Artificial Intelligence (AI) has become an integral part of our daily lives — powering everything from personalized recommendations on streaming platforms to medical diagnostics and smart assistants. These AI systems rely heavily on data, which is often personal and sensitive. However, traditional AI models require centralizing data on servers, raising serious concerns about privacy, data security, and compliance with regulations like GDPR and CCPA.

To address these challenges, the AI community has introduced an innovative approach called Federated Learning (FL). Federated Learning enables AI models to learn from decentralized data, offering a powerful way to preserve privacy while harnessing the benefits of machine learning.

What is Federated Learning?

Federated Learning is a decentralized machine learning technique where multiple devices or institutions collaboratively train a shared AI model without exchanging their raw data. Instead, each participant keeps its data locally and only sends model updates (such as weight changes or gradients) to a central server. The server aggregates these updates to improve the global model, which is then sent back to all participants for further local training.

In essence, FL moves the model to the data, rather than moving data to the model. This concept allows AI to be trained on diverse and private datasets without compromising confidentiality.

Why is Federated Learning Important?

Privacy Preservation

Since raw data never leaves its original location, federated learning drastically reduces the risk of data breaches and misuse. This approach aligns perfectly with privacy laws and user expectations, making it easier for organizations to comply with data protection regulations.

Collaboration Without Compromise

FL enables multiple parties — like hospitals, banks, or mobile devices — to collaboratively improve AI models by leveraging data that they cannot share openly. This collaborative learning leads to better models without exposing sensitive information.

Efficiency and Reduced Latency

Processing data locally minimizes bandwidth use and reduces latency, making it ideal for devices like smartphones or IoT sensors. This allows real-time personalization and faster AI responses.

Real-World Applications of Federated Learning

• Healthcare: Hospitals can jointly train AI models on patient data to improve diagnostics without sharing sensitive records. This collaborative approach enhances medical research while respecting privacy.
• Mobile Devices: Google’s Gboard uses federated learning to personalize typing predictions by learning from users’ typing behaviour locally, without sending personal keystroke data to servers.
• Finance:Banks can collectively build fraud detection models by sharing model updates instead of customer data, increasing security while maintaining confidentiality.
• Internet of Things (IoT): Edge devices can locally train AI models to detect anomalies or predict failures, minimizing data transmission and protecting sensitive operational information.

How Does Federated Learning Work?

1. Model Initialization: A central server initializes the AI model and shares it with all participants.
2. Local Training: Each participant trains the model on their local data and calculates model updates.
3. Update Aggregation: Participants send their model updates (not raw data) to the central server.
4. Model Update: The server aggregates the updates, usually by averaging, to improve the global model.
5. Iteration:The updated global model is redistributed to participants, and the cycle repeats until the model is sufficiently trained.

Challenges of Federated Learning

While federated learning offers promising benefits, it also faces certain challenges:

• Non-Uniform Data: Participants often have data with different distributions, which can make training difficult.
• Communication Costs: Frequent exchange of model updates can consume significant bandwidth, especially for large models.
• Security Threats: Malicious participants could try to poison the model or extract private data from updates, necessitating robust defense mechanisms.
• Resource Constraints: Devices like smartphones may have limited processing power, affecting their ability to train models locally.

Researchers are actively working on solutions such as model compression, secure aggregation protocols, and adaptive training strategies to address these challenges.

The Future of Federated Learning

As data privacy regulations become stricter and users grow more aware of their rights, federated learning is poised to become a foundational technology for AI development. Combining FL with techniques like differential privacy, homomorphic encryption, and secure multiparty computation will further strengthen privacy guarantees.

Moreover, federated learning can unlock new collaborations across industries, enabling shared AI insights without sacrificing data ownership or privacy. Whether it’s in healthcare, finance, or edge computing, FL is helping build AI systems that are not only powerful but also ethical and secure.

Conclusion

Federated Learning represents a transformative shift in how AI models are trained, moving from centralized data collection to decentralized, privacy-preserving collaboration. By allowing AI to learn from data without exposing it, FL balances the growing demand for intelligent systems with the essential need to protect user privacy. For organizations and developers aiming to innovate responsibly, federated learning offers a practical, forward-looking path that respects both the power of AI and the rights of individuals.

By:
Mr. S. SIMONTHOMAS,
Assistant Professor,
Department of Computer Science and Engineering,
Aarupadai Veedu Institute of Technology (AVIT),
Vinayaka Mission’s Research Foundation (Deemed to be University),
Chennai

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The fourth industrial revolution, Industry 4.0, is transforming conventional manufacturing and industrial practices through smart technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI). Predictive maintenance (PdM) is among the most effective uses of AI in these areas – with the use of algorithms to foresee upcoming failures, avoid lost time and expenditure, and overall performance optimisation.

