IoT - Panth Softech

admin

June 7, 2022

Role of IoT in Electric Vehicle Monitoring & Management

Today we are witnessing the temperature rise, and one of the major reasons behind this is air pollution. The emission of global house gases is worsening the situation, and its continuity might leave the earth unfit for humans in the coming years.

Vehicles are one of the major contributors to air pollution; therefore, it has become a major issue to look after. Today, people and the government are looking for ways to handle this issue. One of the best ways to control air pollution is by replacing fuel-based vehicles with electronic vehicles. Electric vehicles (EVs) are a new and environment-friendly innovation in this direction.

Electronic vehicles are hi-tech machines that collect an immense amount of data to deliver optimum performance. The performance parameters incorporate monitoring speed, mileage, acceleration, battery management, fault alert, charging, and predictive maintenance systems. Therefore IoT plays a crucial role in the monitoring of electric vehicles.

What is the role of IoT in Electric Vehicle Management?

Let’s know each aspect of an IoT integrated Electric vehicle management system and how they help obtain the optimal performance of electric vehicles.

Battery Management System:

The primary function of the Battery Management System is to watch and control the battery’s functioning. This implies monitoring the charging and discharging cycle to ensure battery health and minimize the risk of battery damage by assuring that optimized energy is provided to run the vehicle.

The monitoring circuit in Battery Management System (BMS) monitors the key parameters of the battery, that is, current, voltage, and temperature during charging and discharging conditions. It assesses parameters like power, State of Health (SoH), and State of Charge (SoC) and assures good health based on the calculation. Internet of Things exhibits a vital role in monitoring and controlling as it allows remote data logging facility for battery parameters, conditions, etc. Most EV manufacturers use high-quality Li-ion battery packs as they have a longer life and exceptionally high energy density.

However, there are some drawbacks as well. In situations when battery malfunction happens, the onboard sensor data acquired using IoT can aid in managing the issues. Then, these can operate through AI-based models for performance estimation. Tests can be executed on some Li-ions to evaluate the patterns of partial and full charging and discharging. Models are marked using the data gathered from each step and are integrated with Artificial Intelligence before deploying on a server. The EV sends important sensor data to the server, delivering insights on the next course of action and performance. We can conclude that the server checks the condition of the Electronic Vehicle.

Safety and Smart Driving:

The adoption of Iot also allows real-time monitoring of the vehicles and their parts. It helps in preventive maintenance provided by the technology, which is found to be more reliable by the users. IoT devices attached to EVs can offer the following features to the users.

It can measure the exact parameters of the driver like speed, acceleration, and many other things to offer real-time tips to ensure optimal performance.
It can prevent theft by real-time tracking, geo-fencing, and immobilization. This ensures better safety and security to diminish the dependence on insurance.
It also checks the performance data of the vehicle, based on which EV and battery OEMs can enhance thee products. Here parameters are the range of each charge, use of a vehicle, performance difference based on geography, age, weather conditions, and adjustment in range for each charge over a certain period.

Fault Alert and Preventive Maintenance System:

Electronic vehicles also face technical glitches as other machines do. IoT-enabled fault alert systems can help alert vehicle drivers about the EV faults, providing them time to act and address them before it’s too late. Though EVs are well designed to prohibit errors, sometimes parts might fail or stop.

To anticipate this, AI algorithms and remote IoT data play a vital role. They help alert the EV users and provide them time to resolve the issues before they actually happen. This enhances customer experience as they can rely on it for optimal performance. In addition, it is necessary to know the overall temperature and moisture conditions in various geographies, and keeping a check on remote performance is essential. These factors will help resolve the issue promptly and promises comfort and security to the user.

Telematics Data:

By using IoT-based telematics technology, data is gathered when linked to the vehicle sensors, shown through widgets, instant notifications, and produce automatic reports.

Let’s look at the useful factors of employing telematics for monitoring distant electric vehicles.

Battery Usage Data: Electric vehicles with telematics allow users to track real-time battery usage data. It lets users check important parameters like current, voltage, and temperature to skip battery breakdowns. Battery usage of EVs can be recorded and shared to a remote server that empowers to customize the battery configuration and enhance the best charging practices.
Charging Report: The addition of telematics in electric vehicles allows to yield reports on the vehicles’ entire charging sessions, i.e., the entire lifespan. The charging report shows the time duration, location of the charger station, and percentage of charge received by the vehicles.
Nearby Charging Stations Alert: Electric vehicle users encounter challenges like knowing state-of-charge(SOC) to schedule when and where to charge. Electric vehicles keep a tap on solutions with telematics and alert the user concerning the vehicles’ low battery level and the informs about the available nearby charging station.
Driver Behavior Data: Electric vehicle remote monitoring system with telematics ensures safety by monitoring and analyzing the electric vehicle performance data and also checks the behavioral data of the driver. Telematic provides quick feedback on driver’s behavior changes to fleet managers/owners through IoT enabled smartphone app. This ensures safety and improvement for better output.

Challenges of IoT in Electric Vehicle Management

Let us know some possible challenges of IoT for monitoring electric vehicles.

Cybersecurity:

The generation of the high amount of data and its transit over a network makes this data vulnerable to cyber-attacks and data leakage. Therefore, it is essential to strengthening the IoT networks used in the EV system to ensure no data leakage.

High Cost:

IoT systems in EVs are expensive. They are highly advanced and have high installation and operating costs. Thus, this technology requires more R&D, and the future might provide better and more cost-friendly IoT solutions.

Weighing the Benefits & Challenges:

We can conclude that IoT plays a crucial role in monitoring electric vehicles. The performance parameters enclose monitoring speed, mileage, acceleration, battery management, fault alert, charging, and predictive maintenance systems.

Overall, IoT holds an important place in the success of electric vehicles. However, challenges like cybersecurity should be considered seriously. EVs are innovative steps toward the environment, and their success will promise a better and green future.

How is Data Science for IoT changing business outlook

admin

May 26, 2022

How is Data Science for IoT Changing Business Outlook?

The Internet of Things has been noticed as a shape-changing technology that has changed the shape and working process of everything it has touched, either businesses or our daily lives. It has changed the outlook of every individual living a mediocre life into a smart device-connected life.

