This is part one in a series of two parts. In part 1, I will explain how to control any device using a smartphone or voice commands from Google Assistant or Alexa. Part 2 will explain how to use gestures to control any home appliances using Computer Vision and Raspberry Pi.
Why would you want to automate?
- Saves time
- Much more efficient
- Ability to control any device from anywhere in the world
- Insanely useful for lazy chaps like me
- ESP8266-Node MCU (Microcontroller) ($2 in aliexpress)
- Breadboard power supply or any other 5V source ($0.71 in aliexpress)
- Relay board — (2 channel, 4 channel, 8 channel according to your needs) ($1 in aliexpress)
- Jumper Wires (Female to Female) (under $1 in aliexpress)
Taking a Look at the Required Components
The Relay Board uses electromagnets to make and break the circuits. This one here four 4 channels, but you can easily buy up to 16 channels. The number of channels means the number of devices you can connect at once. This relay can handle up to 10A 250V AC. There are four input pins (IN 1–4) to control four relays. You can use Node-MCU to power the board, but it is a good idea to isolate the board from the micro-controller, to do that connect voltage pin from Node-MCU to VCC and separate 5V power supply to JD-VCC and GND.
This is where the breadboard power supply comes in.
Breadboard Power Supply
The breadboard power supply can output 5V, 3.3V, or both. There are two output pins and two jumper pins to set the configuration. It requires 6.5V–12V to function. If you are using both the pins, use the 12V input to deal with voltage drops. You can also power it using USB. There is a switch to turn on and off the device.
ESP8266 Node MCU (Microcontroller)
There is a lot to say about Node MCU, but I will stick to the details related to this project.
There are 16 GPIO pins in Node MCU, which can receive and send signals. Node MCU can communicate with other APIs and devices using WiFi. Node MCU required 3.3 V to power up and it can be done so using Micro-USB or Vin and GND pins. You can use pins from D1 to D8 to send signals to the relay board. We can use Arduino IDE to program Node MCU and use Blynk to send commands using our phone. We can then connect Blynk to IFTTT to connect it to Google Assistant.
Now, let’s connect!
Connecting the Pieces
Step 1 — Programming Node MCU
First, we need to program the Node MCU. The Node MCU connects to Blynk, an IoT platform, which can send commands from smartphone to Node MCU.
Make sure you have Arduino IDE installed. Now we need to add our Node MCU board to the Arduino board manager. To do this, go to Arduino > Preferences and paste the following:
in “Additional Boards Manager URL’s”, and press ok. Now go to boards manager, search “esp8266”, and install.
Next, click on Tools > boards > ESP8266 boards > Node MCU 1.0 (ESP-12E Module). Paste the above code in Arduino IDE, put your authentication code here.
char auth = “PUT YOUR AUTHENTICATION CODE HERE”;
To find the authentication code, use the following instructions:
Open Blynk app and create your account. Create a new project and select Node MCU as the device, choose the connection type as WiFi then click on create. This will send the auth token to your mail. For more details follow this-Getting Started.
Finally, put your WiFi SSID here:
char ssid = “YOUR WIFI”;
and your WiFi password here:
char pass = “WIFI PASSWORD”;
That’s all you need to do to control Node MCU.
Step 2 — Wiring Everything
The above schematic shows the wiring of each component. D1-D4 pins in Node MCU are plugged in the relay board from In1to In4. 3.3V pin of Node MCU goes to VCC in the relay board. Take two wires and put both in 5V configuration of breadboard power supply, connect negative to GND, and positive to JD-VCC of the relay board, respectively. To power Node MCU you can either use micro-USB or use the second output of breadboard in 3.3 V configuration. Connect the positive wire to Vin and negative to GND in NodeMCU, respectively. To power breadboard power supply use a 12V DC adapter. Connect the wires of the appliance to the relay as shown above.
Step 3 — Connecting to Blynk
This will be the last step if you just want to control your devices using your phone and don’t care about voice recognition.
In the Blynk app drag 4 buttons on the main window and change the pin mode in each of the buttons to a digital pin from one to four, For D1 to D4 on Node MCU. If you are using other GPIO pins then choose those pins.
That’s it! Now you can control devices using your phone.
Step 4 — Voice Activation
To use voice recognition, we need to use IFTTT. IFTTT bridges the gap between Blynk and Google Assistant. You can even use Alexa.
Create your IFTTT account and click on My Applets > create applets. There will be a big button saying “If This Then That”
Select the “This” button and then select Google assistant and click on connect. Now to create a voice command, click on “say a simple phrase”, fill in the text box according to your needs.
Now click on “That” and choose webhooks and click on connect. This will allow us to communicate with Blynk. On the URL option type the below URL, keep in mind to put your Blynk auth key there:
Change the D1 to your pin of Node MCU to the corresponding pin above. Ex if you are using D1 of Node MCU, type D5 in the URL, if you are using D2 of Node MCU type D4. Use the image above to find the correct value. This is because Blynk thinks that it is connecting to the Arduino UNO board and the above image is the conversion of Node MCU to Arduino UNO pins.
