Factors to Consider in IoT Implementation

Have you resolved to use IoT to power your organization?

The entire process to develop an IoT ecosystem is quite a big challenge. IoT implementation is not comparable to other IT deployments that are largely software-based because it includes multiple components like devices, gateways, and platforms. If you plan to adopt IoT, then you have the following factors to consider in IoT implementation.


The year 2016 turned out to be an unforgettable year for the telecommunication industry. At that time, the telecom infrastructure was badly hit by a DDoS attack. As a consequence, many users faced difficulties while establishing a connection with the internet. Earlier, it was speculated that there was a cyber warfare element in the attack. Nation-backed attacks are nothing new. Over the past few years, the battleground has changed from the land to the digital realm as cybercriminal groups find support from different countries.

However, in this case, the culprit was someone else. It was known as Mirai, a malware. Soon, it was revealed that the malware belonged to the “botnet” category. A botnet is an attack which compromises multiple systems at once and uses them as digital zombies to carry out malicious actions. Mirai was able to bypass several IoT devices. These devices included residential gateways, digital cameras, and even baby monitors! All of these devices were invaded through a brute-force strategy.

Unfortunately, this is just the tip of the iceberg. The botnet is not the only threat looming over IoT. There has been other malware like ransomware which makes matters worse in the Medical IoT. Similarly, last year’s cyber attacks in Bristol Airport and Atlanta Police paint a worrisome picture which illustrates IoT devices as highly insecure against modern cyber attacks.

If any part of the IoT ecosystem of business is hacked, then it offers the perpetrators remote access to trigger actions. Therefore, it is necessary that cybersecurity strategy of any organization dealing with IoT focuses on the complete system security from sensors and actuators to the IoT platforms for minimizing loopholes.


Authentication is one of the integral key points for IoT implementation. It is important to ensure that a system’s security is not completely reliant on authentication mechanisms which fall into the category of one-time authentication. An enterprise IoT infrastructure demands that connectivity for devices and endpoints is carefully assessed.

There should be an environment which can be “trusted.” Such an environment entails proper identification of all users, applications, and devices so they can be authenticated easily while eliminating unknown devices from the network. There have to be appropriate roles and access defined for all the linked devices. This ensures that the network can only permit authorized activities. Similarly, the incoming and outgoing data in the ecosystem can only be accessed by the user having the required clearance and authority.

Reference Datasets

The data production from IoT devices can only be useful if it is used in the proper context. This context can be utilized from third-party data which stores information of aggregated values, look-up tables, and historical trends. For instance, if IoT is used in home automation, then it can adjust the temperature of the home. The decision for using an air conditioner to increase cooling in the room or for using a heater to increase the inflow of warm air in the room depends upon the real-time data extraction from the weather data sources. Likewise, in the case of a connected car, the car has to send its location coordinateness to the closest service center. Therefore, it is necessary to ensure that adequate reference points are available for IoT devices.


During IoT implementation, one has to factor in all the activities related to managing, processing, and saving data in the sensors. This aggregation enriches the value of data by enhancing the frequency, scope, and scale. However, aggregation requires the correct use of different standards.

There are two standards which are associated with aggregation.

  • Technology Standards – They include data aggregation standards (Loading (ETL), Transformation), communication protocols (HTTP), and network protocols (Wi-Fi).
  • Regulatory Standards – They are specified and overseen by federal authorities like HIPAA and FIPP.

The use of standards springs several questions, for example, which standard will be used to manage unstructured data? The traditional relational database store structured data and are queried with SQL. On the other hand, modern databases like MongoDB use NoSQL (Not SQL) to store unstructured data.

Data Sensitivity

When organizations began providing services and products on the digital realm, they collected user information for processing. However, no one exactly knew what happened behind the scene. Was the information only being used to provide better user experience or was it exploited for hidden purposes? Studies revealed that a large chunk of organizations sold their private customer data to third parties.

In the past few years, a strong wave has emerged to improve data transparency for clients. For instance, in May 2018, the European Union implemented GDPR (General Data Protection Regulation). GDPR is a comprehensive list of data privacy regulations, which is created to make organizations transparent about their data processing. The objective behind GDPR is to protect the privacy of EU residents. This law is applied on any business which engages with EU residents, irrespective of the business’ geographical location.

These emerging trends are important for IoT since the sensors and devices are responsible to store and process large datasets. Hence, it is vital that any of this data does not breach privacy laws. There are four main tips for data sensitivity.

  • Understand the exact nature of data which is going to be stored by the IoT equipment.
  • Know the security measures used to encrypt or secure data.
  • Identify roles in the businesses which access the data.
  • Learn data processing of each component of the IoT ecosystem.



What Is the Internet of Things Ecosystem Composed of?


The internet of things is quite popular nowadays. Many people are familiar with technology and its role in improving the lives of human beings. However, people still do not know much about what makes up the internet of things. Read this post to get the complete picture of the internet of things ecosystem.