IoT connected devices produce tremendous amounts of data wirelessly over the network without any human interference, which is proved to be best for organizations trying to offer the best services to their clients. The only challenge is that IoT generates immense data for traditional data science.

Data Science and How It Applies to IoT

We can simply define data science as a study of processes that assists in extracting value from data. In the IoT system, data is referred to information produced by sensors, devices, applications, and other smart gadgets. Meanwhile, value means predicting future trends and outcomes based on the data.

For instance, suppose you are using a fitness tracker that calculates the number of daily steps. Using this information, data science can predict that:

Amount of calories burnt by you
How many kgs do you lose
When is the possible best time for your workout

This is a simple example of how data science works. Internet of Things is different as it produces high-volume data.

As per the reports, the amount of data to be produced by IoT by 2025 is around 73.1 zettabytes. This will cause trouble for standard data science as it cannot handle it, so it will have to update. Thus, IoT will help data science to go to the next level.

What are the Differences Between Traditional and Data Science for IoT?

There are only a few differences between traditional and IoT-based data science, so here we will check a few critical distinctions.

Data Science for IoT Is Dynamic:

The traditional version of data science is static as it is primarily based on historical information. For example, a company collects data from its clients about their choices and needs. The historical data becomes a base for predictive models that assist the company in understanding its future customers.

On the other hand, IoT changes the dynamic of data analysis as it is all about real-time sensor readings from smart devices. The gathered information permits data science consultants to create highly precise evaluations instantly.

In this case, customer data changes and updates- a feature that is not available in traditional data science. Data science for IoT allows continuous learning, changes with time, and improves operational processes simultaneously.

IoT Drives Larger Data Volumes:

Data science is developing with IoT because of its immense data processing. Here we are not discussing megabytes or gigabytes of data but data science for IoT deals with a massive amount of data that often reach zettabytes.

Better Predictive Analytics Method:

Data science for the Internet of Things is dynamic and wider than the traditional one. Additionally, it also makes a better predictive analytics method.

Thus, data science assists businesses in a great way; using it, businesses can develop better solutions that can diminish operational costs and acquire business growth.

IoT can improve this further through its real-time capabilities. IoT helps make decisions more accurate, assisting companies in identifying new opportunities and improving sales and customer experience while optimizing performance.

The Challenges faced by IoT Data Science:

We all know that data science for IoT holds vast potential, but it comes with challenges. Four major risks have to be overcome before it becomes mainstream.

Data Management and Security:

IoT produces a tremendous amount of data, which also implies that there are high chances of hacking or leaking private information. For example, Suppose hackers hijack the connection between the fitness tracker and doctor’s office app; they can easily access sensitive health records. Thus, it is pretty clear that privacy problems are the major issues with IoT data science.

For instance, many companies often face backlashes for releasing customers’ sensitive information without their consent.

Scaling Problems:

IoT data science is also important, but users often struggle to scale it up to fulfill their demands. When an organization plans to integrate an IoT system or add new sensors to its existing software solutions, it faces some issues and challenges.

Therefore, it is important to prepare for scaling projects in advance. Businesses must set up everything from software to personnel to scale data science processes successfully.

Data Analytics Skills:

Data science for IoT is extensively helpful, but classical data science consultant holds good dominance in the market as IoT analytics is still not very much embraced.

However, this could change soon as more companies adopt IoT technology. IoT scientists will have to add new skills and understand the oddities of the deployment process. For this purpose, they’ll have to learn about the following:

Edge Computing: It is defined as the practice of processing data close to the source to improve performance and reduce network congestion.
Computer-Aided Design: It is essential to know the logic behind the physical design of a smart device.
IoT Computing Frameworks: Data scientists must also employ open-source learning tools to grasp IoT hardware.

Operating Costs:

Another major problem with data science for IoT is the huge cost required to introduce new technology. This is the case for most companies willing to join this latest technology on a larger scale but is restricted by budget.

The Bottom Line:

We can conclude that data science for IoT brings a major upgrade to traditional data analytics. It requires efforts and dedication to make data science more robust, powerful, and accurate. IoT can make it possible through data generation abilities. The interconnected devices over the internet constantly communicate to offer businesses a huge amount of user-related data. This allows data scientists to draw relevant conclusions from their databases.

However, the process of deploying data science for IoT is not an easy task, but the benefits it provides negate every challenge. So, we can expect data science for IoT to be a part of the future at a great scale.

How is IoT Helping The Procurement Team in Improving Productivity

admin

May 13, 2022

How is IoT Helping The Procurement Team in Improving Productivity?

Today, almost every device is connected; whether it is your smartwatch, air conditioner, or television, we can say it’s a world where devices are more connected than people. No, doubt these connected gadgets present around us make our lives easier by working systematically. This is possible because of the most popular concept known as the Internet of Things, which can also influence the procurement team.

IoT, a.k.a Internet of Things, can be defined as a network of interconnected computing devices, either mechanical or digital machines. This technology allows transferring data without human-to-human interaction or human-to-computer interaction. Communication is possible using networks and cloud-based systems.

An IoT ecosystem includes web-enabled smart devices that collect, send and work on data collected from their surroundings utilizing embedded systems such as CPUs, sensors, and communication hardware.

IoT devices can exchange sensor data stored in the cloud for analysis purposes or examined locally by interlinking to an IoT gateway or other edge devices.

Besides this, these gadgets can connect with other related devices and respond according to the information they receive from one another. Even individuals can operate the devices for the beginning setup, give instructions, or recover data; the device can perform most of the tasks without human interference.

The Role of IoT in Procurement

Procurement is an important part of the business. It demands the implementation of new technologies to boost productivity, enhance customer service and save costs. As of now, the procurement process is also embracing automation; IoT in this process is one of the most exclusive things happening in the era of digital transformation.

The inclusion of the Internet of Things will provide greater spending visibility and understanding of the supply and equipment used for the procurement process. So, with a proper understanding of what is being used and the requirement specified, the procurement team will have access to optimize catalogs and manage expenditure. Forecasting demands more closely using analytics can significantly improve budget and contract management. This also helps in improving budget and contract management. Despite this, the data generated through IoT sensors and other devices can assist in making informed decisions.

