Industrial IoT, IoT Security
Article | July 12, 2023
IoT has undeniably become the massive growth propellant for modern-day business. Enterprises employ intelligent systems to improve production in factories, and reduce costs, build industrial automation systems to replace human assignments, monitor and reduce energy; and develop autonomous transportation to enhance driver safety.
Inside these embedded systems are sensors that rapidly transmit data that must be immediately captured, processed, and acted upon.
Traditional embedded database solutions don't understand and meet the complex needs of IoT devices when it comes to processing and managing data. IoT edge database solutions that can understand the constant data stream from sensors enable devices to make crucial decisions in milliseconds.
Real-time Edge Data Processing
Enterprisers and business owners prefer scalable edge data management solutions to deploy hundreds of IoT devices so that each device can manage, collect, and analyze the massive amounts of data these IoT sensors produce without losing performance.
These devices must capture and store critical information so that the IoT node can make independent decisions and trigger appropriate reactions.
Database queries allow device apps to get the information they need to make intelligent decisions in real-time, quickly and without wasting time. To be successful in the IoT, you need the right data management software and the ability to quickly collect and connect device data rapidly to get low latency.
IoT Data Processing and Management
Standard data management solutions do not fully address the complexity of architecting software for IoT data processing. Despite being the primary data source, sensors are often constrained by their limitations and fail to provide sophisticated analysis.
The focus of IoT data analysis and management is to harvest real-time information and make sense of it quickly.
A good solution uses technologies that many developers are already familiar with, like SQL, to solve the new problem of analyzing IoT sensors directly on edge devices.
Conclusion
While building a device application, at every stage, developers must make tough calls to select the best data management and database software to launch their edge-centric IoT systems. Such costly decisions consume significant development and validation time as well.
Using existing IoT data management platforms is a better way to deal with scaling, security, and the weight of data. Businesses can set up, connect, and grow their IoT infrastructure with these platforms. Organizations don't have to build their own IoT infrastructure from scratch. Instead, they can use IoT platforms that give them access to IoT devices, cloud infrastructure, and networks worldwide. Small and medium-sized businesses may find this method saves money.
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IoT Security
Article | June 27, 2023
The biggest IoT trends are likely to manifest in 2022 and beyond.
1. BLOCKCHAIN
The term blockchain is a new concept and is known as a single registry; agreed and distributed in several nodes of a network that will continue in force in the coming years in various activities.
2. MOBILE COMMERCE OR M-COMMERCE
It is part of electronic commerce that is carried out exclusively through mobile devices such as smartphones or tablets. The processes will be specialized. With mobile commerce comes the need for device management. Device management is a vital step to ensure security is promptly implemented.Mobile Device Management (MDM) solutions, also known as MDM, offer brilliant benefits across all areas.
3. TELEWORK AND DISTANCE EDUCATION
Academic and work activities that are carried out remotely, preferably from home, will continue to be applied in a fixed or hybrid way. A smart device(s) can be used from a remote location and therefore enable workers to more effectively manage time.
4. ROBOTIC PROCESSING AUTOMATIZATION
It is all technology-oriented to the use of software, with the aim of reducing human intervention in the use of computer applications, especially in repetitive tasks. This reduces the risk of human error and will also cut down management costs.
5. ARTIFICIAL INTELLIGENCE
It is the combination of algorithms proposed with the purpose of creating machines that have the same capabilities as humans, with the aim of doing a variety of tasks. If we decide to develop an Artificial Intelligence that has greater intelligence, responsibility and scalability, we can make the most of learning algorithms and interpretation systems. In this way, we are able to create value more quickly and with a greater business impact.
It is essential to have new techniques that achieve smarter AI solutions, that require less data, with greater ethical responsibility and more resilience.”
Gartner
6. DIGITAL TRANSFORMATION
Digital transformation is the change associated with the application of digital technologies in all aspects of human society, and especially in organizations. Accelerating business digital transformation requires entrepreneurs to step back and re-evaluate their plans. It’s about aligning the customer experience strategy with coordinated and detailed digitization plans of what needs to be done, by whom and when. To do this, having precisely identified the customer journey of your digital customer allows you a complete approach, for which tools such as the customer journey map is key.
