Tech

What Is Fog Computing And How Does It Work?

Published

2 years ago

December 7, 2022

The decentralized infrastructure for computing in which data, storage, computing, and applications are located between the cloud and data source is known as fog computing. Fog computing brings the power and advantages of the cloud close to where data is created and acted upon, similar to edge computing. These two processes get processing and intelligence closer to data creation; hence many people use edge computing and fog computing interchangeably.

This is done to improve the results and efficiency; however, it might also be done for compliance and security reasons. The term fog originates from the metaphorical term for a cloud close to the ground. It is similar to how the fog concentrates on the network’s edge. The term fog computing is often associated with the Cisco company and is coined by the company’s product line manager. Hence, Cisco fog computing is a registered name, whereas fog computing is open to the community.

Also, fog computing plays a significant role in many fields and has various advantages. So, in this article, we have presented information about its working, benefits, advantages, and disadvantages. Please give it a read, and increase your knowledge about fog computing.

How Does Fog Computing Work?

The working of fog computing involves utilizing local devices termed fog nodes and edge devices. Firstly, raw data is collected by IoT beacons, then sent to a fog node close to the data source. Then, the collected information is filtered, analyzed locally, and sent to the cloud for long-term storage if required. There can be many edge devices like routers, cameras, switches, embedded servers, and controllers. However, any device with computing and network connectivity can act as a fog node.

Later, when edge computing resources and IoT devices collect data, it is sent to the local nodes instead of the cloud. Using fog nodes has the advantage of faster data processing when compared to sending requests back to data centers for action and analysis. And in more extensive and distributed networks, the fog nodes are placed in several key areas to analyze and access crucial information locally. Hence, there are many benefits of using a decentralized computing structure.

For example, many security systems use IoT technology to detect theft, break-ins, etc., to notify the authorities. And it could take much time if the alarm warning triggered by the IoT security system needs to be sent to the data center to be analyzed and acted on. Hence, edge computing benefits time-sensitive data like alarms, device status, and fault warnings, as this data needs to be analyzed and acted upon quickly. Cloud computing struggles to give this speed; hence fog computing is used.

Advantages Of Fog Computing

There are many benefits of using fog computing, and the main benefits come down to increasing the efficiency of an organization’s computing structure and resources. In most organizations, much critical information I generated at the network’s edge can benefit more than data analytics-related processes. The following are some of the significant benefits of using fog computing in regular use.

1. Reduction Of Bandwidth

Fog computing helps reduce the volume of data sent to the cloud. As a result, it helps to reduce bandwidth consumption and related costs. And the devices and network also perform better with the bandwidth reduction used by fog computing.

2. Improved Response Team

The latency is reduced because the initial data processing occurs near the data, and overall responsiveness is improved. Hence, it enables the data to be processed in near-real time, providing millisecond-level responsiveness.

3. Network Agnostic

Fog computing generally places compute resources mainly at the LAN level, as opposed to the device level, which is often the case with edge computing. At the same time, fog computing is also network-agnostic, which means that the network can be wired, 5G, or even Wi-Fi.

4. Real-time Data Analysis

You need real-time data to maximize the efficiency and accuracy of the insights provided by ML (Machine Learning), as it helps to do real-time data analysis. Hence, real-time data analysis is crucial in IoT security, and fog computing helps.

5. Latency

When we remove the issues of cloud latency from the data processes, it makes them more efficient, and it is offered by fog computing. Hence, the cloud can still be used for data storage, but you don’t need to depend on the cloud for processing.

6. Distribution

Fog computing does not require a centralized system; it depends on a network of connected devices. Hence, one can distribute the network across a broader range of locations than traditional computer networking. It leads to a better user experience in the distributed network.

7. Flexibility

Fog computing is flexible because it can rapidly scale up or down depending on the company’s needs. It is easy to remove, add, or move fog nodes to meet your organization’s current needs and challenges. It facilitates the ability to move computing resources when needed.