What Is Predictive Maintenance?

Predictive maintenance utilizes real-time information from sensors, machines, and systems in operation to anticipate when a machine or part may fail. Whereas reactive maintenance (repairing machines after they fail) and preventive maintenance (performing maintenance on machines even if it isn’t due) both involve actions that are not scheduled based on the actual condition of the equipment.

Role of AI in Predictive Maintenance

Industry 4.0 produces large amounts of data that require powerful tools for analysis, pattern recognition, and decision-making. Machine learning (ML) and deep learning, both being areas of artificial intelligence (AI), are at the forefront of transforming this data into smart information. AI algorithms can:

• Detect anomalies in sensor data
• Reveal hidden patterns related to past failures
• Estimate the remaining useful life (RUL) of components
• Trigger maintenance alerts only when necessary

These functions enable organisations to shift from a reactive to a proactive operating model, preventing expensive surprise downtimes.

Key Components of AI-Driven Predictive Maintenance

1. Data Collection and Preprocessing

On industrial machinery, sensors measure vibration, temperature, pressure, and voltage, transmitting data in real time. This raw data is typically noisy and unorganized. We need to clean, normalize the data, and extract useful features, as they do in traditional AI systems.

2. Feature Engineering

The objective of feature engineering is to choose the most important attributes, i.e., independent variables, that describe the machinery deterioration or breakdown. For instance, a rise in vibration frequency can be a sign of bearing wear in a motor. Prediction accuracy in AI models is greatly improved by features engineered for specific domains.

3. Model Development

Predictive maintenance typically includes the use of machine learning models (e.g., Random Forest, SVM, and Gradient Boosting System) for classification and regression purposes. For sequential data of complex systems, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models are more successful.

These models are based on historical failure data, and they continuously update their predictions over time (e.g., by using methods like incremental learning or online learning).

4. Anomaly Detection

Anomaly detection plays an important role in learning early symptoms of failure. Unsupervised learning algorithms such as K-means clustering, Autoencoders, or Isolation Forests detect patterns in data that deviate significantly from the usual, which can alert to potential trouble before it occurs.

5. Remaining Useful Life (RUL) Estimation

RUL estimation predicts the remaining life of a component before it has to be maintained or replaced. This is useful when scheduling maintenance that requires the least amount of downtime. It is usually identified using AI models such as prognostic models and survival analysis models.

Real-World Applications

1. Manufacturing Plants

AI systems for PdM observe the health of CNC machines, conveyors, and robotic arms in automaking and electronics manufacturing. General Motors employs machine learning algorithms to interpret sensor data from 12,000 pieces of machinery in 100 factories worldwide.

2. Oil and Gas

Predictive maintenance in oil refineries and offshore platforms is necessary since breakdowns can result in huge losses. Artificial intelligence programs scan pressure and temperature sensors to identify early signs of pipe deterioration, pump breakdowns, or inefficiency in the compressors.

3. Aerospace

Hundreds of sensors are present in an aircraft engine. Companies like Rolls-Royce employ artificial intelligence to predict engine failures and optimize service scheduling efficiency. Applying AI to preventive measures optimizes safety by pre-determining problems and saving operational costs by maintaining performance optimality.

4. Energy and Utilities

With predictive maintenance software relying on AI, power turbines used in generation, transformers, and grid machinery are continuously monitored, reducing the risk of blackouts and maximizing the utilization of the machinery.

Benefits of AI-Based Predictive Maintenance

• Less Downtime: Early detection enables early treatment, preventing surprise shutdowns.
• Cost-effectiveness: Specialized maintenance, done less frequently, reduces unnecessary labor and parts costs.
• Extended Equipment Life: Prompt reaction avoids complete degradation and destruction.
• Improved Safety: Preventing equipment failure reduces risks for employees and operations.
• Data-Driven Decision-Making: Artificial Intelligence provides useful insights for the management of assets.

Challenges and Considerations

While AI-based predictive maintenance has its advantages, these are some of its challenges:

• Data Quality and Accessibility: Reliable forecasts rely on high-quality, labeled data, which is often a challenge.
• Integration with Legacy Systems: Many industrial setups still operate on legacy machines that lack IoT or sensor capabilities.
• Model Interpretability: Black-box AI models can be difficult for technicians to trust and interpret.
• Scalability: AI models must scale across different types of machines and environments with varying conditions.