7. FINTECH
It is a nascent industry in which companies use technology to provide financial services in an efficient, agile, comfortable and reliable way. They aim to expand bank penetration.
8. DATA ANALYSIS
It is the process by which raw data is analyzed in order to answer questions and reach practical conclusions that support an organization’s decision-making. Using predictive models and AI tools, we can run simulations that are based on real scenarios and information. Thanks to this, we obtain data on contexts that would be difficult, very expensive or impossible to test in physical environments. Big data is big money.
9. SOFTWARE DEVELOPMENT
Software development is generally considered part of the agile family of approaches, and is often used in combination with one or more other methods. Softwareon a smart device can also be upgraded to include better connectivity. In addition to its development, there is likely to be more outsourcing also.
10. ADVANCED MANUFACTURING OR INDUSTRY 4.0
It refers to a new business model in which the interconnection of integrated ICT systems both with each other and with the internet is key. The adoption of Industry 4.0 technologies and the training of personnel will be the greatest opportunities that industries, companies and governments will have in the next decade. Therefore, 2022 represents the next step to embrace technological transformation as an indispensable element for competitiveness, resilience, and development.
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Enterprise Iot
Article | May 11, 2023
Three out of four IoT projects are considered a failure, according to Cisco. This is troubling but even more so when Cisco also found 61 per cent of companies say they believe they’ve barely begun to scratch the surface of IoT can do for their business? Businesses believe in the long-term value offered by integrating IoT into their business plan, however, they lack the knowledge of what is required to ensure the success of such a complex project. By studying past failed projects, technology leaders can gain a better understanding of why they failed and what they can do differently when evaluating and undertaking new IoT initiatives.
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Software and Tools
Article | September 23, 2022
For businesses to grow, they must be armed with the right technology and implement the right strategies to get a high return on their investments. With an IoT strategy, you can successfully make sense of the high volumes of data generated. IoT is about having devices with sensors communicate with other devices over the internet and share real-time data or parameters to maintain healthy system processes. Sharing and transferring data in real time over the cloud creates a lot of data that needs to be carefully managed.
Not having a streamlined method to control and manage the volume of data to capture, send, transmit, and receive over the cloud poses many space constraints as the data piles up quickly. Furthermore, deciding what data to keep and what to discard, how long you need the data, and for what purpose are all critical. Some standard IoT devices include sensors, lights, alarms, and cameras that a smartphone can control.
Learn about the importance of data management in establishing an IoT framework below.
The Top Reason for Establishing an IoT Framework Needs Data Management
Learning from past data trends to make future decisions in an IoT framework is critical. Data management acts as a layer between the IoT devices generating the data and the software accessing the data for analysis and services. It helps review, analyze, and navigate the massive amounts of structured and unstructured data. Defining which actions trigger responses to create data in your process is necessary to monitor your product and services and to keep your customers satisfied. In an IoT framework, managing the large amounts of data that are generated and collected means learning from the past and predicting what will happen in the future.
Why is Data Management for the IoT Framework Crucial for Medium and Large Enterprises?
Creating a better product is essential to add more value to your product offerings and avoid recalls, keeping your brand reputation at stake. The more data, the deeper the analysis, and the more refined the product, the greater the need to manage large amounts of data efficiently.
The future of IoT data management is promising when it comes to improving all aspects of your business processes, mainly controlling the automation and manufacturing processes and software triggers. Check out the in-depth benefits of data management in IoT.
Data management in IoT helps conduct a field test of your IoT products before deployment.
Improve the uptime of your business production lines and equipment.
Perform seamless decision-making for planning, scheduling, and execution systems to meet the changing customer and market demands using accurate and current data.
Data management helps efficiently deploy IoT solutions such as enterprise resource planning (ERP), enterprise asset management (EAM), and manufacturing execution systems (MES) in manufacturing businesses.