Hardware in Device

The “things” in IoT refer to devices. They act as the intermediary between the digital and real world. The chief principle of an IoT device is to gather data. This data is collected with the help of a sensor. If you don’t have enough data on your device, you can utilize a basic sensor. However, industrial applications require an extensive list of sensors. Similarly, there is an actuator which is used to physically trigger an action.

To understand an IoT device, you have to go through multiple factors like size, reliability, lifespan, and most importantly, cost. For instance, a small device like a smartwatch is good to go with a System on a Chip (SoC). Similarly, more complex and bigger solutions require the use of programmable circuits like Arduino or Raspberry Pi. However, if you aim is to install IoT at a manufacturing plant, then you are looking at gigantic solutions like PXI.

Software in Device

While a smart device uses sensors to interact with the real world, it requires an OS that is similar to a robot. The addition of software and hardware elevates a device to a “smart device.” The software establishes communication with other devices (it is called the internet of things for a reason) and other components of the ecosystem like the Cloud. The software enables you to perform real-time business intelligence on the data collected by your hardware (sensors).

The right development of the software in your device is extremely critical. The better the code, the more features you can create from your IoT ecosystem. Bear in mind that adding the hardware part is tricky and costly in the IoT. Therefore, instead of focusing on your hardware, you should work hard on the software of your devices. Afterward, you can fit it into any piece of hardware. The software is classified into two sections.

  • Edge OS

It deals with your operating system. For instance, the type of I/O functionality you will require in the future. Similarly, you have to create a number of OS-level settings so your application layer runs easily.

  • Edge Applications

It is the application layer of the software. The application layer is the real deal to customize the processing. For instance, if you have installed an IoT device in a manufacturing plant, then you can use the software to look for a rapid increase or decrease in temperature. When the device senses a huge difference, it can instantly notify the authorities and enable the plant’s system to react in time.


In the internet of things ecosystem, communication refers to the networking of your device. How is your device going to establish a connection with the outer digital world? Likewise, it also includes the protocols which have to be used. For example, your business may operate on LAN. To ensure that your devices only “talk” with other connected devices, you have to create a tailored network design for your sensors.

Today, smart buildings use BACnet protocol with their systems. If you plan to use your device for home automation purposes, then you can make sure that it runs on the BACnet protocol. Even if your objective is to use it for a different purpose, it may prove helpful in the future to establish a connection between your device and others.

Likewise, you have to plan the connection of your sensors with the Cloud. In some cases, you may want to keep the data private in certain sensors.


The bidirectional exchange of data between an IoT network and protocol is carried out by a gateway. The job of a gateway is similar to a translator; it glues the entire ecosystem as it can take data from a sensor and forward it to any other component of the ecosystem.

Gateways can also be used to perform specific operations. For instance, an IoT provider can use a gateway to trigger an action on the sensor’s accumulated information. When gateways complete their set of routines, they transfer information to other parts of the ecosystem.

Gateways are especially useful to add security in a solution. Encryption techniques can be used with gateways to hide data. Therefore, it can serve as a vital shield to stop a cyber attack, especially the ones targeted at IoT like botnets.

Cloud Platform

The Cloud Platform is perhaps the most important of all the components. Cloud enthusiasts will particularly find it familiar to their “software-as-a-service” model. Your platform is linked to the following segments.

Amassing Data

Remember how we talked earlier how sensors collect data? Sensors stream that data to the Cloud. While creating your IoT solution, you will be well aware of your data requirements like the total amount of data you will process in a day, week, month, or year! Here, data management is necessary to address scalability concerns.


Analytics include processing the data, identify patterns, forecasting, and using ML algorithms. The use of analytics helps smart devices in creating meaningful information out of a cluster of disorganized data.


APIs can be introduced on the device or at the Cloud level. With APIs, you can link different stakeholders in your IoT ecosystems, such as your clients and partners, to allow for seamless communication.

Cloud Applications

This is the easiest path for non-IT folks. It is the end-user of the ecosystem. This is where the client engages in an interaction with the IoT ecosystem. The application in your wearable smartwatch is an example.

As long as your smart device consists of a display, your client is bound to have an application to interact. This assists in getting access to smart devices from any place and at any time.

How Does Random Forest Algorithm Work?

One of the most popular machine learning algorithms is the random forest algorithm. In real life nature, a forest is measured according to the number of its trees. A bigger number points towards a healthy forest. The random forest algorithm functions on the same principle of nature. As its name suggests, it creates a forest with trees. However, these trees we are talking about are the decision trees which we have covered in one of our earlier posts. These decision trees allow a random forest to make accurate forecasting decisions.

The forest is referred to as random because of the randomness of its components. Each of the trees in this forest receives training through a procedure known as the bagging method. This method enhances the final result of the algorithm.

