October 30, 2023 - BY Admin

How We Use AWS Cloud for Mobile App Development

Our deep-rooted experience with Amazon Web Services (AWS) has been instrumental in our ability to deliver robust, scalable, and secure software solutions to our clients. We’ve expertly leveraged a wide array of AWS services, including but not limited to S3 for cloud storage, EC2 for scalable computing capacity, and Lambda for serverless computing, in creating innovative applications ranging from enterprise-scale cloud storage to augmented reality experiences.

AWS, coupled with our ability to utilize its advanced features and services, has enabled us not only to build and manage applications in the most secure, reliable, and scalable way but also to reduce operational overhead and shorten time-to-market significantly. AWS has empowered our clients to drive their digital transformation, accelerating their growth and enhancing their competitive standing in the market.


The Value of Using AWS

Scalability: AWS’s vast array of services and solutions allows us to easily scale up or down based on demand. This flexibility is vital for managing costs and handling sudden increases in user traffic or data volume.

Security: Their comprehensive security capabilities, including encryption at rest and in transit, have helped us ensure the safety of our client’s data.

Innovation: They provide a broad set of machine learning and AI services, enabling us to add intelligence to our applications. From personalized experiences to predictive analytics, this innovation fuels our ability to stay ahead of the curve.

Reliability: AWS’s global infrastructure provides us with high availability and failover capabilities. This ensures that our apps remain accessible and functional, providing a seamless experience for users, regardless of their location.

Integration: Their services integrate well with each other, which helps streamline our workflows. For instance, we can easily integrate AWS’s database, storage, and compute services to create comprehensive solutions.

Cost-Effectiveness: A pay-as-you-go pricing model eliminates the need for heavy upfront investment in infrastructure. This enables us to experiment and innovate.

Real-Time Processing: We can process high volumes of data in real-time, enabling us to provide timely insights and responses. For instance, AWS’s support for MQTT, a lightweight messaging protocol, is key for our IoT-based projects.


In summary, AWS empowers us to deliver high-quality, secure, and innovative solutions that drive real value for our clients. It’s comprehensive and evolving service offerings enable us to stay agile, experiment with new technologies, and continually enhance our offerings.

Use Case 1

Energy Consumption Analysis: Maximizing Efficiency Through User Engagement

The goal of this project was to analyze plug load energy consumption, which accounts for up to 40% of total energy usage in inefficient buildings, with a focus on occupant behavior.

Data Collection Method: We employed Plugwise wireless smart meters attached to individual office appliances to gather detailed energy data. These meters captured electricity consumption data for each occupant at high-frequency 5-minute intervals.

Data Storage: The high-frequency data collected was directly stored in AWS. The power of AWS cloud services facilitated the handling of such high volumes of data and ensured its secure storage for further analysis.

Data Analysis: We employed Python algorithms to process and analyze the data stored in AWS, helping us to identify energy usage patterns and trends. This comprehensive analysis considered various factors, including the type of device, duration of use, and occupant behavioral patterns.

Dashboard Development: With the insights gained from our analysis, we developed an online dashboard featuring a set of Sustainability Metrics and an occupancy detection method. This dashboard provided a visual and interactive representation of the data, making it easier for clients to understand their energy consumption patterns and the impact of their behaviors.

AWS Role: AWS played a critical role in this project. Its robust cloud infrastructure facilitated the efficient collection, storage, and analysis of high-frequency data.

Our energy consumption analysis, underpinned by AWS technology and Plugwise smart meters, empowered clients to deepen their understanding of their energy usage patterns. This led to substantial energy savings and more sustainable practices.

Use Case 2

Mobile Sensor Data Analysis for Measurement Based Care Platform

The objective of this project was to develop a comprehensive measurement-based care platform for both research and clinical use. We harnessed mobile sensor data, including active and passive data, and the power of AWS to perform in-depth behavioral and cognitive analysis.

Data Collection Method: We collected data from a variety of sources:

  • Active Data: This includes information collected when users interact with the app, such as responses to surveys, cognitive test results, and environment tagging.
  • Passive Data: This comprises information collected in the background, even when the user is not using the app. Examples include GPS location, call and text logs, and exercise information.

Platform Design: Our platform, designed to cater to both research and clinical use, allows real-time monitoring, predictive analysis, and improved patient care, leveraging the rich insights extracted from the sensor data.

Data Storage: All sensor data was securely stored in AWS. The scalable and robust AWS cloud storage services were an ideal solution for handling and managing the high volume of sensor data.

AWS Role: AWS’s cloud services were integral at every stage of this project, from data collection to storage and analysis. Key services used included Amazon S3 for data storage, Amazon DocumentDB for scalable and reliable storage of processed data, Amazon SQS for effective message queue management, Amazon API Gateway for secure and managed API operations, and other reliable routing and failover mechanisms. These technologies were critical to the successful execution and robustness of our project.

Through our effective use of AWS services and mobile sensor data, we were able to create a robust, powerful measurement-based care platform. This platform provides significant insights into patient behavior and cognition, enhancing care decisions, improving patient outcomes, and increasing research effectiveness.

AWS Now and Into the Future

We are excited to continue leveraging AWS to drive further innovation and deliver even more value to our clients. We aim to explore more AWS services and stay current with the latest developments in the AWS ecosystem.

Our future with AWS looks promising and exciting as we delve deeper into the world of Machine Learning (ML) and Artificial Intelligence (AI). AWS provides a host of ML and AI services that we plan to incorporate into our projects to deliver more advanced, predictive, and intuitive solutions.

Services such as Amazon SageMaker for building, training, and deploying machine learning models at scale, and AWS AI services like Amazon Lex, Amazon Polly, and Amazon Rekognition for adding intelligent features like image and video analysis, natural language processing, personalized recommendations, and forecasting into applications, are on our radar.

We foresee leveraging these technologies to automate processes, gain insights from vast amounts of unstructured data, build intelligent applications, and offer predictive analytics, ultimately providing more value to our clients.