Consumers today look for futuristic technology made products that they can connect to and interact with, rather than just a car with four wheels and a steering wheel. Next-generation vehicles are being built by the automotive industry with the help of the Internet of Things (IoT). Worldwide car manufacturers and buyers now have new opportunities thanks to its introduction. IoT in the automotive sector has significantly impacted the global auto market and has emerged as a key component in cutting-edge applications ranging from automated transportation systems to connected cars.

What is the Internet of Things?

In simple words, Internet of Things refers to the interconnectivity of thousands of devices like actuators, sensors, gateways, and platforms. These devices connect and interact with each other over wired/wireless networks.

IoT is very powerful technology, and it has been estimated that there will be more than 21 billion connected devices by the end of the year 2025. Most of the IoT implementation was first seen in manufacturing companies where systems like ADAS (Advanced Driver Assistance Systems) were implemented to reduce production costs.

However, it should be noted that the Internet of Things offers countless opportunities and has a wide range of uses, including the automotive sector. The way people interact with their vehicles is being revolutionised by various automotive IoT applications. Let us now see a few of the amazing things that IoT offers for the automotive industry.

Also read: Enhance Efficiency in Manufacturing and Production with IoT & Advanced Analytics

Major applications in the Automotive Sector:

1. Fleet Management

Fleet management has undergone a significant development as a result of IoT implementation in the automotive industry. In modern trucks, sensors for tyre pressure, engine temperature, and other factors are integrated. The sensors can weigh and count the packages inside the vehicle and can also track its location.

All the sensory data from the large fleet of trucks is gathered and sent to the cloud, where it is processed by various analytics and displayed in an easily understandable visual format for the user. This information can be quickly reviewed by the fleet manager or operator, who can also monitor several fleet-related parameters. An IoT-integrated fleet management system can benefit a fleet manager in a number of ways, including:

• Real-time location monitoring of each individual truck in the fleet
• Weight or Volume monitoring of the cargo being transported.
• Vehicle performance statistics like fuel efficiency and speed
• Vehicle health statistics
• Route-management
• Planned routes to avoid heavy traffic conditions on the route.
• Time and Driver Management
• Driving pattern of each driver (Driving pattern data of skilled drivers can be collected and fed to regression algorithms which will help autonomous cars to learn to drive better)

2. Connected Cars

Over 30 million connected cars were sold in the year 2020 and it is estimated that more than half of the cars sold new in 2023 would be connected. These cars are equipped with CV2X (cellular vehicle to everything) IoT technology that connects vehicles and smart transport systems with each other.

Connected cars transmit data at a much faster rate to different objects based on which CV2X can be categorised as below:

1. Vehicle to vehicle (V2V):  In a V2V-Connection, vehicles that are close to one another (within a certain range) are connected to one another and exchange data. The shared data typically consists of details about the location, speed, and size of the vehicle. It is simple for vehicles to help the driver because every connected vehicle in a given area is connected and has knowledge of vehicles close by. It should also give emergency vehicles a better route and reduce accidents.

2. Vehicle to infrastructure (V2I): This V2I connection refers to the connection between different vehicles and road infrastructures. By infrastructure, we refer to streetlights, road width, traffic lights, lane markings, and toll booths. When these infrastructures are connected, this connection gives the vehicle a better understanding of the nearby infrastructure, which in turn allows the vehicle to better assist the driver. V2I also provides smoother traffic flow.

3. Vehicle to pedestrians (V2P):  Nowadays, most pedestrians have smartphones, making it possible to connect a pedestrian to the CV2X network. This aids in locating pedestrians so that the vehicle can avoid them. An app can be used by a pedestrian to find nearby taxis and track the anticipated arrival time for transit. Additionally, it can be used to alter traffic lights so that you can cross a road.

4. Vehicle to network (V2N):  The weather forecast office and the transportation system can both connect to the network to alert drivers to changes in the weather or to any on-road accidents. Additionally, a car can be linked to a smartphone. In this manner, the car’s music system and GPS can be controlled by voice commands while the driver is on the road.

3. Automotive Maintenance System

Predictive analytics is one of the most interesting features of IoT in the automotive sector, as it will save a ton of cost and time for the vehicle manufacturer and the buyer. Different sensors are embedded in different components of a car which collect data and share it with a platform. This data is then processed by an algorithm that can analyze and predict the future state of the component based on its current usage pattern.

