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- A spectral clustering algorithm for intelligent grouping in Dense Wireless NetworksPublication . Guedes, Bruna Toledo; Passos, Diego; Passos, Fernanda G. O.The density of wireless networks has been increasing with the popularization of mobile devices. Dense wireless networks (DWN) present challenges such as the current spectral scarcity and the growing demand for capacity. The Restricted Access Window (RAW) mechanism was introduced by the IEEE 802.11ah amendment to improve DWN performance. RAW restricts the number of stations that can access the channel by arbitrarily separating them into groups. K-Means clustering has shown potential to find more efficient groups using the geographical coordinates of each station. However, due to the mobile and dynamic nature of such networks, location information is difficult to obtain in practice. In this paper, we consider the use of spectral clustering to increase the performance of DWN with hidden terminals. We discuss how a spectral clustering algorithm that generates RAW groups can be implemented in practice without the geographic location of each node. We also compare the performance of the spectral clustering algorithm with the standard grouping method used in IEEE 802.11ah, with the K-Means clustering (i.e., based on node location information), and with the hidden matrix -based regrouping (HMR) algorithm. Simulation results considering several density levels, different traffic patterns, and different propagation models indicate that spectral clustering significantly outperforms both the standard grouping and HMR in terms of collision rate, throughput, and delay. It also closely approximates - and sometimes surpasses - the performance of the K-Means clustering while being much more practical to implement because it does not require knowledge on nodes' geographical coordinates.
- An autonomic parallel strategy for exhaustive search tree algorithms on shared or heterogeneous systemsPublication . Gonçalves de Oliveira Passos, Fernanda; Rebello, Vinod E. F.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.