process as a descriptive analogy, we could either provide a machine with a recipe for baking a sponge cake (traditional programming) or we could provide the machine with cake ingredients and an already baked cake and allow it to learn through trial and error how best to interact with the ingredients in order to get the desired cake (machine learning).

How do machines learn from examples? 

Studies have shown that almost every phenomenon can be modelled, analyzed or explained using mathematical formula. So yes, the magic tool for machine learning is none other than mathematical functions. Supposing we are given an input, say cake ingredients and on another hand, we are given a nicely baked cake as the desired output, what our mathematical function does is this; it accepts the ingredients as input, plays with them following certain mathematical laws using a function, then returns the desired result to us, a cake as output.

There are numerous mathematical functions or algorithms/models applied in machine learning some of which include Random Forests, Linear Regression, Logistic Regression, Support Vector Machines, Neural Networks etc.

The use of large neural networks for machine learning purposes is referred to as deep learning. This means that deep learning is indeed a subset of the field of machine learning, contrary to the false belief that machine learning and deep learning are two separate subsets of artificial intelligence.

Why is there so much fuss around Deep Learning?

Deep learning as a subset of AI has become so popular over the years that there is a common reference to AI as consisting of deep learning and other machine learning algorithms.’

In order to understand the reason for deep learning’s popularity, let us compare a common machine learning algorithm like logistic regression with a deep learning approach.

The problem at hand is email spam classification. This means that we want our machine to be able to correctly classify relevant emails from spam ones.

Logistic Regression Approach

We have a collection of emails both relevant and spam, and that is our data. Our desired result is a label that says if a particular email is spam or not spam. 

For most machine learning models such as the logistic regression model, after gathering our data and their corresponding labels, another necessity is to have something called features, which must be selected carefully through a tedious process called Feature Selection.

Feature selection involves detailed analysis and manipulation of all available data attributes so as to emerge with the most useful features for an excellent model.

Features are attributes that can be extracted from data.  For example: If we are gathering data for house price prediction, possible data features include; size of the house, age, number of rooms, location etc. or if we have pictures of women from different nationalities, possible features could include; face shape, hair color, skin color etc.

It is important to note that the features chosen must be relevant to the problem to be solved. It is not wise to choose the color of paint for the house prediction problem or presence of eyes as a feature from the women’s pictures. If irrelevant choices of features are made, the accuracy of your model will be greatly compromised. Basically, an ideal feature is a characteristic of the data that a human expert will consider while trying to solve the problem at hand.

In our case, we have emails. Possible features that a human would look for when deciding if the email is spam or not would include things like; presence of certain words like ‘deal’, ‘free’, ‘offer’, ‘buy’, presence/absence of email subject, sender’s email address etc.

Note that some features can be numeric such as age, length and size, while others are referred to as categorical; like email addresses, words or categories such as female vs male etc. Numerical features can be passed directly into mathematical functions, while categorical data require a process called encoding in which specific values are assigned to represent a particular category. This means a feature like presence of email subject could have the number ‘1’represent email subject present, and ‘0’ represent email subject absent.

For logistic regression, we take in these features as our input to our function (usually a linear function such as mx + b = y or even a polynomial function, where x represents the input), after which the result from the function is compared against a threshold function (here, for values above a certain threshold say 0.5, predict that the email is spam, while for  values below the threshold predict that the email is not spam).

Deep Learning Approach

In deep learning we do not need features selection. We usually pass in the data in its raw form and allow the neural network to extract the important data attributes that it needs by itself. So, the first layer of neurons in our neural network will receive the emails as they are, or rather direct numerical representations of the contents of each email (as it is necessary for all non-numerical data to be converted to a numerical representation for easy interaction with the model). 

Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, and it was discovered that each layer in a deep convolutional neural network is usually dedicated to detecting the presence of a particular detail or feature in the input data.

This means there is no need for the expertise usually needed for good feature selection during a model like logistic regression. In a nutshell, we say that deep learning does not require structured data (data with appropriate features) unlike other machine learning models.

And that is awesome news, because I do not need to be a medical doctor to be able to train a deep learning model on how to detect cancer from images of patients; all I need is data! And more importantly we can save time and effort used up in performing feature selection, extraction and engineering. Please note that feature selection and feature engineering for most real-world problems can be very tasking and the success of your models greatly depends on the kind of features you choose.