Hence, these were the benefits of using fog computing in one’s organization. It is flexible and reduces bandwidth, latency, and many more features. However, there are a few disadvantages, like any other available technology.

Disadvantages of fog computing

There are some disadvantages of fog computing too, which can hamper your company’s costs. The following are some of the main disadvantages of fog computing: –

1. Physical location

As fog computing is tied to a physical location, it undermines some of the anytime and anywhere benefits. And these benefits are associated with cloud computing; hence cloud computing can be preferred over fog computing.

2. Startup costs

Fog computing needs both edge and cloud resources to operate correctly. This means that a vast hardware cost is associated with it, hence is less preferred by companies.

3. Ambiguous concept

There is still ambiguity around the definition of fog computing, with various vendors defining it differently. However, it has been around for several years and can gain more importance and proper definition shortly

4. Potential security issues

In some cases, fog computing can be subject to security issues, such as IP (Internet Protocol) address spoofing or MitM (man in the middle) attacks. Hence, it appears as a significant disadvantage of fog computing.

Hence, these are the four significant disadvantages of fog computing that must be considered when making any decision regarding fog computing.

Conclusion

Hence, that was all about fog computing, its advantages, disadvantages, and industries which rely on fog computing. One can research more about fog computing and learn various details about it. Fog computing is a powerful technology that processes data collected by IoT devices. Edge and cloud computing can’t expand on a larger scale like the cloud; hence, it can only be used in a smaller region. Therefore, combining fog and cloud computing applications is an excellent opinion for the company’s IoT architecture.

Health

Digital Transformation: the importance for Pharma and MedTech Companies

Published

1 month ago

August 8, 2024

Daniel Egusa

Digital Transformation for Pharma and MedTech Companies

Digital transformation refers to integrating digital technology into all areas of a business, fundamentally changing how companies operate and deliver value to customers. This transformation is crucial for staying competitive and innovative in the pharmaceutical and MedTech industries.

Pharma companies are increasingly recognizing the importance of digital innovation (among which AI is playing an increasingly important role) to enhance manufacturing processes, streamline operations, and improve patient outcomes. The COVID-19 pandemic has accelerated the adoption of digital tools, highlighting their potential to expedite drug development and bring life-saving treatments to market more efficiently.

As healthcare continues to evolve, embracing technology and investing in digital transformation in pharma will be essential for companies aiming to stay ahead in this dynamic sector.

Setting the vision

The first step in digital transformation for pharma companies is to set a clear vision. This involves answering essential questions such as:

What are the company’s long-term goals?
How can digital tools help achieve these goals?
What specific areas need improvement?

By addressing these questions, companies can better articulate their needs to service providers and move forward with a well-defined plan. Setting this vision is crucial for aligning the entire organization towards common objectives and ensuring that every digital initiative contributes to the overall strategy. For instance, if a company aims to enhance its manufacturing efficiency, it should explore digital innovation solutions like AI-driven automation and predictive analytics.

Furthermore, understanding the trend of pharma landscape in 2024 can provide valuable insights into emerging technologies and practices that could be integrated into the company’s digital strategy. For example, digital transformation in pharma manufacturing often involves incorporating advanced technology to streamline processes, reduce costs, and improve product quality.

Key components of digital transformation

Semi-digital vs. fully digital transformation

Many pharma companies are currently stuck in a semi-digital phase. This stage involves processes where documents are manually scanned and then saved as digital versions.

While this represents a step towards digital innovation, it falls short of a true digital transformation. Real digital transformation in pharma and MedTech companies requires eliminating manual intervention and automating the entire process flow. For instance, advanced systems can automate document creation, approval, and updates, ensuring that all relevant files are seamlessly integrated and maintained.

By moving beyond semi-digital practices, companies can fully harness the benefits of digital technology, improving efficiency and compliance. This shift is crucial for staying competitive in an industry increasingly driven by digital transformation in pharma marketing and manufacturing.

Computer system validation (CSV)

In the context of digital transformation in pharma, Computer System Validation (CSV) is a crucial element for ensuring that software systems perform as intended and comply with regulatory standards. The validation process, however, can be extensive and complex, often requiring significant time and resources.