Let’s know how IoT works in procurement.

Traceability of Materials:

A study done by a McKinsey Global Institute shows that by the end of 2025, the Internet of Things’ possible contributions to inventory management, logistics, and supply chain management would reach 560 billion to $850 billion per year. This shows the possible IoT-oriented future awaiting us. Most of the time, IoT contributes to these sections by tracking. IoT sensors can help in making inventory management systems more effective.

For instance, RFID tags connected with IoT devices can track physical inventories and eliminates the need to scan barcodes or labels. In fact, businesses with vast inventory can track the days before items expire using interlinked IoT devices, saving the business from huge losses. IoT also prevents product theft by enabling businesses to know the location of their products.

With the use of machine learning, procurement teams can manage products per demand.

Supply Chain Visibility:

In this process, the procurement team can also potentially use IoT technology. Supply chain visibility, items are documented as transported from the manufacturer to the customer. An IoT-enabled system can read data from various devices like smart tags and sensory data like surrounding temperature and humidity, vehicle speed, and geolocation and accordingly follow the supply chain when connected to it.

The adoption of IoT devices to track inventory and route planning provides the details about where and when items are delayed in transportation. This allows emergency planning and identification of other options to accelerate the supply chain.

Stock Management:

Along with smart shelves and storage bins that inform about the stock levels in real-time and how long the product has been on the shelf, IoT also assists in detecting the pattern of consumption.

For instance, if a product named X is on shelf A and has been the quickest utilized item, IoT sensors will monitor the usage rate and suggest its economic order quantity (EOQ).

This clears how essential procuring an item is, which products are needed, and what amount. Procuring the right inventory quantity reduces costs by lowering waste and the menace of shortage.

Monitor and Alert Maintenance:

The sudden breakdown of equipment in a production unit is the most horrifying dream as it disrupts the business. If the condition of the equipment is not known, things become more difficult and result into process disturbance, indefinite downtime, and even business loss. Regular monitoring of the equipment’s condition through IoT sensors permits the team to watch indicators like vibration, oil, temperature, and performance.

When these indicators go out of range, the sensor alerts the team via computers.

In fact, smart sensors also alert when a machine’s working pattern changes or is about to fail. So this allows teams to schedule the maintenance, decrease the chances of sudden machine failure, and ensure seamless productivity.

Better Decision Making With Predictive Data Analytics:

Procurement teams can predict the future using predictive data analytics and spend analytics. These predictions assist in making critical decisions for designing and executing business techniques. Continous flow and accumulation of data with IoT devices also help create more robust and relevant historical data.

Infact, joining IoT data with additional data coming from other sources can boost business growth.

For example, knowing what quantity of a product is needed can help send accurate requisitions for approvals and create error-free purchase orders.

For example, having information on what quantity of a product is being used can help in sending accurate requisitions for approvals and generating error-free purchase orders. This results in an efficient and effective purchase management system. Data collected by IoT can also be used for onboarding suppliers with supplier management solutions to get new products based on previous performance metrics and set criteria.

IoT Procurement Takeaway:

The Internet of Things has become a sensation and is impacting almost every industry. So, it will be smart to invest in this technology and unheave the existing business model.

The procurement team requires a comprehensive IoT framework consisting of machine learning, artificial intelligence, and embedded technologies. These technologies, all together, can bring holistic change and offer maximum benefit.

How can IoT Sensors Improve Productivity in Manufacturing

admin

April 18, 2022

How can IoT Sensors Improve Productivity in Manufacturing?

Internet of Things has been reaching out to almost every sector, and as a result, it is expected that the global IIoT market will reach $103.38 billion by 2026. Today IoT devices are more affordable, and many manufacturers invest in smart factory technology. One of the significant parts of smart factory technology is IoT sensors. It is essential to gather the necessary information and send data to the cloud for analysis in manufacturing. Businesses analyze data collected from the sensors to produce the most fitting solution to enhance productivity, avoid unplanned downtime, and cut manufacturing expenses.

IoT Sensors

In the Industrial Internet of Things, sensors are able to detect different types of external information and change it into data or signals that humans and machines can comprehend. Data is stored in a database which is managed either on the cloud or within the building for processing and analysis.

IoT sensors employ different types of technology like optics, infrared and thermal to catch the required information. Sensors can also collect one or many kinds of data. Sensors include measuring distance, levels, pressure, environment changes, or anomalies in production line batches.

Types of IoT Sensors

Vision Sensors:

Images are caught by a camera and processed using software to know parts’ presence, orientation, and accuracy. Adoption of vision sensors ensures product quality and consistency throughout batches. It is used chiefly on automotive, food and beverage, and general manufacturing production lines.

Proximity Sensors:

This sensor is used to calculate the distance between two objects. It is used primarily in manufacturing, where machines must know distances between products or measurements for assembly robots.

Pressure Sensors:

This sensor is used to measure the pressure of fluids or gases in an industrial environment. It is vital to maintain the correct pressure for the product quality or safety of the crew.

Temperature Sensors:

The temperature of the component indicates if they are failing or overheating. This can allow the maintenance crew to replace the fault before it results in expensive mechanical failure. Temperature sensors also monitor the ambient temperature to assure the quality of the product or food safety. Instant alert of a cooler going over-regulated temperature helps in saving the unplanned cost of food waste.

Humidity Sensors:

Balanced moisture can be an essential factor contributing to the final product quality. Monitoring the moisture guarantees that quality standards are always fulfilled.

Humidity also degrades equipment, so this sensor can inform the team if the humidity level gets disturbed. It is crucial to maintain the required moisture to enhance sensitive equipment’s life.

Level Sensors:

Level sensors alert the team if the fluid or solids level goes down. In this way, it ensures that hoppers are filled before they run out, and production time is not lost.

Acceleration and Vibration Sensors:

It is crucial to monitor the movement of equipment to know the accuracy or need for machinery maintenance—excessive vibration in the machine indicates loose bolts or worn-out bearings or motors that are about to fail.

Sound Sensors:

The pitch of some machinery also indicates whether it is operating correctly or not. By observing the machine’s pitch, the maintenance crew can be informed if the machine is running too high or low and needs repair or replacement.