Future Outlook

The use of edge computing, digital twins, and 5G connectivity will enhance the performance of predictive maintenance powered by AI in Industry 4.0. By implementing edge devices, real-time monitoring can be made on the production floor, and digital twins- virtual replicas of physical assets- are used in simulating the maintenance scenarios as well as sharpening the process of scheduling. In addition, explainable AI (XAI) is making strides in the transparency of the model and in building user trust.

Conclusion

Artificial Intelligence for predictive maintenance is at the core of smart manufacturing within the realm of Industry 4.0. It facilitates industries to move away from reactive and preventive methodologies to a proactive and smart maintenance culture. While issues still afflict deployment and data fusion, the latest developments in AI technologies usher in an era where industrial equipment is monitored, controlled, and serviced with finesse and effectiveness beyond anything previously conceived.

By:
Dr.S.BALAKRISHNAN,
Professor and Head,
Department of Computer Science and Engineering,
Aarupadai Veedu Institute of Technology (AVIT),
Vinayaka Mission’s Research Foundation (Deemed to be University),
Chennai

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In the rapidly changing world of technology and digital culture, few concepts have sparked as much interest — and controversy — as NFTs (Non-Fungible Tokens) and the Metaverse. The conversation about these innovations has moved from intrigue to a serious investment interest, with sales of digital art in millions and purchases of virtual lands in online worlds. But the real question is: Are NFTs and the Metaverse just passing fads, or do they actually represent the future of digital ownership?

Understanding NFTs and the Metaverse

To assess their potential, it’s important to first understand what these technologies are.

NFTs are unique digital tokens stored on a blockchain, serving as verifiable proof of ownership for a digital asset — whether it’s an image, video, music file, game item, or even a virtual sneaker. When you compare cryptocurrencies like Bitcoin or Ethereum, which are fungible (meaning every unit is the same), NFTs stand out as non-fungible — each token is one-of-a-kind and can’t be swapped on a one-to-one basis.

The Metaverse, by contrast, is a communal virtual environment that reunites digital environments, augmented reality (AR), virtual reality (VR), and blockchain technologies. In the Metaverse, people can work, socialize, shop, play, and own assets — just as in the real world. In this instance, NFTs are essential as a building block for ownership and identity.

The Rise of Digital Ownership

Digital assets that fall into the traditional category—like music, videos, or in-game items—have long been without any sense of genuine ownership. What users typically have is access or a license to use them, which can be revoked or lost. NFTs change this paradigm. When we track ownership on a blockchain, it shifts control to a decentralized model, allowing people to freely own, trade, and transfer their digital assets.

In the Metaverse, this ownership experience is truly enhanced. Users can purchase virtual land, create their own digital storefronts, customize avatars, or collect art pieces — all linked to NFTs. Ownership as an idea reaches far beyond just visual representation; it is also verifiable, tradable, and often monetizable. Platforms like Decentraland, The Sandbox, and Roblox are currently showing us how this works.

Opportunities for Creators, Gamers, and Brands

The combination of NFTs and the Metaverse creates fantastic opportunities for:

• Creators and Artists: They may mint NFTs so that they can sell their virtual artwork directly to collectors without needing any middlemen, receive royalties on resale, and host their art in virtual galleries.
• Players: In-game property, including skins, weapons, or collectable items, can now be effectively traded based on ownership and platforms, which can produce real-world value.
• Brands and Enterprises: From Nike’s virtual sneakers to Digital fashion of Gucci, companies are experimenting with metaverse marketing and commerce.

These innovations additionally increase participation access. Any person having an internet connection can create or achieve NFT to overcome geographical and financial obstacles and can join a virtual economy.

The Flip Side: Hype, Risks, and Challenges

Despite the enthusiasm, many critics claim that the current NFT and Metwor Boom are mainly speculative instead of being speculative. There is significant instability in the NFT market. There has been a rapid decline after initial discussion on the values of some digital artifacts. Confidence and stability have been extended by scams, literary materials, and “pump-dump” schemes.

Metaverse, by contrast, faces various technical and social challenges. A significant number of experiences still depend on the premium VR hardware, which limits access. In addition, questions arise about platforms, long-term user retention, and the difference in the moral impact of living a virtual life.

An important issue is environmental impact. Blockchain networks that drive NFTs, especially athenium, have been criticized for their energy use. However, upgrade and alternative series such as Solana and Polygon are addressing these concerns.

