Enterprise Iot
Article | May 11, 2023
The Industrial Internet of Things changes our view on the classic concept of production today. The largest manufacturing companies are eager to reach for technological solutions that streamline and increase the efficiency of production processes. With the development of subsequent technologies, interest in the concept of industry 4.0 increases, and thus, the number of connected devices. To meet the demand for infrastructure with adequate capacity and speed it was necessary to evolve towards the next generation of networks – 5G.
Read More
Industrial IoT, IoT Security
Article | July 12, 2023
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.
Read More
Security, IoT Security
Article | July 13, 2023
Manufacturing industry or the Industrial Internet of Things has been one of the driving verticals for development of 5G technologies. Wide 5G deployement for Industrial IoT has long been in the pipeline but we might expect it to be a reality very soon.
The true success of 5G depends on the verticals as trends suggest that that Industrial IoT alone will triple the number of needed base stations globally. And many verticals will need efficient wireless connectivity to become successful. 5G has features that are specifically designed to address the needs of vertical sectors, such as network slicing and URLLC. The ultra-reliable low latency communications and massive machine type communications required by the IIoT will soon be realized.
Table of Contents:
How Will 5G Impact Industrial IoT?
5G Accelerations for IIoT
Industrial 5G
How Will 5G Benefit Industrial IoT?
IoT is a B2B application and users just want to get actionable data from their sensors and not worry about whether it’s old data or unreliable data. I think 5G changes this dynamic significantly over the long term by standardizing and simplifying the experience and interactions, and possibly engaging more of the industry to help solve IoT’s problems but also improve the total experience.
- Anshel Sag, analyst at Moor Insights & Strategy
• Data-Transfer Speeds
Any IoT is said to be commercially successful depending on how fast it can set up communications with other IoT devices, software based websites or applications, phones, and tablets. 5G promises exactly all of this with significant increase in transfer speeds.
5G is 10x faster than its LTE counterparts and allows IoT devices to communicate and share data faster than ever. All IoT devices will benefit from the faster speed of 5G with reduced lag and improved sending and receiving of data and notifications between connected devices.
• Greater Network Reliability
5G networks also offer more reliable and stable connection which is extremely important for any IoT including devices like locks, security cameras and monitoring systems that depend on real-time updates.
With reliable connectivity consumers will be the greater beneficiary.
It is however, imperative for manufactures to trust and invest in 5G compatible devices to reap the benefits of high-speed connectivity, very low latency, and a greater coverage that will arrive with the next generation network.
READ MORE:How Will the Emergence of 5G Affect Federated Learning?
5G Accelerations for IIoT
• Diversity in Industrial IoT
The opportunities that industrial IoT bring with is varied and its used cases span the spectrum from indoor to outdoor, less demanding to mission-critical, data rate from dozens of bps to gbps, device motion from fixed to mobility, and power source from button battery to high voltage.
Predictive maintenance, smart metering, asset tracking, and fleet management are some of the commonly known opportunities for IIoT, which be extended further by 5G through continued diversity and expansion.
• 5G Inspires Untapped Frontiers
Industrial IoT application areas such as mobile robot control in production automation and autonomous vehicles in open pit mining require wide mobility, low latency and mission-critical reliability. They rely on wireless access at 50ms to 1ms latency and service reliability from 5 nines to 6 nines.
Though 4G/LTE has attempted to address these areas of IIoT application it has failed due to unsatisfactory performance. With ultra-reliable and low latency connection, 5G will take industrial IoT to unconquered spaces.
• Managing the Enterprise 5G Network
Typically, enterprise IT is responding to the business demand from Operational Technology (OT) and mandates security, integration, visibility, control, and compatibility. In this scenario, 5G is not about “what,” but about “how”. IT needs to consider the right approach to bring 5G to the enterprise and decide whether to co-manage with the service provider (SP) or self-manage. The experience of IT in managing Industrial Ethernet and Wi-Fi may not hold when it comes to 5G. IT will likely require OT’s partnership to address complexity, security, integration, and other new challenges that 5G presents.
Industrial 5G
The potential for industrial 5G huge as it enables whole new business models.
Industrial IoT has a core requirement of the ability to connect sensors, devices, software applications, production process, workers and consumers. The connectivity requires to be seamless vertical and horizontal integrations of all layers of automation pyramids that increases operational efficiency of the plant floor and the supply chain by optimal use of data, information and analytics. This can be improved by five key elements:
• Improved Connectivity
• Availability
• Low Latency
• Flexibility
• Speed
Industrial 5G will impact these areas of the manufacturing industry to guide the success of Industrial IoT.
Industrial 5G will play a key role in helping industrial users achieve the goals of Industrial IoT. 5G offers wireless communications services with reduced latency, increased connection density, and improved flexibility compared to the current 4G generation. 5G technology has a theoretical downlink peak speed of 20 Gbps (gigabits per second), which is about 20 times faster than the current generation.
The key is to start building IoT devices with broadly adopted operating systems, built-in security all the way down to the silicon, verifiable and updatable firmware, and mainstream application development tooling.
- Anshel Sag, analyst at Moor Insights & Strategy
The push and pull in achieving 5G success in IoT will be there until technology providers and end users work together to set up a consensus on standardization. The success will also depend on best-of-breed approach allowing the introduction of new technology over the lifecycle. Software and system integration will also be important attributes to a successful 5G deployment.
READ MORE:How Will IoT Revolutionize Pharmaceutical Manufacturing?
Read More
Enterprise Iot
Article | July 6, 2022
The Internet of Things (IoT) and Industrial Internet of Things (IIoT) markets have experienced explosive growth as a result of the digital industrial revolution that followed the COVID-19 epidemic. To fully benefit, however, organizations have had to handle security concerns associated with these revolutionary technologies. Therefore, finding the correct security strategy is crucial for any organization because of the increasing dependency on IoT and IIoT to manage essential business systems.
IoT and IIoT can be implemented quickly, but they come with inherent vulnerabilities. This risks businesses from cyberthreats such as device theft, spoofing, denial of service attacks, and data breaches or siphoning. Attacks of this nature adversely affect an organization's operations, finances, safety, and reputation.
Many IoT and IIoT devices have passwords hard-coded into their firmware, making it challenging to patch or update security, which is a significant problem. Even when security is deployed on a device, it can usually be bypassed by taking advantage of a variety of known weaknesses. As a result, IT teams may find it challenging to identify an occurrence when IoT or IIoT devices are compromised before affecting systems and data.
Mitigating IoT and IIoT Security Risks
Separate IIoT and wireless devices from the SCADA or ICS network. Micro-segmentation allows only authorized device connectivity in certain circumstances.
Control network access by monitoring what connects and validating each device's security.
Demand visibility across all enterprise security networks and devices. This should be centralized so all devices, networks, risks, traffic, and policies can be handled in real-time across production and IT environments.
Use an intrusion protection system (IPS) to identify threats and patch IoT and IIoT devices virtually. Counter unexpected attacks with active protection and deception techniques.
It's crucial to check that security solutions can grow automatically to meet business requirements before using them. This entails responding to network changes, foreseeing risks and controlling them proactively, and offering real-time threat intelligence.
Read More