With the evolvement of IIoT technology, major industrial sensor manufacturers are designing “smart” sensors. These sensors are easy to implement than analogue ones (as it requires PLCs to process and interpret data protocol). A smart sensor is able to process data within the sensor and transmit it directly back to the managing platform. This causes data transmission to be more versatile and saves bandwidth by just sending helpful information.

IoT Sensor Connectivity

IIoT deployment may involve a few or thousands of sensors monitoring and controlling a single machine or an entire production line. Sensors need to be connected to send back the data to the network and cloud software. This connection can be wireless or wired, and each of them comes with some form of pros and cons.

Many manufacturing plants opt to hardwire their IoT devices using industrial Ethernet cables. Hardwiring can guarantee a reliable connection, but the distance between sensors, I/O blocks and PLCs can limit its function. There is also the risk of damage to the cable, which comes with the cost of replacing it.

Nowadays, wireless IoT sensors are in trend as wireless are more powerful and reliable. It can cover a much larger area and distance. It is more scalable as many sensors can be deployed through this.

For instance, a single private LTE network can wirelessly connect many devices on a factory floor and provide seamless functioning.

How Are Manufacturers Improving Productivity?

Here are some examples that prove that IoT sensors play an essential role in helping manufacturers save costs and improve productivity.

Enhanced Product Quality:

In machines already connected to the cloud platforms, it is easy to store data such as temperature and pressure to track in production batches digitally. Machine vision via high-resolution cameras is another way of tracking products through a production line. Vision sensors with software can monitor product quality. Hence, this technology can reduce poor-quality products from reaching consumers who can imperil the company.

Minimize Unplanned Downtime with Predictive Maintenance:

The accessibility to real-time data and cloud-based analytics allows engineers and maintenance crew to spot inefficiencies in machinery. It is more valuable than scheduled maintenance, in which programs can analyze data collected from sensors to predict if an unplanned breakdown will occur or not. This ultimately helps technicians replace components before they fail, dodging any accident or expensive repair.

Warehouse Management and Asset Tracking:

In a smart warehouse, the IoT sensors can help track the flow of assets throughout the factory. Autonomous robots can pick or move or pack orders without human interference. Automating these tasks can allow employees to focus on other priority tasks.

Improve Procurement and Forecasting:

Sensors can also be helpful for procurement managers. Sensors installed on the production line can watch the assembly of products, help control raw materials usage, and reduce waste. It also alerts the crew when the supply goes down. Thus monitoring these essential items using sensors can reduce waste and enhance forecasting.

Product Development:

We all know that product development is one of the most critical and costly processes in manufacturing. Manufacturers can reduce the sum and make a better decision before concluding on total production.
One of the best ways is to gather data through sensors on the production floor and advanced manufacturing analytics to reduce the time consumed in the R&D process.

In fact, sensors on products can be implemented to collect data in real-life scenarios. Collecting data in real-time allows engineers to make rapid changes to get a more efficient product.

Summary

It is apparent that sensors plan a crucial role in daily operations throughout factories. Data collected using sensors can help develop a more efficient production line, machine operation, and safety.

How Bioengineering and IoT can Increase Agricultural Production

admin

April 11, 2022

How Bioengineering and IoT can Increase Agricultural Production?

Today, the Internet of Things is involved in almost everything, which means it is becoming part of every sector, industry, and daily life. Everything would be entirely in vain if we did not utilize this otherworldly technology for the betterment and improvement of agriculture.

We all know that with the increasing population, we need to improve the agriculture industry to meet the primary demand of every individual. Till now, with the help of digital technology, we have been able to solve some of the most significant issues existing in society, including food insecurity which is prevalent across the world.

Combining IoT, bioengineering, and urban farming methods can be a perfect and situation-meeting solution to enhance agricultural production in developed and undeveloped countries. Modern food production methods are already failing to meet the rising demands. So, it has become high time for all the agriculture industry leaders to focus on it.

We all know that the large-scale farming that is crucial to feeding an ever-increasing population is complex and challenging in a number of ways. On one side, agricultural production on a large scale causes a terrible effect on the planet through greenhouse gas emissions, deforestation, and the use of monoculture. On another side, a sudden pest infestation or disease can damage entire crop fields and contaminate the soils for years.

As per the research conducted by the United Nations (UN), the world’s population will exceed 9 billion by 2050, which means the requirement for 60 per cent more food production to feed everyone. The UN further states that sustainability is a critical component in handling agricultural systems and production in the future. In order to overcome the challenges and meet the needs, agricultural systems managers are trying to reach solutions by switching to technologies.

Using bioengineering, IoT, and other technologies can increase plants’ longevity, distribution, and even natural resistance against different diseases.

What are the Challenges for Agriculture Industry?

We all know the importance of the agricultural industry. The introduction of agriculture replaced the traditional nomadic cultures and planted the seeds for modern society. The earliest farmers domesticated plants and animals, and with the time being, high crop yield evolved.

In the 21st century, monoculture evolved as a low-cost, high-yield method of agriculture production. Though it provides enormous benefits, still it brings many problems like:

The adoption and practice of monoculture involve the continuous plantation of the same crop in the same field for years, reducing the soil nutrients.
Monoculture farms are also vulnerable to pests and disease, which sometimes damages the total yield in a single infestation.

However, bioengineering can help in disease eradication; at least, it has been proven in human beings. As per reports published by the University of California Riverside, scientists have been using bioengineering to stamp out deadly diseases like malaria.

For instance, when genetically modified mosquitoes were released into the wild, malaria cases decreased by 37 per cent. Similarly, bioengineering technology can be employed for agriculture production by leaders in the industry to reduce disease and eradicate pests.

Analyzing Data and Bridging Gaps with IoT:

Innovative bioengineering methods are helpful but cannot eradicate hunger without help from the agricultural IoT. Data shared by IoT can provide valuable insight into what can work and what does not in different locations worldwide. Agricultural sensor technology is already part of countries like Germany, China, and the United States, and we have gathered a lot from the information provided by IoT connected devices.