A person can use an IoT automotive maintenance system to help them take the necessary precautions to stop a car from breaking down unexpectedly. By UI notification, message, or email, this system notifies the driver of potential malfunctions. It’s interesting to note that the driver receives the alert before the fault is discovered. The driver can fix the issue and avoid vehicle breakdowns on important journeys thanks to this forecast. The driver will also have saved time and money by avoiding a major failure.

Both a fleet of vehicles and a single vehicle can use predictive maintenance’s capabilities. Monitoring and managing a defect in their vehicles before it manifests is extremely beneficial for transport and logistics companies.

Let us see a few predictive maintenance algorithms.

• Linear Regression
• Logistic Regression
• Neural Network
• Decision trees
• Naive Bayes models

4. Autonomous Vehicles

One of the most crucial technologies for auto manufacturers to master is autonomous driving. Many automakers are working to create a fully autonomous vehicle that will take over all aspects of driving from the driver. Tesla is at the cutting edge of this technology and offers autonomous driving on all of its models.

However, a number of other manufacturers have already introduced semi-autonomous vehicles that help drivers with some aspects of driving, braking, parking, and lane changing. IoT-integrated semi-autonomous cars help the driver by partially controlling the vehicle operation to reduce accidents and enhance the driving experience. For instance, the hill climb assist feature enables the driver to move the car quickly and easily from a stopped position while standing on an incline. This is made possible by sensors that recognise the angle at which the car has stopped and temporarily apply the brakes so that the driver can press the accelerator.

Conclusion

The automotive industry is experiencing a significant transformation through the integration of advanced technologies such as the Internet of Things (IoT).  This evolution has led to the development of connected and automated cars, revolutionizing the way car inspection and maintenance are carried out and introducing novel forms of entertainment. The advent of vehicular telematics has facilitated long-range data transmission, which has been instrumental in the emergence of IoT-powered fleet management systems.

As IoT technology continues to advance, its applications in the automotive industry are increasing steadily, and it is expected that more sophisticated applications will emerge, fundamentally transforming the way we interact with our vehicles.

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Small data consumes less data but is just as insightful because it leverages techniques such as;

• Time-series analysis techniques
• Few-shot learning
• Synthetic data
• Self-supervised learning

Wide refers to the use of unstructured and structured data sources to draw insights. Together, small and wide data can be used across industries for predicting consumer behavior, improving customer service, and extracting behavioral and emotional intelligence in real-time. This facilitates hyper-personalization and provides customers with an improved customer experience. It can also be used to improve security, detect fraud, and develop adaptive autonomous systems such as robots that use machine learning algorithms to continuously improve performance.

Why is big data not relevant anymore?

First being the large volumes of data being produced everyday from nearly 4.9 billion people browsing the internet for an average of seven hours a day. Further, embedded sensors are also continuously generating stream data throughout the day, making big data even bigger.

Secondly, big data processing tools are unable to keep pace and pull data on demand. Big data can be complex and difficult to manage due to the various intricacies involved, right from ingesting the raw data to making it ready for analytics. Despite storing millions or even billions of records, it may still not be big data unless it is usable and of good quality. Moreover, for data to be truly meaningful in providing a holistic view, it will have to be aggregated from different sources, and be in structured and unstructured formats. Proper organization of data is essential to keep it stable and access it when needed. This can be difficult in the case of big data.

Thirdly, there is a dearth of skilled big data technology experts. Analyzing big data requires data scientists to clean and organize the data stored in data lakes and warehouses before integrating and running analytics pipelines. The quality of insights is determined by the size of the IT infrastructure, which, in turn, is restricted by the investment capabilities of the enterprises.

What is small data?

Small data can be understood as structured or unstructured data collected over a period of time in key functional areas. Small data is less than a terabyte in size. It includes;

• Sales information
• Operational performance data
• Purchasing data

It is decentralized and can fit data packets securely and with interoperable wrappers. It can facilitate the development of effective AI models, provide meaningful insights, and help capture trends. Prior to adding larger and more semi-or unstructured data, the integrity, accessibility, and usefulness of the core data should be ascertained.

Benefits of Small Data

Having a separate small data initiative can prove beneficial for the enterprise in many ways. It can address core strategic problems about the business and improve the application of big data and advanced analytics. Business leaders can gain insights even in the absence of substantial big data. Managing small data efficiently can improve overall data management.