To streamline this, many pharma companies are turning to external CSV consultants or leveraging solutions that adhere to the GAMP5 validation approach. This method not only simplifies the validation process but also aligns with industry best practices, ensuring compliance and operational efficiency.

By adopting such strategies, companies can better navigate the challenges of digital transformation, particularly in the pharma manufacturing and pharma marketing sectors, setting the stage for enhanced productivity and regulatory compliance.

Role of AI in digital transformation

Artificial Intelligence (AI) is transforming the pharmaceutical and MedTech industries by enhancing efficiency, accuracy, and innovation. AI streamlines drug discovery optimizes manufacturing processes, and improves patient outcomes, giving companies a competitive edge.

In drug development, AI accelerates the discovery process by analyzing large datasets to identify potential drug candidates faster and more accurately. It optimizes clinical trials by selecting suitable patients and predicting trial outcomes, leading to faster and more reliable results. In manufacturing, AI-driven automation and predictive analytics improve production efficiency, reduce downtime, and ensure product quality by identifying real-time anomalies.

AI is also revolutionizing supply chain management by improving decision-making, reducing inventory levels, and minimizing out-of-stock scenarios. Integrating solutions like AI for smart pharmaceutical inventory management allows companies to achieve substantial savings. Moreover, AI enhances personalized patient care by analyzing data to provide tailored treatment recommendations and predict medication responses, thus improving healthcare quality.

Implementation and compliance considerations

Accessibility and affordability

Digital transformation in pharma is not just about adopting the latest technologies; it also involves ensuring that these technologies are accessible and affordable for all stakeholders involved. To achieve this, pharma companies must prioritize solutions that are easy to implement across various locations and cost-effective.

Cloud-based platforms are particularly suitable as they offer global implementation capabilities without the need for site-specific installations. This approach not only streamlines operations but also significantly reduces overhead costs, making it a viable option for both large enterprises and startups. By leveraging cloud technology, companies can ensure that their digital transformation efforts are both inclusive and financially sustainable, aligning with the broader goals of digital innovation and enhanced healthcare delivery.

Implementation and transformation time

The time required for a complete overhaul often raises concerns among pharma companies. However, with the right partner, this process can be both swift and seamless.

The key lies in selecting a vendor that provides end-to-end solutions and offers additional support for data import, integration, and training. This comprehensive approach not only accelerates the transition but also ensures that internal IT and QA teams are not overwhelmed by the demands of the transformation.

By leveraging such partnerships, companies can effectively navigate the complexities of digital transformation, thereby enhancing their operational efficiency and competitiveness in the healthcare sector.

Regulatory compliance

In the realm of digital transformation in pharma, regulatory compliance remains a critical consideration. Both pharma companies and MedTech industries must adhere to stringent regulatory standards to ensure the safety and efficacy of their products. Therefore, sourcing a digital solution that meets all compliance criteria is essential.

Opting for 21 CFR Part 11-compliant software can help companies overcome regulatory hurdles by ensuring that electronic records and signatures are trustworthy, reliable, and equivalent to paper records. This is particularly important in the context of digital innovation and healthcare, where maintaining data integrity and security is paramount.

Additionally, the implementation of compliant software solutions can streamline manufacturing processes, reduce the risk of non-compliance, and facilitate smoother audits. As technology continues to evolve, staying ahead of compliance requirements will be crucial for maintaining operational efficiency and ensuring patient safety.

Conclusion

Championing an end-to-end digital transformation project requires careful planning, vendor selection, and execution. Partnering with an experienced service provider can significantly ease the transition from being digital to being digital.

This approach not only streamlines the implementation process but also ensures that the transformation aligns with industry standards and regulatory requirements. By leveraging the expertise of a seasoned provider, pharma companies can focus on core activities while seamlessly integrating digital innovation into their operations.

This strategic partnership is crucial for navigating the complexities of digital transformation in pharma manufacturing and healthcare, ultimately enhancing operational efficiency and accelerating time-to-market for new treatments.