The second reason is that deep learning has achieved unbeatable results in solutions for most of our very challenging real-world problems. Problems like image classification, object detection, image segmentation, visual relationship identification, natural language processing, speech to text processing, etc. have been solved to an astonishing degree using deep neural networks. We can also use deep learning for tabular data classification and regression problems like the famous Titanic- Predict Survival and Housing Prices problems.

Recommender systems from 10,000ft

  1. Intro to recommender systems.
  2. User-Movie Matrix
    • How the user-movie matrix is built
    • Matrix Factorization
  3. Embeddings
    • Initializing embeddings
  4. Making things better
  5. Updating the Embeddings
  6. Eating your Chicken Soup (Making Inference)
    • Recommending movies to users
  7. End Notes

# Intro to Recommender Systems(Recsys)

A recommender system is a system that predicts products to suggest to users that the user may not have seen otherwise by finding similarities between the users and products. Ever wondered how products like Youtube, Spotify, Amazon, Netflix etc keep finding the best products to show you everytime that usually always match your interest? The answer lies in Recsys! We’ll be looking at a high level explanation of how Netflix recommends movies to its users.

In 2007, Netflix organized a competition and put out a cash prize of $1 million dollars for whoever could build a recommender system which was better than what they had at that time for predicting movies to recommend to their thousands of users. The basic ideas behind the winning solution are what will be discussed in this post.

# User-Movie Matrix

A user-movie matrix in a Netflix movie recommender setting is a gigantic matrix that takes in all the ratings for all the movies by all the users in the database. A sample user-movie matrix for 5 users and 4 movies in a database with the movie rating for each user is shown below:

## How the user-movie matrix is built

The matrix above was built by querying the database and selecting the users that rated movies the most and the movies that were rated the most just to simplify things a bit for clarity. In reality, most users don’t rate the movies! One thing most of us don’t know is Netflix actually has a really really huge matrix of the ratings of every user in its system for every user. Just to put this in context, Netflix has over 1 billion users and a countless number of movies so imagine how big that matrix will be. One thing they do is to optimally store the matrix in such a way the size reduces. The idea behind this optimization is introduced in the next section.

## Matrix Factorization

Now that we have a matrix of ratings every user gives for each movie, what do we do with it? One thing we can do to make more sense of this matrix is to factorize it. If you can remember elementary maths, factorization just means reducing a big number like 64 into its factors: 2 and 32 or 8 and 8 etc.

For matrices, factorization involves reducing the matrix into n number of matrices such that when the n matrices are multiplied together by matrix dot-product, we will obtain the original matrix. For recsys, the n is equal to 2 so we have 2 matrix factors for the original user-movie matrix. One of the matrix factors is the user-factor matrix and the other is called the movie-factor matrix. The way these user and movie factors are gotten will be discussed in the next section. The general idea is that the 2 factors we have contain information about the ratings that are dependent on all the users and movies respectively such that when they are combined, the rating is found.

# Embeddings

Embeddings are the learnable parameters/numbers which make up the user-factor matrix and the movie-factor matrix. 

NB: The height of the user-factor matrix must correspond to the number of users being considered in the user-movie rating matrix ditto the width of the movie-factor matrix for the movies.

A sample of a factorized user-movie rating matrix with its embeddings is shown below. We are only considering two factors for each of the user and movie-factor matrices. The factors considered are the amount of comedy-ness or activeness of each of the movies for the movie-factor matrix and how receptive the user is to comedy and action in the user-factor matrix. All these can be seen in the linked image:

As we can see from the image, each user and movie has it’s own 2d embedding such that when a dot product is taken between them, we obtain the rating for that user-movie combination.

Embeddings are important because they enable us to represent each user’s features (ie reception to comedy and action) as numbers which the computer can easily make sense of likewise for movies. These embeddings are the things that enable the computer to learn the similarities and relationships between all the movies and all the users that we may not even know exist. The features extracted by these embeddings are called *latent factors/features*. The idea of embeddings is revolutionary and it is one of the greatest discoveries in training neural networks.

## Initializing Embeddings

Time for us to address the elephant in the room that has been ignored so far. How do we get numbers that properly represent the features of the users and movies such that if we take a dot-product of them, we will get the original user-movie rating matrix or something close to it. Believe it or not, the answer to this question is to initialize all those numbers in the 2 embedding matrices (factor matrices) with random values and just keep adjusting them by some means we will discuss later till we get values that give us the desired user-movie rating output. This simple idea here was one of the many key things that earned the winner of the Netflix prize $1M (now pause and ponder about your life and choices you’ve made).