Change the ‘Method’ option to PUT, ‘Content Type’ as application/json, and in the ‘Body’ type
[“0”] //This means turn on
Similarly, create another applet to turn off the device and instead of putting
[“0”] type [“1”]
Now you can do the same for the other 3 appliances.
That’s it! Now you have more control over your house, you can control anything you want from anywhere in the world for less than $10.
Panel: Smart Manufacturing as a Driver for Business Outcomes – Investing in Industry 4.0
Digital transformation offers promise to industrial organizations to weather the uncertain economy, but deploying digital technologies is frequently challenging.
The manufacturing sector is facing a unique set of headwinds and tailwinds. According to Omdia research, only half of industrial companies have begun a digital initiative. Among those that have, roughly 40% of organizations fail to achieve an expected payback for their digital investments.
In this video, Omdia principal analyst Alex West discusses this situation with Farid Bichareh from the Industrial Internet Consortium, Marylin Glass-Hedges from Daimler Trucks North America and Steve Holdsworth from Crescent Electric Supply.
Strengths of Employing Data Science in Healthcare
Data science employing big data for healthcare needs and the extraction of valuable business insights greatly transformed the medical industry and brought revolutionizing results in care efficiency and personalization.
According to Global Market Insights, the healthcare analytics market size is expected to grow by 12.6% by 2025, and the prescriptive analysis sector is the one that will witness the highest level of expansion with 15.8% against 13.2% in the clinic end-use segment.
Access to medical databases leading to the deployment of data makes it possible to shift from medical treatment that takes up a lion’s share of healthcare budgets, and rather focus on identifying the preventable illnesses (for instance, two leading avoidable deaths conditions are ischaemic heart diseases and lung cancer) and primary and secondary prevention.
Big Data Benefits
Medical data is a powerful resource for deriving valuable insights and reducing data waste. In the context of new reality associated with an overload of healthcare and pandemic challenges, big data can assist healthcare providers in detecting health-related patterns turning vast data into actionable information vital in medicine and medical industries.
Aside from patients getting whose experience of healthcare service can be enhanced as a result of applying data science, the stakeholders interested in the implementation of big data in the healthcare sector include healthcare providers, the health tech industry, pharmaceuticals, and health insurance agencies.
Among multiple benefits of employing big data in healthcare, the following ones come on top:
- Implementation of data science in healthcare allows to create comprehensive patient profiles.
- It provides instant identification of patterns in treatment outcomes
- It enhances patient satisfaction
- It facilitates hospital administrative workflows
- It optimizes medical procedures by increasing care efficiency
- It enables the medical industry to be more cost-effective.
Overall, data analysis in healthcare ensures a highly personalized approach to customers and processing of an individual patient model that can map out their health history and health course trajectory digitally, which implies multiple sharing options, wide diagnosis capabilities and deeper engaging patients in medical decision making.
Furthermore, the data analysis helps to improve the productivity of the healthcare sector as it enables the medical industry to maintain the high quality of the service with fast processing of a large amount of existing (and prospective) medical data at a reduced cost.
Although the application of healthcare analytics is somewhat limited in Europe, a pandemic caused by COVID-19 forced authorities to reconsider the previously imposed restrictions and give the green light to healthcare( in particular, predictive and prescriptive) analytics initiatives.
Big Data Challenges
Due to the sensitivity of health data, its fragmented nature, the enormity and complexity of databases, and the special importance of privacy-preserving technologies, data science in healthcare can face certain challenges.
In particular, challenges of processing and analyzing big data in healthcare that might restrain the market growth mostly pertain to:
- the shortage of IT professionals with relevant expertise
- data integrity issues
- ensuring data safety.
Besides, complexities of regulations and lack of unified procedures in the healthcare industry can create barriers to wider application of data analytics by medical providers and hinder the growth of the health data analytics market.
Data Science Applications
Data science in healthcare ensures a full overview of the patient’s profile in real-time as it lets process clinical information including patient demographics, diagnosis, medication, procedure, lab results, and additional clinical notes.
The large amounts of medical data that became available in healthcare organizations resulted in opening opportunities for successful completion of multiple data science projects: among illustrative applications, the most outstanding belong to practical clinical environments.
A number of pioneering organizations (Cerner Corporation, International Business Machines Corporation, MedeAnalytics, Oracle Corporation, etc.) generate use cases in and outside the clinical environment to show the potential of further exploration of data science in healthcare and its positive transformation.
They made a breakthrough in the market of wearables (they covered the various domain areas including fitness, exercise, movement, physical activity, step count, walking, running, swimming, energy expenditure, etc.), and diagnostic tools demanding implementation of advanced analytical models.
In general, the incomplete list of data science applications includes the following areas:
In this particular scenario, computers demonstrate self-learning abilities to interpret MRIs, X-rays, mammographies to recognize patterns in the data and find tumors, or any organ anomalies.
In this case, data-processing tools through analysis and interpretation help to come to an understanding of data from next-generation sequencing experiments.