Smart agriculture devices gather data, from soil quality to moisture content, and it even monitors livestock health. Sensors attached to the equipment can monitor the health of equipment to ensure that machines are running correctly and do not face any mechanical issues. Farmers can use sensors to identify the potential source of waste like water and pesticides and can utilize the data to make informed decisions to reduce unwanted costs without negotiating with the health of the soil.

Sensors, along with IoT connected devices, can be beneficial for the agriculture industry as they can streamline the process of food from the farm to the dining table. Distribution of the produce is also essential, and IoT helps in this process also.

Reducing food insecurity in urban areas:

Food insecurity is becoming a prominent expression globally, but it can differ at different locations. Generally, city people are not in touch with nature and are deprived of natural food sources; and it is necessary to provide healthy food access to the ever-increasing urban population. With the integration of advanced technology like IoT and bioengineering, some agricultural industry leaders are looking for a solution, and urban farming has come up as an option to combat food insecurity.

For example,
San Francisco has become an example of feeding urban populations where at an indoor farm run by Plenty Inc., crops are grown in a carefully managed environment equipped with plenty of sensor monitors. To save space, crops are planted vertically rather than the traditional way that is in garden beds. Upon harvest, the bulk produced is distributed and sold locally.

Conclusion

The agriculture industry is broad and more complicated than one has imagined. However, advanced technology like bioengineering is at the vanguard of change for agricultural production. Farmers and agricultural industry leaders can utilize data from IoT to make better-informed decisions which ultimately improves crop production and potentially improves public health.

How do Cellular Networks in IIoT Promise Scalable and Secure Connections

admin

March 2, 2022

How do Cellular Networks in IIoT Promise Scalable and Secure Connections?

Internet of Things has been an exclusive topic to be discussed for the last few years. We all know about the opportunities it unveils in business. How adopting this technology can add more benefits and success to the company. But do you know how the choice of connectivity can affect the performance of the solution offered by IoT? One must consider the network, its effective range, reliability, device battery use, how much data of different types it can transfer, and the speed for implementing Industrial IoT projects.

Many connectivity options are available, and cellular connectivity is the most popular as it is a simple, scalable, and secure way to connect industrial IoT devices.

Today manufacturers all around the world are stepping ahead to make their business competent and more efficient by integrating it with IoT applications as it can boost productivity, reduce equipment downtime and enhance the efficiency of factory operations and processes.

Cellular IoT networks and devices are cheap and widespread, alluring more interest. Many organizations have already deployed cellular IoT networks to cut out the different business problems and start smart manufacturing. Organizations establish connectivity to gather data from various devices to adopt these projects successfully.

Cellular Connectivity In Industrial IoT Application

Integrating connected devices with cellular connectivity has been a long-time attempt of industrial engineers in creating autonomous manufacturing equipment and factory automation systems. Mobile technology has offered the skill for organizations to seamlessly accelerate the speed and extend the data processing capability of the systems.

Cellular connectivity empowers companies to transmit and process large amounts of information in a jiff without the requirement to send all the data through a centralized IT infrastructure. This provides organizations with an opportunity to execute strategies for machine health monitoring with the aid of wireless industrial IoT sensors without any requirement to construct their infrastructure.

Influence of Different Cellular Standards on IoT Connectivity

Cellular connections are pretty flexible with different protocols. Mostly LTE, i.e. Long Term Evolution is the prevalent worldwide approach. IIoT service providers prefer it because of its lower cost, ease of implementation quality, and less power need.

Device vendors are launching new cellular IoT devices, gateways, IoT inclined routers and new solutions that fit into IIoT solutions like IoT apps, IIoT system integration, and device analytics.

Including cellular connectivity to extend IoT solutions will boost the range of Plug and Play Sensors applications, enhancing the efficiency of Industrial IoT roll-outs and fast reconfigurations to fulfil the business requirements. Robust connectivity is necessary to achieve critical information about the health and performance of the machines; hence industries are focusing on getting the latest technologies that provide faster and more precise information. For successful deployment of cellular IoT systems, solution providers and multi-national end-user companies seek solutions that offer worldwide support (2G, 3G, and LTE). These are already becoming part of businesses scaling IoT solutions and assuring seamless deployment worldwide.

eSIM

Cellular standards impact the performance, range, ease of development, reliability, security and cost of implementation for expanding IoT in the manufacturing industry. Traditional sim cards which are utilized in cellular IoT devices are confined to single network carriers. They need a technical to manually insert or replace sim cards which cause deployment bottlenecks, especially in remote locations.

These challenges can end with the latest eSIM platforms, which have a non-removable chip that can download the carrier profile over the air and permit multiple telecom providers to programme in advance so that the device can choose the best connection.

IoT devices with eSIM have a single sim card with a cellular module that offers the ease of deploying anywhere globally and guarantee dependable connectivity due to their ability to switch carriers without any need for physical human interaction.

These devices are a blessing for monitoring machines in complex and remote areas and help to slay logistical challenges during movement. All these features provide speedier scalability for IoT applications.

Benefits of Cellular IoT Connectivity

Today, cellular connectivity is attaining unexpected height for implementing integrated machine-to-machine communications that facilitate wireless condition monitoring of industrial assets. This is all possible because Cellular IoT connectivity enables high network dependability. The cellular IoT devices data (transfers with high data rates) are not disturbed by bad weather conditions. The distance between the device and the base location does not impact it compared to other wireless communication options. This shows that cellular connectivity provides the best coverage and the capacity to avoid overload problems. It also offers greater freedom of mobility that supports obtaining connectivity even in complex environments where equipment is not stationary.

Mobile technology impressively transforms Industrial IoT applications and solutions because of its advantages. In IIoT, various manufacturing verticals will keep using cellular IoT devices as the most successful implementation.

Digitalization

Digitalization of manufacturing operations guarantees lowered downtime & enhanced productivity, and cellular networks are essential to accomplish it. Cellular networks provide organizations with the opportunity to use these technologies to speed their Industry 4.0 journey potentially.

The availability of cellular network coverage eases the work by making it prompt and cost-saving for manufacturers to keep a watch on industrial assets even in remote areas. The incoming of 5G provides a more robust opportunity for organizations to leverage data through faster connections, ultimately improving capacity to handle real-time information to discover the most fitting and highly potential Industrial IoT.