NB: When working with embeddings, the computer does not know if the features it is trying to learn has to do with comedy or action. Its job is just to find the optimal values that will give back the rating matrix. However, upon a close examination of the embedding matrix that is learned/obtained for the user and movie-factor matrices, we will find out that users with similar taste in movies will be close to each other in the embedding space. the same also applies to the movies

# Making things better

In order to improve the embeddings which have been randomly initialized, we have to compare the ratings matrix gotten from the dot-product of the randomly initialized user embedding matrix and movie embedding matrix which is called the prediction to the actual ratings matrix which was shown earlier. After the comparison, we get a value which tells us how close or how far the predictions are from the actual value. The machine learning jargon for this for those who want to be cool is called *error/loss calculation*. The loss function that is usually used for this type of problem is the squared L2 loss. L1 loss can also be used but one thing to consider is that it penalizes higher losses a bit too much. The diagram below shows the predictions made(left) and the actual values(right) and how a comparison is made between them(blue arrow) to obtain the loss. We can see that for user 1 with user embedding [0.2, 0.5] and movie 1 with movie embedding [1.2, 2.4], when a dot product is taken, we obtain 1.44. This result is then compared to the actual value which is 3 and the error is calculated.

## Updating the Embeddings

The derivative of this loss with respect to each of the embeddings that produces it will be what tells us the direction to push each value in the embedding matrix so as to enable it to make better predictions. This cycle of making predictions, calculating loss, obtaining gradients of loss and then updating weights is an iterative process that is done a couple of times in order to get optimal values for the embedding matrix that lead to low loss. This iterative process is called *gradient descent*.

# Eating your Chicken Soup (Making Inference)

On a super high level, we’ve done a bit of cooking so it’s time to make predictions with the learnt embedding matrices we have for the user-factors and movie-factors. The goal is to use these embeddings learnt from a couple of users and movies and be able to generalize it to other users and movies…kinda. We’ve learnt some embeddings for a couple of users with the most number of ratings and the movies with the most number of ratings. The big question now is how do we recommend a movie to any one of these users using the embeddings we have learnt?

## Recommending Movies to Users

Assume we have the user-movie rating matrix below which shows that there are some movies the user hasn’t seen so they have no rating(white spaces in the image below). We can take a simple dot product of the learnt embeddings from gradient descent done in the previous section to predict the ratings for movies the user has not seen.

That is, we’re asking the computer to make predictions on the rating a user would give a movie based on the knowledge(latent features) it knows about the user and each movie without a rating. This can be visualized below. If the embedding of the user is more inclined to a user that likes action, the computer will make high predictions for action movies and lower predictions for movies that aren’t action.

After all these rating predictions are made for each of the movies the user has not seen, they will be sorted by rating value and the movie with the highest rating gets recommended for the user and there we have it, the chicken soup!

# End Notes

**Recap**: We have learnt that in the most basic form, a recommender system is just a matrix multiply of embeddings which were trained using gradient descent to predict ratings for a user on a movie he/she has not seen yet. Now it’s put this way, it sounds so easy. If only we knew this few years back, we’d have won the netflix prize (stops again to reflect on life)

There are a whole bunch of things that can go wrong. This is an incredibly naive system that does not account for things such as when a new user that we have no embedding/knowledge about registers on the platform. SInce we don’t have the previous taste of the user, we can’t know what to predict. This problem is infamously called the cold-start problem and finding a fix for it is beyond the scope of this article so…go do it yourself!

IMPORTANT SIDEBAR: Recently, deep learning has been a buzzword and because of it’s recent human level achievements, most people think what happens in a deep learning system is nothing but magical so they tend to think up fairly trivial problems and expect the computer to magically come up with answers/predictions. Imagine asking a model to predict if a child will become a criminal or to predict the stock market accurately without any mistakes…smh(insert meme of choice here).

In recommender systems and every fairly every other machine learning problem, the computer is only making predictions from the data it is given. No act of magic happens. What happens instead is just a huge matrix multiplication with certain sophisticated algorithms(like gradient descent) not some Hollywood Westworld-like simulation. This means that predictions may be wrong and biased more often than naught. In cases where the training data is incoherent or if your learning algorithm is flawed, expect the trained network to be useless (it can’t learn anything by itself without proper training from properly labeled or unlabelled data)**. A neural net is nothing similar to a human brain in terms of performance so it clearly cannot act like one**.