Bridging the Gap: A More Grounded Future

Even though the propaganda cycle has generated unrealistic expectations, it will lead to completely dismissing NFTs and the metaverse. History indicates that disruptive technologies often meet doubts in their early stages. Internet, social media, and smartphones were once mocked – yet they have become important for modern existence.

The application of NFTS is carrying forward the areas of art and gaming. We now see them in digital identity, education credentials, real estate deeds, and supply chain tracking. Metaverse is used rapidly in virtual training, distance collaboration, and hybrid events.

Conclusion: A Glimpse Into the Digital Future

So, are NFTs and the Metaverse just hype — or the real future of digital ownership?

There may be a lie in the middle of the answer. While overblown expectations and speculative bubbles are real, the underlying technologies are powerful, innovative, and developed. As the infrastructure matures and cases are used, NFTs and the metaverse are designed to re-add to how we give importance to experience and digital assets.

They cannot displace the physical world, but they are undeniably defined by the digital location. And in that sense, the future of ownership is already here — just in a form we’re still learning to navigate.

Remember when someone invests in a virtual mansion or digital jacket for his avatar next time: this is not a perfect pastime – it is the prediction of the next digital revolution.

By:
Dr.S.BALAKRISHNAN,
Professor and Head,
Department of Computer Science and Engineering,
Aarupadai Veedu Institute of Technology (AVIT),
Vinayaka Mission’s Research Foundation (Deemed to be University),
Chennai

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You’re probably tired of everyone saying “be industry-ready” or “learn the latest technology.” But no one really tells you where or how to actually do it, right? Well, AVIT has stepped up to fill that gap.

Among the many engineering colleges in India, and especially after the introduction of the New Education Policy—which brings a lot of flexibility—staying relevant to industry requirements has been a million-dollar question, or let’s say, a grey area. Many institutions talk about collaborations, but those often remain unclear to students who are planning to take them seriously.

This is where AVIT (Aarupadai Veedu Institute of Technology), located in Chennai, has come out with a different approach. Starting this year, all engineering programs offered by the institution are industry-integrated. Their professors, along with experts from the industry, will be teaching and mentoring the students.

One such recent strategic alliance is the MoU signed between AVIT and Medini Technologies—a strategic training partner—which is a huge advantage for students interested in Civil or Mechanical Engineering.

Now, you might wonder what’s so special about this MoU. Medini Technologies isn’t just another training partner. They are actively involved in the industry and know exactly what’s in demand. They bring with them cutting-edge tools like Building Information Modeling (BIM) for Civil Engineering, and Product Design and Manufacturing for Mechanical Engineering.

The best part? These globally trending technologies are directly integrated into your curriculum. Every semester, you’ll earn certifications from top companies like Autodesk, Bentley, Trimble, Dassault, and PTC. You’ll work closely with industry experts who will guide you through your projects, helping you upskill with what’s currently trending in the industry. And upon successful completion of every project, you’ll be awarded Credly badges—globally recognized digital credentials.

The icing on the cake? You can even gain AICTE-approved internships and guaranteed placement opportunities.

By the time you’re in your final year, you’re not just a student—you’re someone with a proven track record of skills, industry-recognized certifications, and real, hands-on project experience. So yes, at AVIT, we’re not just teaching—we’re preparing you for the real world.

If you prepared for NEET but couldn’t qualify, it’s completely okay! Many students face this situation, but that doesn’t mean you have to give up on your passion for medicine and healthcare. There are several other rewarding career options in the medical field. One such high-demand field is Biomedical Engineering (BME).

Why choose Biomedical Engineering?

Did you know that Biomedical Engineering is one of the highest-paying fields abroad? In the United States, biomedical engineers earn an average annual salary of INR 47L (USD 56,000). Imagine the salary surge by 2030! With advancements in AI-driven healthcare, 3D-printed organs, and wearable medical tech, demand for biomedical engineers is set to skyrocket. According to reports, it is estimated that the profession is expected to have 1,400 job openings per year. So, why not choose one of the most lucrative career choices in the medical tech industry?

What does AVIT offer?

At Aarupadai Veedu Institute of Technology (AVIT), Paiyanoor, Chennai, the B.E. Biomedical Engineering programme offers an exclusive industry-integrated training programme with INTEL & NEC. This provides students with hands-on experience in the latest advancements in medical technology.