Some of the advantages of small data are:

• It is present everywhere: Anybody with a smartphone or a computer can generate small data every time they use social media or an app. Social media is a mine of information on buyer preferences and decisions.
• Gain quick insights:  Small data is easy to understand and can provide quick actionable insights for making strategic decisions to remain competitive and innovative.
• It is end-user focused: When choosing the cheapest ticket or the best deals, customers are actually using small data. So, small data can help businesses understand what their customers are looking for and customize their solutions accordingly.
• Enable self-service: Small data can be used by business users and other stakeholders without needing expert interpretation. This can accelerate the speed of decision making for timely response to events in real-time.

For small data to be useful, it has to be verifiable and have integrity. It must be self-describing and interoperable.

Indium can help small data work for you

Indium Software, a cutting-edge software development firm, has a team of dedicated data scientists who can help with data management, both small and big. Recognized by ISG as a strong contender for data science, data engineering, and data lifecycle management services, the company works closely with customers to identify their business needs and organize data for optimum results.

Indium can design the data architecture to meet customers’ small and large data needs. They also work with a variety of tools and technologies based on the cost and needs of customers. Their vast experience and deep expertise in open source and commercial tools enable them to help customers meet their unique data engineering and analytics goals.

FAQs

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● Inventory demand planning

● Marketing campaign optimization

● Sales lead conversions

● Financial budgeting.

In the last few years, data generation and technological advancements have accelerated tremendously. For instance, bytes of data generated increased from 2.5 quintillion bytes every day in 2018 to nearly 1.7 MB every second by 2020. There has been rapid adoption of technologies such as IoT, cloud services, AI/ML, and data generation which has provided people with access and the ability to harness analytics across business applications. Users can view data in context and garner valuable insight to make informed decisions, which lead to better outcomes.

As a result, the embedded analytics market is also growing, from USD 36.08 billion in 2020 to USD 77.52 billion by the end of 2026, at a compounded annual growth rate of 13.6%.

Why Embed Analytics in Your App?

Data and analytics solutions are acknowledged as being critical components of digital transformation initiatives. Integrating them with the process workflows as embedded analytics

helps businesses experience significant benefits such as marketplace expansion, revenue growth, and competitive advantage.

With embedded analytics, customers are given access to the data they need in a timely manner, empowering them to analyze and make informed decisions. Users are also given the freedom to choose from a variety of dashboards, charts, graphs, and KPI widgets to visualize data in the most appropriate manner and draw their own conclusions. This helps them with improve customer experience by responding immediately and in the best possible way to their requirements. It helps identify strengths and weaknesses and increase operational efficiency. It also helps different teams to collaborate and work together on increasing efficiency and effectiveness of their improvement efforts.

By embedding analytics into their products, app developers can:

Enhance Application Value: Measure usage by number, depth, and session length to assess the value of your product. By embedding analytics into the app, users can access key metrics while using it, reducing exits and increasing session lengths. The insights also help with identifying strengths and areas for improvement. It can also be a key differentiating factor, enhancing the value of the application.

Facilitate Data-driven Decisions: Access to data visualizations within the app enables users to make informed decisions based on real-time data analytics. It helps uncover insights otherwise not easily available. It will also help to draw correlations and discover interrelationships between data.

Improve Pricing Strategy: Plugging in pre-built data visualizations when building new products can enhance the value of the app and increase its usefulness for the customers. This can help with pricing the product at a premium and improve profitability.

Benefits of Embedded Analytics

Data is the new oil that is helping businesses become more efficient and profitable. With embedded analytics, companies can increase their competitive advantage. Embedding analytics is proliferating across industries and functions. For instance, finance tools embed analytics tools to help customers analyze their income and outgoes. Utilities related tools help customers identify usage patterns and optimize consumption to lower energy bills. It can help discover new markets or build new features that customers seek. It can help serve customers better by anticipating their needs and providing timely service.

According to one Frost & Sullivan guidance, with embedded analytics, organizations can improve customer experiences, increase operational efficiencies, and reduce the time to market new products and services.

5 Kinds of Embedding

There are five levels at which analytics may be embedded. These are:

Web Embedding: This is the most basic form but highly effective, popular, and relevant. iFrames and HTML or JavaScript are used to embed the code needed to publish reports, dashboards, and data visualizations to websites.

Secure Custom Portals: The visualizations and reports are aggregated and published to a portal that could be meant for internal purposes or external, for customers and partners. Such portals are secure, with controls, and enable personalization, scheduling, and custom styling and branding.

SaaS/COTS Embedding: In this kind of embedding, two-way interactivity is possible with authentication and row-level controls for secure access to data. Typically, these are commercial off-the-shelf software (COTS), and so, it is essential to ensure that it does not need a separate analytical interface for running analytics.

