Transforming Hydration Monitoring with an Innovative Device

Client Spotlight: Transforming Hydration Monitoring with an Innovative Device

Engineering ServicesIoT Software

Intake Health, a company based in the US, is changing how hydration is monitored. Recognizing that hydration is not a static metric but a dynamic process requiring continuous assessment, Intake Health has developed an innovative solution that is both effective and hassle-free. This breakthrough is particularly significant for athletes, where hydration levels directly impact performance.

The device designed by Intake Health is elegantly simple yet highly functional. Installed in urinals, it uses advanced color sensors to monitor hydration levels in real-time. This ingenious approach eliminates the need for intrusive methods, offering seamless and efficient hydration testing.

The Role of OTA Updates in Hydration Monitoring Devices

Over-the-Air (OTA) updates are crucial for interconnected devices. They enable remote firmware updates, eliminating the need for manual intervention. OTA updates address critical issues such as software bugs, enhance security, introduce new features, and extend device functionality without human intervention. This approach is particularly valuable for devices deployed across diverse and widespread locations.

Why OTA Updates Matter

In today’s competitive landscape, timely updates are crucial. OTA updates not only ensure devices remain functional but also extend their lifecycle. They enhance user experience, enable rapid iterations, and ensure compliance with evolving industry standards, keeping devices competitive and reliable.

How we Empowered Intake Health’s Vision with Firmware Updates

Srushty Global Inc. played a pivotal role in enabling Intake Health’s hydration monitoring devices by implementing OTA functionality. Collaborating on an older NRF51 platform, Srushty revamped the firmware to include:

Seamless Remote Updates: Enabled remote firmware updates for efficient device management.

Optimized Memory Partitioning: Enhanced memory utilization to support new features.

Reduced Power Consumption: Lowered power usage from 10mA to just 25μA, ensuring long-lasting device operation.

These improvements transformed the hydration monitoring device, making it more functional, scalable, and user-friendly. Intake Health’s team appreciated Srushty’s proactive approach, technical expertise, and commitment to understanding their business goals.

“I am impressed with how proactive the Srushty team is”

“The Srushty team acted as an extension of our own, proactively ensuring they had the right requirements and delivering on time. Their implementation of OTA updates and other features transformed our device’s functionality and usability,” says Michael Bender, CEO, Intake Health.

Keep Reading: Client Spotlight: How Wetflix Solutions Realized Its Vision in Water Management
Serverless_Architecture-IoT

What is serverless architecture? What are the use cases?

IoT Software

What is Serverless Architecture?

Serverless architecture represents a cloud computing paradigm where the infrastructure management tasks like server provisioning, scaling, and maintenance are handled by the cloud provider. This model enables developers to focus purely on code functionality without the overhead of continuous server management, promoting a more efficient development workflow.

Benefits of Serverless Architecture:

  • Scalability: Automatically adjusts computing resources on the fly to seamlessly handle increasing or decreasing loads, making it ideal for applications with unpredictable traffic.
  • Cost-Effectiveness: Reduces costs by charging only for the resources used in function executions, eliminating the need to pay for idle computing capacity.
  • Increased Agility: Developers can push updates faster and more frequently with reduced dependency on the underlying infrastructure, significantly shortening the development lifecycle.
  • Improved Reliability: Utilizes the cloud provider’s distributed architecture to ensure higher availability and fault tolerance across geographic locations.
  • Enhanced Security: Takes advantage of the cloud provider’s security measures, including data encryption, routine compliance audits, and multi-factor authentication, ensuring robust defense mechanisms are in place.

5 Use Cases for Serverless Architecture:

  • Real-time Data Processing: Capable of processing large data streams in real-time, serverless architecture supports applications that require immediate computational responses, such as event-driven data analysis or real-time monitoring systems.
  • API Gateway: Provides a scalable way to manage API requests and routings, ensuring high availability and automatic scaling during demand spikes or drops.
  • Web Applications: Facilitates the rapid development and deployment of interactive web applications, particularly useful for sites with dynamic content that changes based on user interaction.
  • Machine Learning: Enhances the efficiency of deploying machine learning models by allowing for on-demand, scalable computing power to handle complex computations without the cost of running dedicated hardware.
  • Background Tasks: Ideal for scheduling and executing background tasks, such as data synchronization, email processing, or time-intensive computational jobs, without the need for permanent server capacity.

Key Players in Serverless Architecture:

  • AWS Lambda: Integrates with Amazon’s vast cloud ecosystem, providing extensive tools for deploying serverless code across different applications and services.
  • Azure Functions: Supports a wide range of programming languages and integrates seamlessly with other Azure services, offering a highly customizable environment for developers.
  • Google Cloud Functions: Excels in handling and processing data from Google’s analytics and machine learning platforms, making it a preferred choice for applications built on Google Cloud.