Data management helps remote monitoring of automation systems and robotic systems in industrial IoT needs current data and management.
Improve production flexibility and responsiveness by welcoming smart manufacturing using IoT data management.
When it comes to the data management of IoT devices, different types of data management systems take care of structured and unstructured data.
8 Data Management Systems for Your Enterprise IoT Devices
IoT device management means registering, organizing, monitoring, and remotely managing IoT-connected devices at scale. Various cloud architectures with different data management systems help with efficient IoT device management. In addition, equipment data, sub-meter data, and environmental data help track the performance of your IoT devices through IoT data collection. Let's find out how data management systems for IoT devices would help develop an IoT strategy for your large enterprise.
IoT gateway device management involves many steps in keeping your operations healthy and maximizing uptime. These are provisioning, authentication, configuration, control, monitoring, diagnostics, software updates, and maintenance. In addition, data management systems aim to make data available for analysis in the long term. The different data management systems are as under:
Querying
Production
Collection
Aggression/Fusion
Delivery
Pre-processing
Storage, updating and archiving
Processing or analysis.
These data management systems capture, organize, store, retrieve, and analyze data when required. Sorting out the data management in IoT will initiate your internet of things database scalability. An IoT data lifecycle is built around the data management systems in the data flow, which acts as guidelines or checkpoints for a smooth data flow across your IoT platform. Let us unfold them below.
Seven Guidelines for Cost-Effective IoT Data Management
• Querying: Accessing and retrieving data for temporary monitoring. For example, you could ask IoT devices or sensors for data in real time to learn more about trends and patterns.
• Production: Sensing and transferring data by the "things" or IoT devices in an IoT framework is the data production phase. Pushing the data to the cloud network and the IoT database servers and reporting it to the interested parties. This rich data has different formats such as audio, video, or image content, and is time-stamped and geo-stamped.
• Collection: Collecting and retrieving data for a predefined time interval and sharing it with the governing components within the gateways is a part of the collection. Filtering out valuable data and compressing it accordingly helps seamless data transfer. It is also a part of data collection.
• Aggression or fusion: Part of the aggression is real-time data transmission across the network to increase the rate of data streaming over the limited bandwidth. It pulls together information from different points of contact and reduces the amount of information that needs to be stored and sent.
• Delivery: Collating the data from multiple touch points across the IoT framework and summing it up for the final responses is a part of the data delivery management system. Making data ready for permanent data storage is also a part of it.
• Preprocessing: Removing redundant, missing, and incomplete data and making all the data unified is a part of preprocessing. Data cleaning is also one of the preprocessing methods applied to data mining.
• Storage, Update, and Archiving: Storing data in an organized way for long-term offline usage or big-data systems is a part of the storage data management system. It can be decentralized or centralized as per the required capabilities.
• Processing or Analysis: Retrieval of stored packets of data accessed for an efficient analysis is a part of data processing or analysis in a data management system.
Whenever handling large amounts of data, an efficient data management system will solve numerous problems concerning your IoT strategy, as discussed above. Find out exactly what can keep you from implementing IoT.
5 Growth Challenges in Data Management for IoT Technology
High Initial and Ongoing costs:
Upgrading the hardware and software infrastructure that is already in place, hiring IoT-trained staff, and building an IoT infrastructure will all require upfront and ongoing costs.
Vulnerability:
Your IoT security strategy is a critical aspect of your IoT platform strategy. Multiple data points for structured and unstructured data captured, transmitted, stored, and retrieved by software come with security risks.
Procuring Quality Hardware:
Finding compatible hardware for your requirements and building an infrastructure around them can take a while regarding decision-making for scalability. In addition, hardware must remain supportive of the quick adoption of future software innovations.
Installation and Upkeep of Hardware Infrastructure:
Setting up a complex IoT strategy with the implementation of IoT data management, infrastructure, security, and more takes time and expertise. One of the other big worries is keeping the hardware infrastructure in good shape so that security can't be broken.