Maximizing AI and IoT Business Value While Protecting Customers privacy

admin

February 21, 2022

How to Maximize AI and IoT Business Value While Protecting Customers Privacy

Today more than 9 billion devices potentially utilize Artificial Intelligence (AI) and the Internet of Things (IoT). No enterprise or individual is untouched by the influence of these latest technologies. They are using it through their smartphones, applications, cloud services, sensors, RFID systems, and various other means. These unparalleled opportunities are skyrocketing business operations and constructing deep customer relations.

But all these benefits come with a situation that every business needs to address appropriately. Many business owners and executives prioritize customer privacy and security while utilizing exceptional opportunities of AI and IoT, and all this is possible through CIAM.

CIAM to Maximize Business Value

CIAM, Customer Identity and Access Management, is a sub-category of identity and Access Management (IAM)that aids in improving customer experience and security concurrently. CIAM integrates digital identity-based authorization with authentication to customer-facing applications. Businesses can opt for CIAM solutions either on-premises or as-a-service. They can cause it through interconnected identity APIs on web services and applications.

As per IBM Security, today, about 80 per cent of organizations are facing security breaches, and they stated that cybercriminals targeted customers’ personally identifiable information that is PII.

As per them, a compromised security breach on average costs approx $150 per customer. Thus, management is an essential security measure every business should add. There are four primary things CIAM does when implemented.

It facilitates customized authentication mechanisms for enterprises and their customers.
It improves the customer registration and login experience by reducing the risk of a data breach or account hacking.
It generates scalability regardless of the customer headcounts.
It Impacts AI and IoT amid expanding Security and Privacy Demand.

We all know that AI and IoT will be flowering in the coming decades. Businesses are trying to utilize these technologies properly to improve business techniques and methods. IoT promises to produce new mechanisms that can streamline business operations and boost customer experiences with the increasing business workload and hiking competition.

On the other side, AI is carrying a revolutionary change in time-consuming and tiring manual jobs by automating systems. It can also extract insight from granular customer data to enhance business efficiencies and create better customer engagement opportunities.

AI and IoT are the leading technologies that support modern business. However, these come with the concern of data privacy and security. Collecting and processing data using modern technologies might compromise customers’ privacy. But organizations that respect customers loyalty and trust use essential privacy protection techniques. Organizations can raise their standards and services from their competitors by benefiting from CIAM and privacy maintaining management systems.

Security and Privacy Challenges arising from AI and IoT:

Most progressive organizations need AI and IoT to expand and know the growing sensitivity of customer data privacy. Regardless, the dawn of AI and IoT drags in security and privacy challenges jiggling user trust.

IoT Security Challenges

With increasing IoT system connection, evolution, and expansion across any industry or organization, it becomes challenging to keep data and communication safe and secure. IoT is still in the blooming stage, along with its communication protocols. Business executives and customers find it challenging and problem-alluring to benefit IoT systems due to internet-based software attacks, authentification flaws, network-driven attacks and hardware attacks.

Connecting Data in AI and IoT systems

Organizations struggle with growing pains in adopting these technologies because of privacy reasons. IoT devices are implemented in sensitive areas like the healthcare, pharmaceutical and finance industries. Without securing the authentication of employees and customers, the entire system data and privacy could face significant risk. Thus, it is essential to ensure the security of an organization’s and its customer’s data while churning the best AI and IoT.

Inadequate User Experience with Conventional Security Tools

In IoT, user experience is highly non-interactive. Customizing IoT systems for excellent user experience without compromising security and privacy is complicated in legacy systems. The authentification mechanism in IoT devices does not provide a user experience. Lack of user experience affects the business value of AI and IoT-driven systems.

Disadvantages of Legacy Security Measures

An organization that use AI and IoT systems deploy security and privacy controls to mitigate the challenges mentioned above. There is no doubt that these legacy security measures can reduce data breaches, identity leaks and control access management but at the cost of lowering the potential of AI and IoT. They might mislead that adopting AI and IoT technologies are unsafe or risky.

As per a report, senior executives and managers are concerned that AI and IoT might expose customers and employees to strict privacy, ultimately reducing the potential of AI and IoT usage in the future. These hurdles will sustain for a long time until organizations switch from legacy security approaches to CIAM solutions.

Advance CIAM solutions enables AI and IoT integration without hammering user privacy, security, and user experience. Organizations can leverage CIAM solutions that align with the business guidelines yet provide cybersecurity and privacy protection while customers interact with IoT devices or AI-enabled systems.

Integrating CIAM Enabled AI and IoT

Security researchers and CIAM providers support customer identity and access control solutions while dealing with IoT and AI-driven systems. Such effective solutions help manage the challenges of governing, managing, safeguarding customers’ access to sensitive data. CIAM is necessary to balance AI and IoT-driven organizations with customer identity management. CIAM solutions double-shield the security by facilitating MFA, SSO, social identity-based login, PIN, etc.

CIAM solutions also have classically-minted IoT authentication methods and AI-based intelligent login procedures that enhance user experience that help in strong protection upon account takeover and data privacy without negotiating with user experience.

CIAM can provide tracking user consent, understanding and logging activities and helps in recognizing user preference at a granular level. It prioritizes customer privacy and security while generating a rich user experience in these static devices. It also aids businesses in deploying successful AI and IoT solutions.

Aligning Solutions

Organizations can manage or align CIAM solutions as per business policies. It can even analyze data extracted by AI or IoT sensors using predictive models while clearing privacy hurdles. It also emphasizes eliminating and removing data on demand when any customer ends the service or relationship.

Here are some essential points that the CIAM solution displays at an enterprise level.

Adopting Solutions

Today organizations are embracing CIAM solutions. These CIAM solutions offer different authentication techniques and measures like two-factor authentication, social media identity as login, biometrics login etc. Furthermore, the CIAM solutions also provide the service of employee identity and access management (IAM).

Maturing

The maturity of CIAM solutions assists organizations in deploying AI and IoT systems without concern. Advance CIAM solutions have direct measures addressing AI and IoT-specific security concerns. Reports state that organizations using advanced and matured CIAM solutions are 33 per cent more potential to implement plans in deploying AI and IoT than organizations with low CIAM maturity.