New Drug Launch
Pharmaceutical companies use data science to make financial predictions and the potential market impact of a new drug by analyzing the operational pipelines from manufacturing agents to end-use consumers.
Predictive Analytics Purpose
By extracting deliverables from data, medical industries use it to predict trends and behavior patterns to enhance healthcare customer experience and calculate probabilities of medical outcomes based on the statistical approach.
Monitoring Patient Health
By storing digital health-related information of the patients, healthcare providers can improve the productivity of healthcare delivery systems. Besides, data analysis is used to monitor health parameters including blood pressure, body temperature, and heart rate in real-time.
Tracking Health Conditions
Data science can provide ongoing accurate tracking of health conditions and mark potential cases that a patient is prone to. For instance, data science proved to be an invaluable asset when it comes to assisting individuals with diabetes in keeping track of the meals, physical activity zones, and blood glucose levels.
Providing Virtual Assistance
With the comprehensive platforms available due to data science, patients are provided with the means of identifying the disease by entering the respective symptoms in the application search bar. The virtual assistant will immediately identify the condition and offer to choose the possible health solutions.
Data Science Access
Access to big data and data science in healthcare made a positive impact on the practice of medicine with widening capability of medical professionals to apply data-driven decision making, take a personalized approach while treating patients and instantly checking real-time data against patients’ profiles for delivering high-quality healthcare.
It allows us to be confident in forecasting the bright future of data science and further development of tools for comprehensive analysis in healthcare linked in the expansion of the market of data science applications.
In addition to providing new levels of data completeness and interoperability, they can successfully address, among various issues, the problems with disease prevention, symptoms, monitoring health conditions, dosage calculations, and pharmaceuticals.
IoT Security & Education: Toward a Secure Connected Campus?
IoT devices are everywhere and starting to be used in many industries, as well as in public places. Technological innovations and advancements make it possible for our devices to become smarter, but in some sectors, the adoption rate has been quicker than others.
Education is one sector where adopting new technologies takes longer than many other industries. Smarter devices could improve the interaction between students and teachers as well as provide more efficient education and learning. However, there are specific security concerns involved that have to be taken care of first for schools to adopt devices that would replace traditional books and notebooks. This article takes a look at some of the challenges faced by the education sector when it comes to the use of IoT.
State of the Education Sector
When it comes to the education sector and IoT, there are many changes possible that the entire industry could utilize making it look completely different in the timespan of a year or two. IoT provides the kind of value that other technologies don’t by advancing education so much so that its structures and environment could change completely.
Today we have schools and educational institutions sticking to the traditional ways of operation. However, there are also schools that use IoT which allows them to offer more personalized learning at a higher level of efficiency. The use of smart devices on campuses and in schools can improve the students’ access to relevant information, as well as help manage the entire classroom with more transparency and efficacy.
Education Use Cases
Below are a few interesting use cases reflecting the benefits of IoT in the Education field:
Enhanced Student Acquisition
- Improved understanding of prospective students and their educational needs.
- Improved forecasting and acquisition of students and faculty through integration of mobile apps to website navigation.
Improved Student Experience
- Distance learning integration.
- Student life analysis through device integration for any early detection of patterns that require course corrections for improved academic outcomes.
- Develop courses and curricula that meet student needs effectively based on student sentiments and their interests.
- Differentiated services and cost reductions for improved operations.
- Accelerated research through device integration for faster experimental data collection, and integrated analytics with predictive capabilities
In open environments such as the ones nurtured by higher education institutions, cybersecurity can be a massive problem. It’s quite difficult for many institutions to implement proper cybersecurity practices while striving to teach and share information with anyone who may need it. The enormous number of students passing through an institution’s system each year certainly does not help in that mission, as they all use their personal devices.
The threats could be more severe than you might think, not only for the devices but also the data that is managed by educational institutions. In Florida, there was a cybersecurity data breach through the security system of a virtual K-12 school that jeopardized the safety of the sensitive student and parent personal data. It included the names and birth dates of students, email addresses of the parents, as well as Social Security numbers of the teachers.
Cases like this, clearly show that the level of cybersecurity in the education sector isn’t on a high enough level to deter cyber criminals.
Solving Cybersecurity Concerns
The problem of IoT-related security concerns isn’t exclusive to the education sector but the sensitivity of the assets we are expected to protect in this field is particular. Therefore, Educational Institutions must start teaching cybersecurity not as “a best practice” but rather “by practice”. One way is to start teaching the young generation about cybersecurity in a fun and practical way. As a great example, ISSA France – the 1st French-speaking European chapter of the Information Systems Security Association (ISSA) has just launched a Holiday Workbook presenting cyber risks to children and their parents.
Besides, to be able to trust IoT devices, connected education campuses must drive a dedicated IoT risk analysis and adopt security assurance by design, rigorous testing, and security standards for the devices and systems in use.
Only by knowing where the weaknesses are and how they can be exploited can we deter cybercriminals from breaking into internet-connected systems to steal sensitive data and cause a massive amount of damage.
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