Laura Silva de Assis

The design of Wireless Sensor Networks (WSNs) involves balancing conflicting objectives related to sensing quality, connectivity, and resource consumption. This becomes more challenging in heterogeneous networks, where sensor nodes differ in capability, coverage, and cost. This paper addresses the Sensor Allocation Problem through a multi-objective integer linear programming formulation. The proposed model considers a set of predefined candidate locations for node deployment and multiple transceiver types with different communication ranges and energy consumption characteristics, using data from commercial devices. An e-constraint approach is used to minimize total average energy consumption subject to minimum coverage requirements, thereby generating Pareto-efficient network topologies. The model is evaluated through 225 computational experiments with different monitoring-region sizes and candidate-location densities. The results highlight that the proposed formulation can generate feasible and connected heterogeneous WSN topologies and capture the trade-off between coverage requirements and energy consumption.

Wireless Sensor Networks (WSNs) have become ubiquitous across various domains, from household applications to industrial grounds, and critical areas like emergency response and environmental monitoring. While numerous optimization techniques have emerged in the literature to enhance network performance by strategically allocating sensor nodes, most studies in this field rely on heuristic methods. Designing the topology of WSNs is crucial for optimizing node energy consumption, connectivity, and coverage area. In this work, two methodologies are proposed to minimize the overall energy consumption of the network. The first is an exact method using a Mixed Integer Linear Programming (MILP) model. The second is an optimization method based on genetic algorithms (GAs). In the computational experiments, instances were generated based on grid-like networks, some of which assuming highly irregular topologies. The results have shown that the exact approach found optimal solutions for some instances, up to five thousand nodes. In contrast, the GA obtained high-quality solutions for instances with more than 18 thousand nodes in reasonable computational times.

Spectral clustering techniques depend on the eigenstructure of a similarity matrix to assign data points to clusters, so that points within the same cluster exhibit high similarity and are compared to those in different clusters. This work aimed to develop a spectral method that could be compared to clustering algorithms that represent the current state of the art. This investigation conceived a novel spectral clustering method, as well as five policies that guide its execution, based on spectral graph theory and embodying hierarchical clustering principles. Computational experiments comparing the proposed method with six state-of-the-art algorithms were undertaken in this study to evaluate the clustering methods under scrutiny. The assessment was performed using two evaluation metrics, specifically the adjusted Rand index, and modularity. The obtained results furnish compelling evidence, indicating that the proposed method is competitive and possesses distinctive properties compared to those elucidated in the existing literature. This suggests that our approach stands as a viable alternative, offering a robust choice within the spectrum of available same-purpose tools.