The paper, authored by Rodrigo Almeida Bezerra (UFBA), Luiz Fernando Bittencourt (UNICAMP), Leandro Villas (UNICAMP), and Maycon Peixoto (UFBA), is set within the intersection of the fields of IoT and Brain-Computer Interfaces (BCI).
The study simulates the operation of a BCI. In this specific case, the BCI involves a game that connects via electroencephalography—a device that, when placed on the head, captures brain signals to establish a connection between the user’s brain and the game.
In this context, potential delays can negatively impact the user experience. Edge computing comes into play, then, to enable a fast, low-latency user experience. To achieve this, processing must take place at the edge; that is why the shift from the cloud to the edge is important for specific applications like this one.
In the case of this work, there is an additional dimension: user mobility. The user is moving from one place to another, and the application modules need to move along with the user. User mobility, therefore, complicates the interactions between the system’s elements; the edge could be, for example, a device in the user’s home, and when the user moves away, latency increases.
It is in this context that the MX algorithm solution comes into play, to track the user.
What is the MI-X algorithm, and what is its main objective?
This algorithm, developed by the researchers, is a strategy for migrating game applications from the fog to the cloud.
Mobility Impact-X (MI-X) is a dynamic allocation algorithm and a mobility-aware modular allocation strategy designed to operate in Hierarchical Fog Computing architectures. It was specifically designed to manage modular, latency-sensitive Brain-Computer Interface (BCI) applications, such as electroencephalogram-based games (EEG Games), within the cloud-fog-edge continuum.
Its primary goal is to minimize application latency and response times while simultaneously optimizing the use of computational resources. To achieve this, MI-X seeks to dynamically balance the following factors:
Computational proximity to the user: Prioritizing execution at the edge (cloudlets) to reduce perceived latency.
Communication cost: Assessing the impact of distributing or migrating interdependent modules across the network.
Operational stability: Avoiding abrupt migrations and long hierarchical hops that could cause system instability.
What sets MI-X apart is its ability to integrate the application’s modular structure with mobility management. This allows the algorithm to keep interdependent components physically close to one another within the infrastructure, preventing network costs from negating individual processing gains and ensuring consistent performance even amid continuous user mobility.
Challenges
Edge applications are close to the user but have limited resources; cloud applications, on the other hand, have more resources but are farther from the user, resulting in longer response times.
In urban mobility scenarios, edge resources can become saturated. The developed solution analyzes the impact of each option: it performs a search and attempts to balance latency, computational capacity, communication costs, etc.
An interesting fact about the development of this solution is that the history of this algorithm includes others, built in 2019, 2020, and 2023, which have evolved over time; MI-X is the first in the series to offer the possibility of reducing latency.
The researchers emphasize that it is not enough to simply place everything at the edge; processing must intelligently follow the user, tracking their movements.
Work continues to evolve through the research of master’s student Rodrigo Bezerra (UFBA)—now with a new strategy called HERMES (Heuristic Resource Management for Edge Systems). This approach builds on MI-X by considering not only where application modules should be executed as the user moves, but also when a migration is actually worthwhile. HERMES analyzes the impact of each possible reallocation, taking into account latency, resource availability, and the cost of transferring modules, thereby avoiding unnecessary migrations and the so-called “back-and-forth” effect between the edge, fog, and cloud. Thus, the research evolves from the idea of simply following the user to that of intelligently and stably orchestrating the entire path between the edge, the fog, and the cloud.