Advancing

Advanced CIAM solutions allow organizations to overcome AI and IoT-related security challenges. As per reports, organizations having mature CIAM solutions are 26 per cent to 46 per cent more feasible to overwhelm AI and IoT-driven security issues.

Deploying

Deploying mature CIAM solutions supports the security teams to draft a solid plan. Organizations with progressive CIAM solutions are 20-52 per cent more potential to boost business value without impacting the user experience or privacy. Besides this, these solutions also minimize customer data breaches and produce insights from the granular data they gather.

Artificial Intelligence has different building blocks like Machine Learning (ML) and Natural Language Processing (NLP). Although AI can generate a range of insights into several CIAM processes, the start of CIAM systems requires organizational knowledge, policy setup and human interaction to gain value from AI.

IoT sensors and devices capture an immense amount of data, including device location and even status. Such data are primarily stored in cloud-based servers. The scalability and distributed data from IoT devices increase data mishandling. Such data breaches also cause corrupt authentication. Organizations can use CIAM solutions for better authentication, regulatory data collection, and compliance in such cases.

Running on Automation

Today, almost all industries have adopted automation, which means most things operate through AI and IoT. Business owners and executives interested in utilizing IoT and AI in full potential take precautions about customer data security and privacy without negotiating with user experience. Organizations must employ mature Customer Identity and Access Management solutions to integrate smart authentication and authorization. These CIAM systems offer omnichannel interactions and authentication along with track while managing granular user consent, preferences, and activities.

Prompt Softech Ranked One of the Top IoT Development Companies in 2022

admin

February 8, 2022

Prompt Softech Ranked One of the Top IoT Development Companies in 2022

Techreviewer has recognized Prompt Softech for their astonishing performance and rewarded them all with a spot Techreviewers list of top IoT Developers. Prompt Softech offers many services all by experts. They have grown their customer base tremendously and have a very trusted brand.

All companies in a specific market are compared to one another and they compete on several criteria that Techreviewer ranks them on. Only the best performers can receive a spot on the list. They rank on:

Services provided
Brand visibility and reputation
Customer reviews
Awards offered
Company clients
Case studies
Social media activity

Prompt Softech has proven itself in every criterion there is. Not only do they have a reputable brand that offers IoT development services done by only the most expert workers, but they also are consistently on social media, have won awards, have a plethora of clients, and have multiple case studies. The company is the epitome of an industry leader. Techreviewer expects to see great things from them in the future.

About Techreviewer

Techreviewer researches top-performing companies in IT-specific markets and creates lists of the best performers. Their main goal is to provide consumers with a way to find reputable companies that offer outstanding work so that consumers will never have to worry about getting ripped off. All companies on their lists pass through their standards and outperform competitors.

admin

February 3, 2022

How to Prevent Data Lake from Turning into a Data Swamp?

IoT devices drive in many opportunities to gather more data than ever before. However, the challenge has changed; it is not about ways to get data but how to store an immense amount of data once it’s gathered. This is where data lakes come in the role. To clarify, a data lake is not just about a cheaper way to store data, but when it is appropriately crafted, data lakes act as a centralized source of truth that offers team members valuable flexibility to examine information that influences business decisions. This is only possible when we potentially utilize data lake practices. Raw data is like crude oil, requiring a thorough refinement process to distil more valuable products like gasoline. In the same way, raw data requires complex processing to get the most beneficial and business-rich insights to take action and measure outcomes.

With the increase in the volume of available data and the variety of its sources continuing to grow, many companies find themselves sitting on the data equivalent of a crude oil reservoir with no feasible way to extract the actual market worth. Traditional data warehouses are like gas stations; data lakes are oil refineries.

Data warehouses are becoming insufficient for managing the flooding business’s raw data. They need the information to be pre-processed like gasoline. Data lakes are the one that allows for the storage of both structured or unstructured data coming from different sources, such as business and mobile applications, IoT devices, social media etc.

Any idea? What does a well-maintained data lake look like? What is the best possible way to lead to implementation, and how do they impact the bottom line?

Explaining Data Lakes: How they Transform business

Data lakes are centralized storage entities to store any information mined to get actionable insights. These contain structured, unstructured, and other information from relational databases like text files, reports, videos, etc. A well-maintained data lake has real prospects to change the outlook of the business by offering a singular source for the company’s data regardless of its form and allowing business analysts and data science teams to extract information in a scalable and sustainable way.

Data lakes are generally designed in a cloud-hosted environment like Microsoft Azure, Amazon Web Services or Google Cloud Platform. The vision offers compelling data practices that offer noticeable financial edges. These practices are approximately twenty times cheaper to access, store and analyze in a data lake rather than employing a traditional data warehouse.

One of the reasons behind the domination of data lakes is the design structure or schema, which does not require to be written until after the data has been loaded. Regardless of the data’s format, the data remains as it is entered and does not separate into silos for different data sources. This automatically decreases the overall time for insight into an organization’s analytics. It also offers enhanced speed while accessing quality data that helps to inform business-critical activities. Advantages provided by data lakes like scalable architecture, cheaper storage and high-performance computing power allows companies to divert their shift from data collection to data processing in real-time.

Rather than investing hours excavating scattered deposits, it provides one source to extract from that ultimately decreases dependency on human resources, which could be utilized to create stronger partnerships across teams. A data lakes give time to your data scientists to explore potential business-critical insights that could advise new business models in the future.

Best Practices from the Experts

There are challenges in the data lakes process; it acts like a stagnant pool of water-polluting over time if it is not held to the correct standards. It becomes challenging to maintain and susceptible to flooding from insufficient data and poor design.

What to do to set up a supreme system for business transformation and growth?

Here we recommend the following actions to prevent your data lake from turning into a swamp.

Set Standards From the Start

A dynamic structure is the backbone of a healthy data lake. This means creating scalable and automated pipelines, using cloud resources for optimization, and monitoring connections and system performance. Initiate by making intentional data-design decisions during project planning. Mention standards and practices and ensure they are followed at each step in the implementation process. Meanwhile, allow your ecosystem to manage edge cases and the possibility for new data sources. Don’t forget; it is all about freeing up your data scientists from tending to an overtaxed data system so that they can shift their focus on other priority things.

