Introduction

Recently, at the international convention MIPRO 2021, I presented the full research paper titled "Leveraging Smartphones for Distributed Global Navigation Satellite System Post Processing". The research span for 6 months and I was the main author of the paper working together with my appreciated co-authors.

Brief walkthrough of the paper

After the conference proceedings, a published version of the paper will be available, so I would give you a brief walkthrough and introduction.

The main starting point of the research was the final course project for Parallel and Distributed Computing taught by Vladimir Zdraveski at FCSE. The starting point was Statista's analysis on the daily usage of smartphones which is 13% of the time. The next step was to find an applicable use case since we didn't want this research to solve an imagined problem.

The above-mentioned process defined our research hypothesis:

Can we take advantage to use smartphones while they are idle for GNSS data post-processing?

To prove the hypothesis we started architecting a system, so at the core we have:

• Clients - the producers of raw data,
• Post-processing provider - the entity which receives the raw data produced by the clients. Responsible for orchestrating the post-processing, and managing the pool of workers,
• Smartphone workers - managed devices that process batches of the raw data.

The client produces raw GNSS data and sends that data to the post-processing provider. When the provider receives the data, they are identified into the system and are ready for processing. The provider utilizes it’s reducer to split the data into smaller batches, each batch is send to a worker from the pool of available workers. Each worker upon reception of the data, starts the processing algorithms which are embedded in the software provided by the Post-processing provider. As the workers complete the tasks, they send the processed data back to the provider, responsible for keeping track of the overall process, and appends each completed batch to the data for the particular job. When the workers finish, the data is send to the appropriate client.

After implementing a proof-of-concept, we used it to show that the post-processing of GNSS data, can be significantly improved. In this case we have an improvement of 5317% compared with traditional post-processing providers, and 1595% improvement compared to in-house data post-processing.

Taking a step back

This blog post is not primary intended to showcase the research (the paper serves that purpose), but indeed to emphasize the word, term, process called research.

In my example, this started as a course project in which I put all of my focus, available knowledge and resources at the time being and resulted in one of my greatest achievements. This principle does not only apply to academia, instead it applies to every aspect of our life.

Tireless work, perseverance and getting into the smallest and most delicate detail or edge case are necessary to achieve top results in any field regardless of whether and what kind of recognition will be received for it. That way of working will make us leaders, not leaders in a company, organization, community, but leaders on our lives and our skillset.

How CodeChem fits into this story?

At the beginning of the research I had never heard of CodeChem, but during the time of my research I participated in Open Day 2021 and shortly after I started my internship. Alongside the learning process involved and the number of new concepts I was introduced, which rendered a never growing list, one of the things that fascinated me was the amount of research and effort for deep understanding put by the team for every new challenge that came along the way. Regardless of the project or the task, enormous thinking process and research is done by every member of the team, starting from a task estimated with couple of story points to a complex system being developed by several teams. That's why I am honored to be part of CodeChem.