• Direct training from INTEL & NEC professionals and HCLTech: Experts train our students to equip them with industry-based AI and Machine Learning
• Global Collaborations: The department has signed MoUs with leading industries and international universities, preparing students for a successful global career
• Clinical exposure: Acquire practical experience through hospital training programs at Aarupadai Veedu Medical College & Hospital (Pondicherry & Salem)
• One-credit courses: Develop niche skills in Medical Instrumentation, Medical Coding, Medical Image Analysis, and Medical Robotics

Career opportunities in Biomedical Engineering

The global biomedical market is expected to grow to $840 billion by 2030. With a B.E. BME degree from AVIT, you can:

• Work in hospitals, R&D labs, medical device companies, and AI-driven healthcare startups
• Design prosthetics, imaging systems, artificial organs, and AI-based diagnostics
• Explore opportunities in biotech, pharmaceuticals, and healthcare innovations

Emerging trends in Biomedical Engineering

• AI & Machine Learning in Healthcare
• 3D Printing of Organs & Prosthetics, creating custom implants and bioengineered tissues
• Wearable Medical Devices like smartwatches, glucose monitors, and biosensors
• Biotechnology & Tissue Engineering to develop artificial organs

Long story short, Biomedical Engineering is a fantastic alternative to MBBS. So, grab this opportunity from AVIT, one of the best engineering colleges, and be a part of this growing field.

Is Biomedical Engineering a good career opportunity?

Are you passionate about medical technology and innovation in healthcare? Then, a degree in Biomedical Engineering could be your perfect path. At Aarupadai Veedu Institute of Technology (AVIT), Paiyanoor, Chennai, we offer a transformative learning experience that bridges engineering and medicine, preparing you for a high-impact career in the ever-evolving world of medical innovation.

Why choose Biomedical Engineering at AVIT?

At AVIT, we go beyond traditional learning! Our exclusive collaboration with tech giants like INTEL & NEC, and HCLTech ensures that industry experts train our students firsthand. This real-world tech exposure gives our graduates a competitive edge, preparing them for the future of AI-driven healthcare and medical technology.

• Direct training from INTEL & NEC and HCL TECH professionals: Learn AI-based medical applications, and sensor-driven diagnostics directly from industry leaders
• One-credit courses: Develop niche skills in Medical Instrumentation, Medical Coding, Medical Image Analysis, Medical Robotics, Nanotechnology in medicine, and Regulatory aspects in Medical Equipment
• Job-ready curriculum: Master the latest healthcare automation techniques like computational medical imaging, medical data analytics, biomechatronics, and more
• Clinical exposure: Gain real-world experience through hospital training programs at Aarupadai Veedu Medical College & Hospital (Pondicherry & Salem)
• Global Collaborations: The department has signed MoUs with leading industries and international universities to equip students for a successful global career in biomedical engineering

Future Prospects & Career Opportunities

The global biomedical engineering market is expected to reach $840 billion by 2030 with a CAGR of 6.7%. With an AVIT Biomedical Engineering degree, you can:

• Design life-saving medical devices, imaging systems, artificial organs, and more
• Work in hospitals, R&D labs, medical device companies, and pharmaceutical firms
• Explore opportunities in AI-driven healthcare startups and biotechnology firms

Emerging Trends

• AI & Machine Learning in Healthcare
• 3D Printing of Organs & Prosthetics
• AR & VR in medical diagnostics
• Wearable Medical Devices (Smartwatches, glucose monitors etc.,)
• Biotechnology & Tissue Engineering

With a Biomedical Engineering degree, you don’t just learn technology—you innovate for life. Happy learning!

Water is the most essential resource for survival in this world. All living beings rely on water to sustain life. It serves various purposes, including drinking, bathing, cleaning, cooking, and more. It is also used in agriculture, power generation, and other industries.

Although we know that water covers 70% of the Earth’s surface, only 3% of it is freshwater, suitable for drinking. Given its limited availability, we must consume water wisely. To ensure its availability, we need to keep water clean and neat. We can safeguard water for future generations if we minimize industrial waste and prevent it from dumping into our waterways. There should be public awareness campaigns and educational initiatives about water management so that everybody understands its importance.

We should focus on controlling water pollution…

By:
PREETHIVIRAJAN . S,
Reg no: 3422220504,
Department Of Civil Engineering,
AarupadaiVeedu Institute of Technology,
Chennai.

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Water whispers secrets in rippling waves,
Yet many still thirst in silent caves.
A silver stream should quench the land,
But filth and waste choke its hand.

A drop of care can flood the night,
Yet many let wrongs drift out of sight.
The well of neglect runs deep and wide,
While promises dry in the desert tide.