Best Practices for Serverless Architecture:

  • Design for Scalability: Craft applications with the expectation of scalability, ensuring that the architecture can handle both high peaks and low troughs in demand.
  • Monitor and Optimize: Employ comprehensive monitoring tools to track application performance and optimize resource usage, minimizing costs and improving efficiency.
  • Secure Your Code: Adopt strict security protocols, including the use of secure coding practices, regular audits, and proactive threat detection strategies.
  • Test Thoroughly: Implement robust testing strategies to ensure applications perform consistently and reliably under various scenarios.
  • Choose the Right Provider: Consider not only the capabilities but also the limitations and costs associated with different cloud providers to find the best fit for specific application needs.

Challenges and Limitations:

  • Cold Start: Address the latency issues that arise from the initialization of function instances, which can affect performance, especially in high-demand scenarios.
  • Vendor Lock-in: Evaluate the implications of being tied to a specific cloud provider’s infrastructure and services, considering the potential challenges of migrating to a different platform.
  • Debugging Complexity: Develop strategies to effectively troubleshoot and debug in a serverless environment, where traditional debugging tools and methodologies may not be applicable.

Serverless architecture is reshaping application development with its ability to streamline operations and enhance scalability. By embracing this approach, organizations can drive innovation, cut costs, and respond more agilely to market demands. Understanding its framework, capabilities, and integration strategies is crucial for leveraging its full potential.

AUTHOR

Sumithra N

Solution Architect, Srushty Global Solutions

An Experienced Solution Architect with a strong background in Full-stack development, targeting challenging opportunities in Software Development. Focused on team leadership, training, and guidance, she excels in driving high-performing teams toward success. Her expertise spans across software development and related functional areas, where she consistently delivers innovative solutions and fosters a collaborative work environment. Passionate about mentoring and empowering her team, she is dedicated to guiding the next generation of engineers while staying at the forefront of technological advancements.

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AI and IoT Automation

How AI and IoT Together are Reshaping Automation and Decision-Making?

IoT Software

The rapid convergence of Generative AI and the Internet of Things (IoT) is guiding a new era of innovation and efficiency across industries. While IoT has been instrumental in connecting devices and gathering massive amounts of data, Generative AI leverages this data to create models, automate complex decisions, and generate predictive insights. This combination enables businesses to monitor operations in real time and enhance productivity, efficiency, and customer experiences through advanced automation.

Understanding the Intersection of Generative AI and IoT:

The Internet of Things (IoT) connects physical devices—sensors and wearables to industrial machinery—into networks that continuously collect data. Generative AI, on the other hand, uses machine learning algorithms and AI models to analyze this data, recognize patterns, and generate outcomes or predictions that mimic human creativity and intelligence. When these two technologies intersect, the potential to revolutionize business operations becomes immense.

For example, in a manufacturing plant, IoT sensors monitor machinery by capturing real-time data on temperature, pressure, and vibration levels. Generative AI processes this data to predict maintenance needs and suggests optimal operational strategies. The AI could identify inefficiencies in energy usage and generate automated solutions to reduce waste and costs.

The key lies in the data. IoT provides the data streams from interconnected devices, while Generative AI transforms this data into actionable insights. Whether predicting equipment failures, optimizing production schedules, or generating innovative product designs, the synergy of these technologies drives smarter, more adaptive business operations.

Real-World Applications of Generative AI and IoT:

The combined impact of Generative AI and IoT is already evident across several industries:

  • Manufacturing: In smart manufacturing, IoT-enabled sensors monitor equipment performance and production metrics in real time. Generative AI models use this data to predict failures before they happen, optimize production schedules, and even design improved manufacturing processes. For instance, an automotive company could use Generative AI to create optimized assembly line configurations, minimizing bottlenecks and enhancing throughput.
  • Healthcare: Connected devices like wearables and smart monitoring systems track patient health. Generative AI analyzes the data to predict potential health risks, suggest personalized treatment plans, and even generate synthetic medical data for research and training. AI-driven predictive models can alert healthcare providers to early signs of chronic diseases, enabling timely interventions.
  • Logistics: Supply chain management becomes more efficient with IoT sensors tracking inventory, shipments, and warehouse operations. Generative AI forecasts demand, generates optimal inventory strategies, and automates supply chain decisions in real-time. A logistics company could predict shipment delays, reroute resources, and automatically adjust inventory levels to meet customer demand.

In each case, the collaboration between IoT’s real-time data collection and Generative AI’s predictive power is transforming industries, driving them toward higher efficiency and smarter automation.

Enhancing Automation with Generative AI and IoT:

Automation has always been a key goal for businesses aiming to improve operational efficiency, reduce human error, and scale quickly. The combination of Generative AI and IoT is unlocking new levels of automation by enabling systems that can adapt, learn, and create new solutions autonomously.