Real-time Interactive: Also called context analytics, it can be accessed from specific areas or functions within enterprise software or a bespoke solution. This needs rich software development kits (SDKs) that can provide both interactive and predictive capabilities. Such a solution is cloud-friendly, is flexible and agile, and can be upgraded and customized..

Action-oriented Analytics: This is a very high level intelligent data application with low-code or no-code development capabilities that can learn and adapt. It can facilitate event triggers, automation, and workflows, triggering action and supporting scenarios even if analysis is not possible.

Which of these 5 levels of embedded analytics will go into an app will depend on the application needs and the development environment.

Indium for Embedded Analytics Solution

Indium Software is a digital engineering solution provider with capabilities in app engineering and data and analytics. The cross-domain expertise helps the team develop innovative solutions to meet the unique needs of its customers. The team works closely with the customers to understand their needs and offer solutions that can help them improve their competitive advantage and app value.

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The stress on improvement in process and quality led to the development of methodologies such as Lean and Six Sigma to increase throughput but was still driven by humans with the technology used only for metrics and advanced analysis.

Though manufacturing companies did derive much benefit from these methodologies, the advent of Industry 4.0 technologies such as cloud, artificial intelligence, and Industrial Internet of Things (IIoT) devices has magnified the benefits manifold.

IIoT has made it possible for manufacturers to create smart factories and integrate systems. This has provided them with a unified data source that enables advanced analytics to identify patterns and trends and facilitate informed decision making.

End-to-end connection of machines right from production to delivery provides manufacturers with visibility improving the formulation of strategies and policies for accelerating growth.

Fast-Paced Adoption of IIoT

The integration of systems enables manufacturing companies to have better control of their inventory and supply chain as well as improve energy management. This naturally leads to cost reduction, resource optimization, increased profitability and overall enhanced operational efficiency due to industrial automation, centralized monitoring and predictive maintenance of assets.

No wonder then that the market for IoT in manufacturing industries is expected to grow at a CAGR of 10.1%, from USD 33.2 billion in 2020 to USD 53.8 billion by 2025, according to a ResearchAndMarkets.com report.

A PwC survey of around 1,000 industrial manufacturers revealed that 71% were already building or testing IoT-related solutions in both active and in-development projects and 68% intended to increase their investment in the next couple of years.

The surveyed companies were investing in better technology infrastructure, data management, workforce culture and change management to reap the benefits of digital transformation.

Benefits of Smart Factories

A smart factory with interconnected systems can automate workflows across functions and manage complex processes with greater visibility and traceability. Some of the key areas where they can see the advantages of IIoT devices include:

1. Predictive Maintenance: The breakdown of machinery and the resulting disruption to production is one of the biggest challenges manufacturing companies face. This causes unexpected delays in addition to the cost of repair. In smart factories, sensors embedded in the machinery provide data that can help analyze machine performance as well as receive alerts in case of any issues or deviations from preset specifications for preventive maintenance. This improves the longevity of the machinery, effects cost savings as well as enables scheduling maintenance in a more planned manner.

2. Product Quality: A piece of faulty equipment can also affect product quality. Embedded technologies can help manufacturers keep their machines well-calibrated to ensure that the machinery is as per specifications and can produce the desired product.

3. Supply Chain Management: The IoT devices can be connected to the ERP or SCM system to track inventory and draw real-time insights about product movement from raw materials to finished goods for a smooth supply chain management. It enables the different departments to have a view of the production process and also removes the need for manual documentation, thereby reducing manual errors and the resultant costs.

4. Safety and Security: Worker safety and security in the plant are becoming important due to regulatory requirements as well as to reassure employees and improve their engagement with the business. IoT systems can make it easier for safety leaders to be alerted in case of any potential hazards and risks and monitor Key Performance Indicators (KPIs) of health and security to not only improve compliance but also make the shop floor safe.

5. Energy Efficiency: Not only is energy one of the highest areas of expenditure for manufacturing companies, but it is also one of the most important areas where conservation is the most needed. IoT devices can help identify inefficiencies at the device level to enable businesses to address them effectively. This can help reduce waste and also meet regulatory standards more efficiently and effectively.

The integration of systems also ensures access to enterprise-wide data that facilitates better visibility into operations and more informed decisions. This provides a competitive advantage in addressing potential challenges before they become a problem and helps managers take a proactive approach rather than a reactive one.