The role of technology in the fight against Corona Virus

Innovators and researchers around the world are putting technology to work to curb the effects of the global health crisis. A lot has been done so far on this, from the use of software that collects and analyzes data relating to the spread of the virus to hardware devices like smart cameras, embedded with artificial intelligence and ventilators for hospitals.

The emergence of COVID-19, has sparked a health crisis that in turn, has affected the global economy drastically. The community of scientists and researchers have put their together, full weight behind finding solutions that help reduce the impact of the pandemic as it spreads globally.

Below, are some innovative global contributions from the technological sector:

1: Understanding the Virus’ Genetic Tree Using Big Data:

Big data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions.

With the use of big data, scientists and researchers have been able to study the genetics of the virus and its development overtime.

An open source project called Nextrain provides data, sequencing, and visualizations showing the evolution of pathogens like corona virus, together with adequate information that can help epidemiologists understand how the virus evolves in different countries as well as possible mutations that can change its nature.

2: Finding therapy through the use of Machine Learning

With the use of adequate data, artificial intelligence could prove to be a very powerful tool used for predicting future trends of diseases as well as finding possible treatments.

A biotechnology company, AbCellera makes use of machine learning to develop therapies from antibodies on patients who have recovered from the disease. They have used Artificial Intelligence technology to actually analyze over five million immune cells as they search for those capable of producing antibodies to help patients recover.

3: The use of Telemedicine to control hospital population

Telemedicine can be seen as a form of technology that enables remote health care (telehealth). It Basically makes it possible for physicians to treat patients remotely, by making use of smartphones and computers.

Telemedicine is one of the alternatives that communities are turning to in order to control the population of patients in hospitals, thus preventing overpopulation and unmanageable influx of patients.

Telemedicine is making it faster and easier for patients to receive diagnosis: with this patients only need to open applications, describe their symptoms and wait for a reply from a doctor, through virtual consultation.

For example, in Shanghai, China, the Xuhui hospital has consulted with patients as far away as Tibet and France. In Spain, a Seville-based company, Open Salud ( Open Health ), has launched a telecommunication platform that allows doctors or clinics to determine the best mechanism for tending to their patients

4: Application to Free up Hot Lines

Emergency hot lines in some regions are not reachable due to traffic in communication. In a bid to ease off the load on the these lines, health care applications have been developed aid in remote diagnosis.

In Madrid, the regional government has launched an initiative called “Corona Madrid”, which is available both by application and on a web page. Individuals who suspect they might have the virus and conduct a physical self-assessment based on their symptoms and pending on the results will receive instructions and advice about steps to take for treatment. This initiative, jointly developed by several Spanish and in record time, aims to reduce call congestion for the regional Corona virus hotline while providing health authorities a more concise local snapshot of the pandemic.

5: Providing Relevant Information Relating To the Virus through Chat Bots

A Chat Bot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.(Wikipedia)

The World Health Organization (WHO) launched the WHO chat bot with the intention of providing information about the novel Corona virus and to provide answers to questions frequently asked about the sickness, such as current infection rates and what can be done to protect oneself. The WHO’s bot takes a simple approach: it does not use natural language, rather users have to send numbers or emojis to get information on different topics. For example, if they want the latest figures about the virus, they should send ‘1’ or if they want information about travel, submit ‘5’. The WHO chat bot operates on the WhatsApp platform, which belongs to Facebook. The tech giant has also created a social media Corona Virus Information Center that appears at the top of its users’ feeds providing an official source of information, while removing pages that spread disinformation and fake news.

6: Keeping People Together With Despite Distancing

With the aid of video calls, there has been massive progress in keeping in touch with contacts beyond one’s four walls, especially for those who live alone. Some of the most popular applications are Skype, WhatsApp, Google Hangouts, Duo, Webex, and Zoom, which according to the New York Times, received approximately 600,000 downloads in one day at the beginning of the pandemic. According to company Sources, Facebook Messenger’s video call functionality has seen a 70 percent increase in activity since the beginning of the pandemic.

These applications are being used both to organize remote meetings for teams and remote workers and to help loved ones stay in touch with one another. The last few weeks have borne witness to an unleashing of creativity as users have organized concerts, workshops, virtual get-togethers, birthday parties, and even weddings for which guests have received invitations with a link to a site where they can see the ceremony streamed.