For example, in a smart factory, IoT sensors provide data on every aspect of the production line, from energy usage to equipment performance. Generative AI processes this data to simulate various production scenarios, generate optimized schedules, and automatically adjust operations in real time. This minimizes waste, reduces downtime, and ensures that production runs at peak efficiency.

Another example is autonomous supply chains. IoT tracks the movement of goods, while Generative AI predicts demand and suggests automated replenishment strategies. The system continuously learns from data and adjusts its operations to optimize inventory levels, reduce delays, and cut costs. Retailers can leverage this technology to automate restocking processes based on real-time sales data and demand forecasting.

As businesses increasingly rely on these intelligent systems, Generative AI and IoT are driving a new era of adaptive automation, where systems can not only react to data but also generate solutions and act upon them with minimal human intervention.

The Role of Edge AI in the IoT Ecosystem:

As IoT networks grow, there’s an increasing need for processing power closer to the data source. Edge AI is emerging as a critical enabler in the IoT ecosystem, where data is processed locally at the device level rather than relying solely on centralized cloud systems. This allows for faster decision-making and reduced latency in critical applications.

For instance, in a manufacturing plant, edge AI is used for quality control during the production process. High-speed cameras and sensors inspect products as they move along the assembly line, and edge AI processes this visual and sensory data in real-time to detect defects or anomalies. If an issue is detected, the system can immediately stop production, reject faulty items, or make adjustments to the process without waiting for instructions from a centralized system. This instant decision-making not only improves product quality but also minimizes waste and downtime.

In industrial settings, edge AI is transforming predictive maintenance. Machines equipped with edge devices can process data locally and predict failures or optimize performance without continuous cloud communication. This reduces bandwidth usage and ensures uninterrupted mission-critical operations.

Building Scalable AIoT (AI + IoT) Solutions:

As AI and IoT integration becomes more widespread, scalability is a key consideration for businesses looking to deploy these solutions across multiple environments or geographies. Scalable AIoT solutions need to be flexible enough to adapt to different contexts, whether it’s a single factory, a global supply chain, or a network of connected cities.

For example, a global retail chain could implement scalable AIoT solutions to manage its entire supply chain. IoT sensors monitor product movements, while AI models predict demand and optimize logistics. The system is flexible enough to scale up during peak seasons and adapt to regional variations in consumer behavior.

The fusion of Generative AI and IoT is more than just a trend—it’s the future of intelligent automation and decision-making. Businesses that leverage this synergy will not only optimize their operations but also gain a competitive edge in an increasingly data-driven world.

AUTHOR

Sumithra N

Solution Architect, Srushty Global Solutions

An Experienced Solution Architect with a strong background in Full-stack development, targeting challenging opportunities in Software Development. Focused on team leadership, training, and guidance, she excels in driving high-performing teams toward success. Her expertise spans across software development and related functional areas, where she consistently delivers innovative solutions and fosters a collaborative work environment. Passionate about mentoring and empowering her team, she is dedicated to guiding the next generation of engineers while staying at the forefront of technological advancements.

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architecture for a smart water monitoring system

Microservices Architecture: How you can develop a robust architecture in IoT

IoT Software

When redesigning the software architecture for a smart water monitoring system, the original setup was based on a monolithic architecture—an approach that had its fair share of limitations. In this blog, I’m excited to dive into why transitioning to a microservices architecture could be a game-changer.

What is Microservices Architecture?

Imagine breaking a large application into several smaller, independent pieces, with each piece responsible for carrying out a specific function. This is essentially what microservices architecture is all about. Each module, or microservice, can function independently and interact with other microservices through clearly defined interfaces. This structure not only makes the system more flexible but also simplifies management, an attractive proposition for any technology-driven entity.

Pain Points with Monolithic Architecture: In a traditional monolithic architecture, the water monitoring system might be designed as a single, large application with all functionalities tightly coupled. This setup presents several challenges:

  1. Difficulty in Scaling: If there’s a need to handle more data from newly installed water tanks, the entire system needs to be scaled, which is resource-intensive and costly.
  2. Slower Development: Adding new features, such as real-time alerts for water quality issues, requires modifying and testing the whole application, leading to longer development cycles.
  3. High Impact of Changes: A minor change in one part of the system, like updating the data collection module, might necessitate extensive testing and redeployment of the entire application, increasing the risk of introducing bugs.
  4. Limited Flexibility: Adapting to new requirements, such as integrating with third-party water quality sensors, becomes challenging because of the tightly interlinked nature of the monolithic system.