At Indium Software, serving the manufacturing sector has been one of our key focus areas and, over the last decade, we’ve picked up immense expertise in serving fast-growing manufacturing companies in industrial, energy, automotive, and diversified segments.

The core of Industry 4.0 revolves around data. And, Indium’s experience in data management and data engineering are key assets while serving this segment.

Challenges to IoT

IoT comes with its own challenges too: Cost, Security, and Lack of Standards, to specifically name a few points.

Manufacturing companies with legacy equipment may find that customizing their existing machinery to scale up to become an embedded device comes at a cost. However, this can be more cost-effective than investing in new equipment and provide the flexibility they require.

Therefore, identifying the right partner who understands their business and can develop bespoke solutions that enable digital transformation at a reasonable cost would be a prime requirement.

The second is security. As more and more devices get added, the security environment becomes that much more complex. Ensuring encryption and other protection to safeguard data would be the second criterion that a partner should be able to ensure.

Using open frameworks and modern software development tools to write IoT firmware can help overcome the limitations of the lack of standards.

A partner such as Indium Software, with more than two decades of experience in cutting edge technologies, can help manufacturing companies experience painless digital transformation.

Our team of experts has experience in Industry 4.0 technologies, IoT, open frameworks, data engineering, security and testing, which is combined with cross-domain expertise to deliver best-fit solutions meeting the unique needs of our customers.

If you would like to know how we can help you improve your operational efficiency with IoT on your shop floor, contact us now.

https://www.indiumsoftware.com/inquire-now/

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The use of external APIs can be obvious in some situations such as when YouTube video is embedded in a news story, or when the Pay with PayPal is clicked, where the user is redirected to a PayPal pop-up to complete the order with an API call.

For instance, API calls that occur when we communicate within a webpage may not be known to us. Instead of using PayPal, if the user wants to pay using credit cards, the website will possibly call a different API to check if the entered information is correct. If the payment is verified, a reply is sent back to the original website by the application enabling the user to complete the transaction online.

In order to build new products, API integrations let new applications and websites to reuse the existing applications and data.

Organizations that create APIs often benefit from such integrations. APIs can be monetized directly by charging for use or indirectly by enabling companies to attract new customers or collaborations. When used internally, APIs help to automate and simplify business processes and reuse data and processes within and around the organisation.

As a result of the financial value of APIs, API initiatives are taking place in almost every organisation. However, when designing these projects, the important question is how to create data services that will ne revealed as your APIs efficiently. Data needs to be integrated first through several types of source systems which can be a dynamic, time-consuming process.

Data Virtualization

Data virtualization serves as a bridge between various disparate data sources, putting vital decision-making data together in one virtual location to power analytics. Data virtualization provides a modern data layer that allows users to access, merge, transform and distribute datasets with cutting-edge speed and cost. It allows users easy access to data stored around the enterprise- including conventional databases, large data sources and cloud storage spaces.

Via integrated governance and protection, data virtualization users are assured that their data is consistent of high quality and secured data. In addition, it also allows businesses to easily understand IT curated data resources that are easy to find and use through a self-service business directory.

Key benefits of using a Data virtualization service

Data virtualization allows you to integrate data distributed through various physical locations into logical business models without the need to transfer data from the underlying sources.

Data virtualization offers a variety of primary advantages for an organisation.

• Data is combined and converted in real-time, meaning consumers or applications have access to the latest data without having to connect directly to the source. The data virtualization layer summarises the location and format of the original data so that the end user is not subject to ambiguity of the underlying data model. This implies that if adjustments are made to the underlying data, the logical represent
• Logical datasets generated in the data virtualization layer can be used regardless of the consumption process to determine the accuracy of the data across the enterprise.

Creating a Data layer

Using traditional architecture techniques to develop data services can be both sluggish and expensive. Data virtualization techniques allow you to build stable data services over models in your virtual layer using a simple graphical interface. With only a few clicks, these can be developed and implemented using various protocols and supporting all major security and documentation requirements.

Logical datasets used to build data services are the same datasets that are accessible to other users connected to a data virtualization layer. This allows application developers, web portals, or any other frameworks to use the same approved datasets as BI teams eliminating the issue of developers producing their own uncertified silo datasets. This has the ability to generate logical data models with integrated real-time data, in combination with easy-to-use publishing options for data services layer to accelerate your API initiative.

Other Benefits of Data Virtualization

• Centralized layer for protection
• Potential data governance aid
• Advanced query optimization techniques for effective federation
• Advanced caching features
• Assistance for the control of workload

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