Sustain Flexibility for Transformative Benefits

A healthy data lake exists in an environment that can manage dynamic inputs. This isn’t just about varying sources, sizes and types of data and how it is downed into storage.

For instance, creating an event-driven pipeline facilitates automation that offers source flexibility in file delivery schedules. Setting up a channel with trigger events for automation, based on when a file hits a storage location, eases concerns whenever the files come in. It is necessary to support the data science team’s fluidity around rapid testing, failing and learning to refine the analytics that empowers the company’s vital strategic endeavours, eventually driving unique, innovative opportunities.

Develop the System, Not the Processes

Most people have a misconception that problem-specific solutions may seem faster initially. One of the best things about data lakes is that they’re not connected or centralized around any one source. Hyper-specialized solutions for individual data sources restrict themselves to implementing change and need error management. Besides this, when a particular process is introduced, it doesn’t add value to the system as a whole as it cannot be utilized anywhere else.

Designing a data lake with modular processes and source-independent channels saves time in the long run by facilitating faster development time and streamlining the latest feature implementations.

Handle Standard Inventory to Find Opportunities

Event-driven pipelines are the best option for cloud automation, but the tradeoff demands post-event monitoring to comprehend what files are received and by whom and on which dates, etc.

One best way to monitor as well as share this information is to establish a summary dashboard of data reports from different sources. Adding alerting mechanisms for processing errors produces a notification when part of the data lake is not correctly functioning as expected. It even ensures that errors and exceptions are detected on time. When an immense amount of data is flooding, it becomes essential to track and handle it in the best possible way.

Right inventory initiatives create stable environments where data scientists feel supported in discovering additional metrics opportunities that can help make more robust business decisions in the future.

Revolutionize Business Intelligence

Data lake revolutionizes business intelligence by chartering a path for team members to peer clean data sources promptly and in the most effective way. A pristine data lake accelerates decision-making, removes struggle, and enhances business model ingenuity. So, we can conclude that prohibiting data lake getting muddied is necessary to get the optimal outcome. One must follow a few data lake practices that can reduce future headaches and keep your data streamlined and humming.

Why Private cloud is the first choice of Businesses when it comes to IoT

admin

January 11, 2022

Why Private Cloud is the First Choice of Businesses When it Comes to IoT?

Today, terms like smart refrigerator or smart town or home security system and many other words are familiar with everyone. Not just this, people even know that how these devices fit into the Internet of Things (IoT). Besides changing the lives of individuals, IoT has become a boon for businesses as it helps in making it more effective and efficient. Through automated sensors attached to packages or vehicles to inform the organization about the supply chain status, devices to monitor and track business development processes, or create more customer engagement, IoT provides every possible solution to help businesses grow and succeed.

Another business-outlook changing tool that is helpful for devices within the IoT is the cloud. It is an interconnected network of servers that store data for individuals and businesses alike. Individuals opt cloud for storing files on iCloud instead of saving on phone or computer, while companies use the cloud for business processes, mainly to store data from IoT systems.

Do you know the Difference Between a Private and Public Cloud?

Well, it’s not mandatory to have a cloud for IoT systems because the operations of IoT systems can even take place locally rather than on the cloud through a connection to the internet. Yet, using the cloud for IoT systems within your business might help in reducing costs and scale that often accompany cloud use.

Organizations can opt for either a private cloud, a public cloud, or a hybrid cloud for cloud use. It is necessary to know the pros and cons of all three cloud services. One of the most popular types of cloud service, especially for individual use, is the public cloud. In this, a third-party service provider owns this cloud but will not be responsible for any maintenance or infrastructure. Google Drive, Amazon Web Services, and iCloud are some examples of public clouds.

In a private cloud, the stored data and information are only available and can be accessed by the organization for which it was developed. This implies that Private Clouds offer more control over their data to the organization. Private clouds are the first preference of organizations like financial institutions or government institutions because they deal with sensitive information.

The third option is the hybrid cloud. These are a blend of private and public clouds. This combination empowers organizations to customize which cloud type to use for better results.

Benefits offered by Private Cloud for businesses:

There are many reasons for which a company may opt to work with a private cloud:

Protects Company Data:

Companies that have adopted IoT systems and devices experience immense data flow. This data helps churn valuable insights that can help the business to improve and grow. It is now apparent why organizations are concerned with data security. Private clouds have dedicated service providers that enable organizations to control data firmly. In this, the organization is responsible for installing and maintaining the cloud infrastructure so they can manage their valuable data in a better way.

Improves Productivity and Efficiency:

One of the main reasons for opting for private cloud over others is its features that promise efficiency and productivity in a business. An organization can prosper when they are concerned more about improving productivity among their employees.

Choosing a private cloud can improve a company’s efficiency by:

Facilitating a business’s data usage and storage
Enabling communication among co-workers more comfortable and faster
Providing more flexibility and customization that allows systems to comply with special regulations or standards within the company or industry
Offering employees better file-sharing capabilities

Additional Benefits:

There are several other benefits organizations may enjoy with private cloud usage, such as:

The Expenditure. While it may seem as if a public cloud may be the more affordable option in many cases; however as per a report shared in 2019 concluded from 451 Research reveals that private cloud computing, mainly if it runs on a reliable single-tenant VMware are found to be less costly for some businesses.
More Efficient Decision: A company is dependent on data and wishes to store data that can help in making significant business decisions on a more local level instead of sending data to a centralized location for processing purposes and proper analysis.
Less Latency: On-premises management of systems and devices can promise faster data connectivity between servers and devices, lowering latency and permitting businesses to operate promptly.
Proper Integration With Existing IoT Systems: An organization can easily integrate IoT systems with new systems more efficiently if they can physically access their data management system.

Conclusion:

In this fast-changing world, it has become mandatory to reevaluate the decisions made for the business benefit. Cloud computing is proliferating, so it has become obligatory to consider the future of cloud services for media, individuals, and businesses.

While considering cloud services for your business, be sure about the requirements of your business and opt for the most fitting cloud. Are you looking for cloud migration? Contact us to get the most reliable and result-focused services.

← Newer Posts

Older Posts →