Pipes should sing, not cough and groan,
Yet murky ghosts haunt taps at home.
A thirst unquenched, a tale untold,
Where children drink what time has spoiled.

Flush the folly, unclog the past,
Let clean hands hold the future fast.
Drain the doubt, let justice flow,
Where crystal streams and rivers grow.

No longer should a sip be a fight,
Nor morning start with parched delight.
A flood of change, a cleansing rain,
Let water’s worth be clear again.

For every well should brim with grace,
And none should beg for just a taste.
So let us stand, as oceans wide,
To turn the tide, to heal, to guide.

By:
S.SONA,
Research Associate,
Department Of Civil Engineering,
AarupadaiVeedu Institute of Technology,
Chennai.

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Water is life. It flows through our homes, our bodies, and our ecosystems, yet for billions, access to clean water and sanitation remains a distant dream. The United Nations’ Sustainable Development Goal 6 (SDG 6) isn’t just about providing water—it’s about transforming lives, economies, and the planet. Imagine a world where every drop of water is a source of hope instead of a carrier of disease, where sanitation isn’t a privilege but a fundamental right. Today, 1 in 3 people globally lack access to safe drinking water, and over half of the world’s population doesn’t have proper sanitation. This crisis isn’t just a humanitarian issue; it’s an economic and environmental challenge that stifles growth and endangers our future. When clean water is available, children can go to school instead of spending hours collecting water. Women, who disproportionately bear the burden of water collection, can engage in economic activities and uplift communities. Farmers can produce more food without relying on contaminated water sources, reducing malnutrition and improving food security. Industries can function sustainably, minimizing water pollution and preserving aquatic ecosystems.

But achieving SDG 6 requires more than just infrastructure—it demands innovation, commitment, and a shift in how we view water as a resource. Solutions like wastewater treatment, rainwater harvesting, and decentralized sanitation systems can provide clean water where traditional pipelines can’t reach. Advanced filtration technologies and water recycling can ensure that every drop is used efficiently. Policies promoting responsible water usage, coupled with education on hygiene and conservation, can empower communities to take charge of their water security. Governments, private sectors, and individuals must collaborate to fund, implement, and sustain these solutions. Every effort counts, from reducing household water wastage to supporting organizations working to provide safe drinking water in underserved regions. Water scarcity is not a distant problem—it’s happening now, affecting millions. Climate change is exacerbating droughts and water pollution, making it even more urgent to act. We must treat clean water as the precious lifeline it is, ensuring it is accessible, sustainable, and safe for all. The ripple effect of clean water is profound—it leads to healthier people, stronger economies, and a more resilient planet. If we unite for SDG 6, we won’t just be providing water; we’ll be unlocking opportunities, restoring dignity, and securing a future where no one is left behind.

By:
S.MONISHA,
Assistant Professor-II,
Department Of Civil Engineering,
AarupadaiVeedu Institute of Technology,
Chennai.

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Edge AI is powering the current digital revolution to a large degree and revolutionizing data processing and analysis. Edge AI places artificial intelligence at or near data sources such as sensors, cameras, and IoT devices and thus reduces latency, improves security, and optimizes efficiency in real-time decision-making.

What is Edge AI?

Edge AI is the use of AI algorithms in edge devices so that they execute the data locally rather than using cloud infrastructure. Edge technology shatters the dependency on the continuous use of the internet and provides real-time data processing at the “edge” of the network where data originates.

Key Benefits of Edge AI

1. Low Latency & Real-Time Processing
   • Autonomous vehicles, medical monitoring, and industrial automation applications need instant responses. Edge AI provides decisions to be run in milliseconds without depending on cloud servers.
2. Enhanced Privacy & Security
   • Data processed on-site does not need to send sensitive information to other servers, hence curbing the risks of data breaches and cyber threats.
3. Reduced Bandwidth & Operational Costs
   • Edge AI alleviates network congestion and reduces costs related to cloud storage and computing by reducing the data that is transmitted to the cloud.

Edge AI Applications in Real Life

• • Smart Cities: Traffic management and monitoring with AI functionality enhance urban planning infrastructure
  • Healthcare: Wearable technology with real-time monitoring of patient vital signs enables early disease detection
  • Manufacturing: Predictive maintenance via AI prevents machinery breakdowns

With the ongoing advancements in AI, Edge AI will revolutionize industries, making tech systems more efficient, responsive, and secure. It will be one of the driving forces in the development of the next generation of smart systems.

By:
Dr.S.Balakrishnan,
Professor and Head,
Department of CSE,
AVIT

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