Solution with Microservices: By adopting a microservices architecture, Wetflix can address these pain points effectively:

  1. Independent Scaling: Each microservice, such as data collection, real-time alerting, and reporting, can be scaled independently based on demand. For instance, if data collection from water tanks increases, only the data collection service needs to be scaled.
  2. Faster Development: New features can be developed, tested, and deployed independently. Adding a new alerting feature won’t impact the reporting module, allowing for quicker iterations and faster time-to-market.
  3. Reduced Risk of Changes: Changes in one microservice, like enhancing the water quality analysis algorithm, can be deployed without affecting other services. This isolation reduces the risk of introducing system-wide bugs.

Impact:

  1. Independent Scaling: Each microservice, such as data collection, real-time alerting, and reporting, can be scaled independently based on demand. For instance, if data collection from water tanks increases, only the data collection service needs to be scaled.
  2. Faster Development: New features can be developed, tested, and deployed independently. Adding a new alerting feature won’t impact the reporting module, allowing for quicker iterations and faster time-to-market.
  3. Reduced Risk of Changes: Changes in one microservice, like enhancing the water quality analysis algorithm, can be deployed without affecting other services. This isolation reduces the risk of introducing system-wide bugs.

By transitioning to a microservices architecture, the client can create a more robust, scalable, and flexible water monitoring system, significantly improving its ability to meet customer needs and adapt to future challenges.

 

AUTHOR

Nivedha Purushothaman

Software Engineer, Srushty Global Solutions

An experienced Full Stack Software Engineer, she excels in creating and sustaining dynamic web applications. With expertise in both front-end and back-end development, She is skilled in technologies such as React.js, Node.js, and AWS. Driven by a passion for tackling complex challenges and a commitment to knowledge sharing, she writes to motivate and inform fellow developers on best practices and current industry trends.

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Simplifying Reconfiguration for IoT-1

Simplify Reconfiguration for IoT Monitoring Devices with Software Coding

IoT Software

Reconfiguring IoT devices when shifting them from one location to another can be a cumbersome task. One of the critical problems faced in this process is the need for extensive reconfiguration, which can be both time-consuming and error-prone. Let me give you a background of the project: this is an IoT-based smart water monitoring device used to monitor water consumption from tanks in different locations

The Challenge

When users shift the device from one location to another, they have to reconfigure everything, a process that can be quite taxing. We identified this problem and rectified it with our innovative software coding.

The Solution

To address the challenges in re-onboarding firmware devices when they are moved or shifted, we proposed a revised architecture that leverages the device’s MAC address as a stable identifier. This approach streamlined the process, reducing the need for extensive software linking between old and new data.

Key Points of Our Solution

Stable Device Identification:

    MAC Address Utilization: We use the device’s MAC address as a unique and persistent identifier.

     Constant Device ID: This ensures that the device ID remains constant regardless of location or re-onboarding events.

Seamless Data Continuity:

     Consistent Data Tracking: Our solution avoids the generation of a new device ID during re-onboarding.

    Efficient Data Linking: It facilitates seamless continuity of data without the need for extensive software processes to link old and new data.

Simplifying Reconfiguration for IoT-2

Efficient Re-Onboarding Process:

            Simplified Procedure: The re-onboarding process is now quicker and less prone to errors.

    Reduced Overhead: It reduces the overhead on IT and operations teams by eliminating the need for complex data reconciliation.

Dynamic Configuration Update:

          Real-Time Updates: We implemented a software solution that includes an edit screen for updating device specifications, such as tank specs, when a device is moved or shifted.

        Uninterrupted Operation: This allows for real-time updates without disrupting device operation or data integrity.

Improved Device Management:

        Centralized Management: Device information is managed centrally through a user-friendly interface.

        Enhanced Tracking: This enhances the ability to track and manage devices across different locations or conditions.

Increased Reliability and Resilience:

        Data Protection: Our solution reduces the risk of data loss or misalignment during device transitions.

        Minimal Downtime: It ensures that devices can be quickly re-onboarded and operational with minimal downtime.

Cost Efficiency:

     Reduced Manual Intervention: Operational costs are lowered by reducing the need for manual interventions and complex software processes.

        System Efficiency: Overall system efficiency is enhanced, reducing the total cost of ownership.

Enhanced User Experience:

        Streamlined Process: Users benefit from a streamlined and intuitive process to manage and update device information.

        User Satisfaction: Improved user satisfaction by minimizing disruptions and ensuring data consistency.

By adopting this revised architecture, the re-onboarding process for firmware devices became more efficient, reliable, and user-friendly, addressing the key challenges currently faced and providing a robust solution for future scalability.

AUTHOR

Nivedha Purushothaman

Software Engineer, Srushty Global Solutions

Seasoned Full Stack Software Engineer with lot of experience in building and maintaining dynamic web applications. Specializing in both front-end and back-end development, She is proficient in technologies like React.js, Node.js, and AWS. Passionate about solving complex problems and sharing knowledge, She writes to inspire and educate fellow developers on best practices and the latest industry trends.

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