This paper presents a HaLow wireless simulator, which provides a simulated environment based on the IEEE 802.11ah standard. The simulator allows generating metrics to evaluate network performance considering groups generated by the Restricted Access Window (RAW) mechanism. Users can also provide grouping configurations through input files for testing with custom grouping algorithms. Evaluation results show that the simulator scales efficiently while maintaining low memory footprint and execution time, even in large-scale network scenarios with 1,000 stations.

Summary Backtracking branch‐and‐prune (BP) algorithms and their variants are exhaustive search tree techniques widely employed to solve optimization problems in many scientific areas. However, they characteristically often demand significant amounts of computing power for problem sizes representative of real‐world scenarios. Given that their search domains can often be partitioned, these algorithms are frequently designed to execute in parallel by harnessing distributed computing systems. However, to achieve efficient parallel execution times, an effective strategy is required to balance the nonuniform partition workloads across the available resources. Furthermore, with the increasing integration of servers with heterogeneous resources and the adoption of resource sharing, balancing workloads is becoming complex. This paper proposes a strategy to execute parallel BP algorithms more efficiently on even shared or heterogeneous distributed systems. The approach integrates a self‐adjusting dynamic partitioning method in the BP algorithm with a dynamic scheduler, provided by an application middleware, which manages the parallel execution while addressing any issues of imbalance. Empirical results indicate better scalability with efficiencies above 90% for instances of an application case study for the discretizable molecular distance geometry problem (DMDGP). Improvements of up to 38% were obtained in execution speed‐ups compared to a more traditional parallel BP implementation for DMDGP.

This paper presents an approach for detection and quantification, with low latency, of the flow of leakage bubbles, in sub-surfaces, making use of video recorded by remote underwater vehicle using only image analysis and under the premise of no overlapping bubbles. Implementation details are presented allowing its trial and reproduction. Results are confronted with videos acquired in a laboratory under controlled conditions and in real operational situation from literature, showing great efficiency in terms of processing time and all other important aspects for pipeline inspections, considering environment and safety in the oil industry.

Evaluating wireless links is a common task for many control mechanisms. However, the inherent variability of those estimates negatively impacts network performance. To reduce this variability, the Hypothesis Test Estimator (HTE) was recently developed as an alternative to the commonly employed moving averages. Performance analyses carried out in recent works found that HTE returns more stable estimates at the cost of a typically larger average estimate error. This work uses numerical simulations to complement the previous analyses, but now under the perspective of convergence time –i.e., how long it takes for actual changes in the link quality to be reflected in the estimates. Our results indicate that HTE has, in general, a better convergence time than the moving averages. They also show that further improving HTE's convergence time is not trivial, as simple variations of the method that aim to improve convergence do not result in significant gains.