In conclusion, the applications of technology to combat COVID-19 are increasing day by day as more individuals and scholars are gaining interest in finding solutions to the numerous problems that have resulted from the pandemic.

Cryptobarons to speak at the Crypto Games Conference; Minsk 2018.

With Crypto Games becoming a huge force and crypto-bound games springing forth in a sneeze, the need for a developer-investor gathering was absolutely necessary hence the Crypto Games Conference. The Conference is a unique and exclusive gaming event where developers, entrepreneurs, and investors meet and discuss the world of cryptocurrencies as well as the blockchain technology development.

The Gaming Conference which boasts of well over 600 professionals in attendance with up to 50 speakers from around the world is scheduled to have it’s next stop at Minsk, Belarus from 17th-18th October, 2018. The Crypto Barons team are not only going to be present, but also will be one of the speakers at the event.

The conference is poised to unite the worlds of games, blockchain, cryptocurrencies and other cutting edge technologies together, while discussing the profound implications of their convergence to discover the new opportunities that result from their synergies.

To read more


CB: Progress Updates and Barons Awakening Game Release.

Hello everyone. We know you’ve waited a while for some updates and we have good news. We’ve really been working hard to put things in place and get everything ready for a smooth release and we can tell you that you’ll love what we’ve cooked.

CryptoBarons was selected as one of the finalists for Cryptogame challenge and the demo version of the game has already been sent for review. Let me tell you a little secret about it… sshhh!! Don’t tell anyone yet. Its awesome; you can purchase ERC 721 cryptoassets and … Yes!!! you can fight battles   Since our last update, we have made a number of modifications in our game design, logo, gameplay etc. to bridge the gap between blockchain games and everyday games that can be downloaded on Google Playstore and Apple App Store.The game is quite interactive and soon enough, you would be able to compete with your friends when playing the game.


Over 3,000 years ago, there existed the era of Barons who were an unstoppable force in war and had an unbreakable bond between them.
While the Barons enjoyed their victory over the Banditians (Kingdom of thieves) in the cool of the night, Sparda poisoned their drinks and sent them into a deep sleep. While the Barons slept, Sparda harvested their souls as sacrifice to the dark lord Amheh.

This is the intro story for the mini-game which we have released as a preamble to the Main version of the game which will be released much later in October this year. In this mini-game you can buy the game assets such as Soldiers, Workers, Farm, Gold mine, Defence mechanisms, Barracks etc. You can gather food, wood and even upgrade your assets too. All these game assets you have purchased in mini-game will also be available when the Main version of the game is launched, by the power of the blockchain.


Getting Started
To set up an account, click on the Register link provided and sign up with a valid Email Address.
An Ethereum address will be generated for you and mapped to the Email provided. If you already have an account, you can login with your existing Email and Password.

Viewing Your Profile
To view your profile, chest items or transaction history, click the Profile link on the top header.

Purchasing Baron Tokens
Purchase Baron Tokens from your profile page by clicking on the “Purchase Tokens” tab. You have an option of paying with your credit/debit card (Mastercard or Visa) or with Ethers.

Acquiring Game Assets
Purchase Game Assets from the market place with your Baron Tokens, Ethers or Smart Card.
The purchased assets will be mapped to your Ether address.

Barons Awakening: Game Intro
Navigate to Barons Awakening and input your password to continue.
Every Baron is expected to gather resources from their production buildings, and upgrade available buildings in preparation of the War of Barons.

                                                           Screenshot of gameplay

Remember, we’re currently selling Baron tokens at a discounted rate. Buy tokens, buy assets, play the mini-game, upgrade your assets and have fun. The marketplace to resell assets will be released soon and players will trade cryptos for money.

Join the discussion in our community:

KIN Developer Program; WiCrypt, the next big thing?

Cryptocurrencies or magic internet money, anything you wish to call it, is fast becoming the future of online trading. How long will it be in play??? That’s still a huge debate amongst developers but one thing is certain and that’s the need for more crypto-bound apps!

With increasing online transactions, there is a massive need for platforms enabling easy payment and receipt for services provided and also for swift transferability… Still wondering where WiCrypt comes in?

