Wireless Sensor Networks - Insights and Innovations, Oct 4, 2017
A wireless sensor network's lifetime is influenced directly by the sensors power management that ... more A wireless sensor network's lifetime is influenced directly by the sensors power management that composes the network. The models applied to the problem aims to optimize the energy usage managing the sensors activation in time intervals, activating only the minimum number of sensors respecting the coverage and connectivity restrictions. However, this problem's class has a significant computational complexity and many applications. It is necessary to implement methodologies to find the optimal solution, increasing the network's size, becoming closer to the real ones. This research's objective is to present a method based on a Partition Heuristic aggregating the Generate and Solve method, improving the results, and increasing the network's instances size, while maintaining the flexibility and reliability when applied to the homogeneous wireless sensors networks with coverage and connectivity restrictions.
International Journal of Distributed Sensor Networks, 2012
The integrative collaboration of genetic algorithms and integer linear programming as specified b... more The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.
International Journal of Distributed Sensor Networks, 2013
This paper presents an integer linear programming model devoted to optimize the energy consumptio... more This paper presents an integer linear programming model devoted to optimize the energy consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off specifics sets of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. By resorting to this model, it is possible to provide extra lifetime to heterogeneous wireless sensor networks, reducing their setup and maintenance costs. This is an important issue to be considered when deploying sensor devices in hostile and inaccessible environments.
The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic compon... more The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve cutting and packing problems, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. In this paper, we present an extended version of GS, focusing primarily on the concept of a new Density Control Operator (DCO). The role of this operator is to adaptively control the dimension of the reduced instances in such a way as to allow a much steadier progress towards a better solution, thereby avoiding premature convergence. In order to assess the potentials of this novel version of the GS methodology, we conducted computational experiments on a set of difficult benchmark instances of the constrained non-guillotine cutting problem. The results achieved are quantitatively and qualitatively discussed in terms of effectiveness and efficiency, showing that the proposed variant of the GS hybridization framework is highly suitable when effectiveness is a major requirement.
GSM Network Designs usually offers big challenges for achieving an efficient cost while respectin... more GSM Network Designs usually offers big challenges for achieving an efficient cost while respecting the complex combinatorial technical constraints. This networks have hundreds or thousands BTS (Base Transceiver Station). They have their traffic grouped in hubs, then in BSC (Base Station Controller) nodes to reach the MSC. Hubs must be elected within the BTS set and BSC nodes have to be geographically allocated in the available sites. Also, the number and model of these BSC impact in the overall cost while the distances affect the transmission costs. This paper presents a mathematical model for designing a GSM network from the BTS lower layer until the MSC layer.
This paper presents an applicability analysis over a novel integer programming model devoted to o... more This paper presents an applicability analysis over a novel integer programming model devoted to optimize power consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off a specific set of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. As the problem instances grow the time spent to the execution turns impracticable.
Wireless Sensor Networks - Insights and Innovations, Oct 4, 2017
A wireless sensor network's lifetime is influenced directly by the sensors power management that ... more A wireless sensor network's lifetime is influenced directly by the sensors power management that composes the network. The models applied to the problem aims to optimize the energy usage managing the sensors activation in time intervals, activating only the minimum number of sensors respecting the coverage and connectivity restrictions. However, this problem's class has a significant computational complexity and many applications. It is necessary to implement methodologies to find the optimal solution, increasing the network's size, becoming closer to the real ones. This research's objective is to present a method based on a Partition Heuristic aggregating the Generate and Solve method, improving the results, and increasing the network's instances size, while maintaining the flexibility and reliability when applied to the homogeneous wireless sensors networks with coverage and connectivity restrictions.
International Journal of Distributed Sensor Networks, 2012
The integrative collaboration of genetic algorithms and integer linear programming as specified b... more The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.
International Journal of Distributed Sensor Networks, 2013
This paper presents an integer linear programming model devoted to optimize the energy consumptio... more This paper presents an integer linear programming model devoted to optimize the energy consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off specifics sets of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. By resorting to this model, it is possible to provide extra lifetime to heterogeneous wireless sensor networks, reducing their setup and maintenance costs. This is an important issue to be considered when deploying sensor devices in hostile and inaccessible environments.
The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic compon... more The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve cutting and packing problems, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. In this paper, we present an extended version of GS, focusing primarily on the concept of a new Density Control Operator (DCO). The role of this operator is to adaptively control the dimension of the reduced instances in such a way as to allow a much steadier progress towards a better solution, thereby avoiding premature convergence. In order to assess the potentials of this novel version of the GS methodology, we conducted computational experiments on a set of difficult benchmark instances of the constrained non-guillotine cutting problem. The results achieved are quantitatively and qualitatively discussed in terms of effectiveness and efficiency, showing that the proposed variant of the GS hybridization framework is highly suitable when effectiveness is a major requirement.
GSM Network Designs usually offers big challenges for achieving an efficient cost while respectin... more GSM Network Designs usually offers big challenges for achieving an efficient cost while respecting the complex combinatorial technical constraints. This networks have hundreds or thousands BTS (Base Transceiver Station). They have their traffic grouped in hubs, then in BSC (Base Station Controller) nodes to reach the MSC. Hubs must be elected within the BTS set and BSC nodes have to be geographically allocated in the available sites. Also, the number and model of these BSC impact in the overall cost while the distances affect the transmission costs. This paper presents a mathematical model for designing a GSM network from the BTS lower layer until the MSC layer.
This paper presents an applicability analysis over a novel integer programming model devoted to o... more This paper presents an applicability analysis over a novel integer programming model devoted to optimize power consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off a specific set of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. As the problem instances grow the time spent to the execution turns impracticable.
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Papers by Alexei Aguiar