Artificial Intelligence

Galaxy AI to Expand to Mid-Range Galaxy A Phones in 2024

Published

1 month ago

August 7, 2024

William Watson

Exciting news for Galaxy A phone users! Samsung is set to bring its innovative Galaxy AI technology to select mid-range Galaxy A models in 2024. This expansion will start with the Galaxy A35 and Galaxy A55, according to sources from SamMobile.

Limited Galaxy AI Features for Galaxy A35 and A55

While this is a significant move, it’s important to note that not all Galaxy AI features will be available on these mid-range devices. Samsung has yet to specify which AI tools will be included, but features requiring substantial on-device processing power will likely be omitted.

The rollout of Galaxy AI to the Galaxy A35 and A55 is expected to happen through the One UI 6.1.1 update, which could arrive as early as this month or next. However, there is no confirmed release date.

Current Availability and Limitations

As of now, the most affordable Galaxy AI-enabled phone available is the Galaxy S23 FE. Even this model does not include all the AI features found in the original Galaxy AI suite for the Galaxy S24 series, such as Instant Slow-Mo.

Despite hardware similarities, older Galaxy A models like the Galaxy A54, which shares the Exynos 1380 SoC with the Galaxy A35, are not confirmed to receive Galaxy AI. Samsung appears to be focusing on models released in 2024 and later.

Samsung’s AI Ambitions

At Unpacked 2024, Samsung announced its ambition to extend Galaxy AI tools to over 200 million Galaxy devices, including phones, tablets, and wearables. This expansion to mid-range Galaxy A phones is a part of that strategy, aiming to democratize advanced AI features across a broader range of devices.

Summary

Samsung’s Galaxy AI is set to arrive on mid-range Galaxy A phones, starting with the Galaxy A35 and A55, through the One UI 6.1.1 update. While not all AI features will be available, this marks a significant step in making advanced AI technology more accessible. Stay tuned for further updates on the rollout and specific features.

Apps and Software

Reports: Samsung Delays One UI 7.0 Beta Release, Focuses on One UI 6.1.1

Published

1 month ago

August 7, 2024

Florencia Haden

If you’re eagerly anticipating the One UI 7.0 beta update for your Galaxy phone, you may need to exercise some patience. According to a recent rumor, the beta program for One UI 7.0 is not expected to launch shortly.

Rumors Indicate Delay

The latest situation of One UI 7 Beta: There are still no plans to launch it in the near future.
— ICE UNIVERSE (@UniverseIce) August 5, 2024

The information comes from a post on X (formerly Twitter) by @UniverseIce, which states, “There are still no plans to launch it [One UI 7.0 beta] shortly.” The source did not provide specific dates, leaving the exact timeline unclear. This contrasts with earlier reports suggesting the One UI 7.0 beta might go live in the third week of August.

Focus Shifts to One UI 6.1.1

According to our sources, Samsung may have delayed the One UI 7.0 beta release to prioritize the rollout of the One UI 6.1.1 update across more devices. One UI 6.1.1 debuted with the Galaxy Z Fold 6 and Galaxy Z Flip 6 during the Unpacked event in July. Despite being a minor update, One UI 6.1.1 is expected to reach older Galaxy foldable phones, Galaxy tablets, and some high-end slab-type phones.

New Features and Improvements

The One UI 6.1.1 update is set to introduce new Galaxy AI features to older flagship phones. However, One UI 7.0 promises to be a more significant release. It is rumored to bring substantial changes to UI elements, new animations, and potentially more extensive Galaxy AI capabilities.

Limited Beta Release

When One UI 7.0 beta eventually launches, it will be available in limited markets and for select devices. If everything proceeds smoothly, the first stable build is expected to roll out before the end of the year.

Conclusion

Samsung’s decision to focus on One UI 6.1.1 indicates a strategic move to enhance the current user experience while preparing for the more extensive changes expected with One UI 7.0. We will keep you updated with any new information regarding the release dates and features of these updates.