The Kin Developer Program, is a modelled initiative that will empower developers to successfully create KIN-powered apps, or build KIN-powered experiences into their existing apps with more than 200 developers from over 37 countries around the world proposing new and innovative use cases for KIN over the past few months, with WiCrypt, a product of Ugarsoft Limited being 1 out of the 40 selected finalists in the KIN Developer Program.

WiCrypt enables its users to get paid while sharing their WiFi (Now tell me what beats that, really!). The app is so unique, it allows peer-to-peer internet sharing, giving you your deserved reward for sharing your mobile data. Users can easily purchase a connection to a WiCrypt hotspot using KIN while the hosts will earn KIN in return for providing internet connection. And when you think it’s all over…BOOM! Users get to receive bonuses for high ratings from those who have used their internet connection. (Now I’m certain nothing beats that!).

WiCrypt (which by now I guess you already know is the first of its kind), leverages the Blockchain and allows users to receive payments per kilobyte of their WiFi data consumed by a beneficiary, allowing the user having the freedom to set desired price per kilobyte of data consumed.  The app which is an inbuilt wallet will verify the beneficiary has enough cryptocurrency to start using the service and automatically disconnects the beneficiary when the money is exhausted.

With WiCrypt, the problems of expensive internet services, unavailability of service from service providers at specific locations, unavailability of internet services for tourists and underutilization of abundant internet connection by Individuals/Companies that do not exhaust their excess data or have unlimited internet connection have all been solved.




CB: Fan Art and Animation Contest.

CryptoBarons game is launching very soon, and we cannot but get excited about the first 3D Blockchain Game that will be released on multiple platforms.

The Concept characters for the game have been created, modeled and animated in 3D and we’d love for our fans to start creating beautiful arts and animations for HD wallpapers, memes, video sliders, short videos etc.

The aim of the contest is to select the Best 10 fan arts / animations which would also be used in the game promotion.

The 10 best arts/animations will be given 0.1ETH each. So you are encouraged to have as many entries as possible.

The winners will also be among the first people to beta test the CryptoBarons game play on android and iOS two (2) weeks after the competition winners are announced.

Please note that for every entry submitted, we reserve the right to use images, text, video and other materials submitted for the purposes of promoting the Game.

The Fan Art kit will be made public in a shared file for fans to download and start creating beautiful images.


Please follow these steps to submit your works to increase your chances of winning.

1. Register on
2. Use the email you used to register in Step 1 above to submit your work.
3. Choose the Category for the Art Submission. (Use one Category for each submission)
4. Submit the Art Work here:
5. Post your Art work on our twitter thread here and retweet for likes

The contest starts on Friday 13th of July and ends on Friday 27th of July.

The winning arts will be based on votes and will be announced on 3rd of August.

So start preparing your templates and designs. Something big is coming to blockchain games.

Art works can be posted on our social networks to increase your chances of winning:

  1. TwitterTweet @crypto_barons
  2. Facebook Post artworks on our facebook page
  3. Discord — Post artworks on the #art-and-animation channel
  4. Telegram — Post in our telegram group.

Works should be submitted using this link:

Terms and Conditions:

You agree to submit contributions to the challenge (“contributions”) under the following conditions:

  1. CryptoBarons does not claim ownership of your contributions to the challenge in form of images, text, videos, articles, or any other contributions you may submit.
  2. By submitting your contributions, you are granting CryptoBarons and its affiliated companies the following worldwide, non-exclusive, perpetual, irrevocable, royalty-free, unconditional, fully paid-up rights — to publicly perform or display, broadcast, transmit the contributions in whole or in part, for promotional and marketing purposes worldwide in any and all forms of media, whether now known or hereafter devised, and to distribute (directly or indirectly through multiple tiers) the contributions to CryptoBarons affiliates, its marketing agencies and to third parties for these purposes.

Your contributions to the challenge will be available to other participants and to ensure they are safe and freely usable by other participants, you warrant that:

  1. you own or otherwise have all rights necessary for you to provide the contributions and grant the rights described above and you do not disclose any information which would constitute a violation of a confidentiality obligation; students are responsible for verifying that they own the intellectual property (IP) for their game demo and not their university or other institution of higher education.
  2. your contributions do not contain any viruses, worms, spyware, or other components or instructions that are malicious, deceptive, or designed to limit or harm the functionality of a computer; and
  3. your contributions are not subject to license terms that require any software or documentation incorporating or being derived from your contributions to be licensed to others.

Apart from prizes offered as part of the challenge, no monetary compensation will be paid for any of your challenge contributions.