The classifier class in a random forest is convenient. While the trees in the forest grow, the algorithm applies to achieve a greater degree of randomness.

In other algorithms, the best feature is determined during the split of a node. In this one, when the algorithm is in the working stage, the best random feature out of a collection is searched. This is done in order to enhance the model’s diversity.

Bear in mind that out of all the features, only a select few are assessed by the random forest during a node’s split. For further randomness in its trees, another technique known as the random threshold is used where they are equipped with all the individual features.

To illustrate this point, take the example of a man named Bob who wants to dine at a new restaurant. Bob asks his friend James for a few suggestions. James asks Bob questions related to his likes and dislikes in food, budget, area, and other relevant questions. In the end, James uses Bob’s answers to suggest a suitable restaurant. This whole process mirrors a decision tree.

Moving forward, Bob is not happy with the recommendation of James and wants to explore other dining options. Thus, he begins asking other friends for their recommendations. He goes to 5 more people who act similar to James; they ask him relevant questions to provide a recommendation. In the end, Bob goes through all the answers and picks the most common answer. Here, each of Bob’s friend acts as a decision tree and their combined answers generate a random forest.

Determining the relative importance of the feature of a prediction is easy in random forest, especially if it is compared with others. If you are looking for a tool which can aid you to calculate such values, then do consider the scikit-learn—a machine learning library in Python.

In the post-training period, a score is assigned to all of the features so the results can be scaled. This makes sure that a zero value is placed for each of the importance sum.

The assessment of feature importance is crucial in order to drop a feature. Usually, a feature is dropped when it struggles to add anything of value to the prediction. This is done because too many features pose the issue of over-fitting.


One of the factors that make random forest unique is the precise output which does not require tuning of hyper-parameters. Like a decision tree, a random forest carries its own hyper-parameters.

In random forest, hyper-parameters are used for increasing the speed of the model. Following are the scikit-learn’s hyper-parameters.

  • n_jobs

It provides the engine with details about the limit of processor for computational usage. If it has a “1” value, then this indicates that only a single processor can be run. On the contrary a “-1” value indicates that there is no restriction.

  • N_estimators

It is the overall number of trees to be generated in the time period before the determination of max voting and averages for predictions. A larger number of figures increases reliability but it also affects the performance speed.

  • random_state

It is used to convert the model’s output to create a replicable result. If similar piece of training data, a definite value for random_state, and hyper-parameters are inserted in the model, then the output would also be identical.

  • min_sample_leaf

It examines the lowest limit of a leaf for the split of internet nodes.

  • Max_features

It takes the figure of maximum digit of features that are required to be used in each tree.

Now that you have understood how a random forest algorithm works, you should begin implementing through coding. For more information about machine learning, check other blogs on our website.

What Is Classification and Regression in Machine Learning?



Machine learning is divided into types: supervised and unsupervised machine learning. In supervised machine learning, inputs and outputs are offered. To aid judgment in future, it offers several algorithms of fixed quantities. Supervised machine learning algorithms have come up with applications like chatbots, facial expression system, etc.

Both classification and regression fall into the category of supervised learning. So what are they and why is it necessary to understand them?


If your dataset requires you to work with discrete values, then you should use classification. When the solution to a problem demands a definite or predetermined range of output, then you most probably have to deal with classification. The following scenarios are one of the few examples where classification is used.

  • To determine consumer demographics.
  • To predict the likelihood of a loan.
  • To check who wins or lose a coin toss.

When a problem can have only two answers (yes or no), then such a classification falls under the category binary classification.

On the other hand, multi-label classification processes several variables. This type of classification comes handy in the above-mentioned consumer segmentation, grouping images, and text and audio analysis. For instance, a sports blog can have posted about multiple sports like basketball, baseball, tennis, football, and others, at the same time.

There is also the multi-class classification in which a target defines a sample. For example, it is possible for a fruit to be apple or banana but it cannot become both at the same time.

Classification computes only those values which are “observed”. It relies on the total of its input to compute forecasting which offers more than a single result. The algorithm which maps a provided input into a specific category is referred to as the classifier. The feature is a measurable variable.

Before the creation of a classification model, firstly you have to pick a classifier and have it initialized. Subsequently, you have to provide some training to that classifier. In the end, you can check the output for the observed x values to predict the label y.


Regression works opposite to classification; it is used for the prediction of results where continuous values are at play. In regression, the variables are flexible and can be modified, unlike classification hence; there is no need to restrict to a fixed set of labels.

Linear regression is one of the leading algorithms. Sometimes, linear regression is underestimated as some perceive its working to be too easy. However, in actuality, linear regression can be used in multiple cases, as it is quite simple in comparison to others. You can use logistic regression to estimate the prices of property, assess the churn rate of customers, and even manage the collection of money from that person.

For more details, keep following this blog series. If you have any questions, then you contact us to clear up your confusion.