Constraints on Scalability and Agility:
The humungous IoT data traffic poses a severe concern for appropriate control of the data storage, retrieval, analysis, monitoring, and everything aligned with IoT data management. Also, the fact that IoT data doesn't last as long as other types of data is a risk to the way data flows and is collected.
Now, let us figure out how to implement IoT that aligns with your business objectives.
How to Implement IoT in Line with Your Business Goals
A complete analysis of your immediate and long-term business objectives is critical as it helps decide which data to keep and which to discard after how much time. Every byte of data you hold and analyze comes with a cost for storage, retrieval, and security, which can be a barrier to implementing IoT for your business. Identifying IoT data collection helps you align your IoT implementation strategy with your business objectives. Here are a few ways to address your implementation of IoT.
Consider the use cases of IoT data management as per the processes involved in your business.
Implement security protocols for encryption and restricted access as per the type of business data.
Organize training for the existing workforce and hire skilled professionals in IoT.
Understand your business's data requirements, including the data collection process.
Allow enough budget for IoT infrastructure and resources.
Consider the design and development of the product as per the customer's behavior.
Consider the impact of the environmental conditions affecting your business.
Measure real-time performance metrics using a suitable IoT sensor to streamline your process.
Take automated decisions with the help of AI once IoT sensors recognize the performance gaps.
Choose the right IoT platform that defines how you communicate and handle data.
Understand that IoT implementation is a complex process and needs commitment.
Collect only the important data and statistics for a smooth workflow and to lower the cost of putting IoT into place.
Taking into account where your storage and production lines are located, choose the best ways to gather, organize, and analyze your data.
Use cold path analytics for the long term and hot path analytics for real-time data storage.
Building infrastructure with scalability in mind will help small businesses grab market share quickly and efficiently. As a result, medium-sized enterprises will find prominence in their industry. Using data visualization in business intelligence allows for rapid optimization of your IoT devices and for controlling data management costs in the long run without negatively impacting performance. Explore more about IoT data visualization down below.
Role of Data Visualization in IoT for Business Intelligence
With IoT data visualization, you can optimize business processes by applying visualization business intelligence to get your business ready to scale. Discover the role of data visualization in your IoT strategy.
Make sense of the data you've collected or saved.
Patterns and trends should be recognized.
Check the data for inconsistencies and errors. The output should then be visualized over time for analysis and monitoring.
IoT infrastructure and devices improve performance and streamline the IoT data flow.
Analyze real-time data correlations across multiple business verticals using the IoT communication platform.
Make future decisions based on the data captured in the past.
Get actionable insights on customer behavior and
Identify the factors impacting your business.
Once you identify the gaps in business processes, you can make changes to the process and further improvise. Creating an optimized workflow and detecting errors and faults in a process early are the primary goals of data management in an IoT strategy. Tackling vulnerabilities in data security and data redundancy helps the cost-effective implementation of IoT for small businesses, opening avenues for scalability. With IoT data management, you can also optimize your products to make customers happier and get a bigger share of the market, which is great for your business's growth.
Summarizing
With secure access control, encryption, software updates, endpoint security, and communication protocols in place, the relentless power of data visualization for analyzing and monitoring the captured data has proved to be unmatched. Bringing resilience and giving a rapid boost to the scalability of your medium and large enterprises is now becoming a norm with organized IoT data management.
FAQs:
• What is the most significant benefit of IoT?
IoT helps devices or sensors report real-time data for smooth interconnected production operations. In addition, IoT keeps healthy functions throughout and minimizes the turnaround time for troubleshooting and maintenance.
• What are the three types of IoT?
Depending upon the needs from time to time, the three types of IoT include short form, medium form, and long form. The short form meets immediate needs, the medium form meets future needs, and the long form keeps the system running smoothly.
• How does data analytics help IoT?
Effective process optimization is possible by analyzing the data generated in an IoT framework. It helps boost efficiency, and connectivity, cut costs and unlock scalability.
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