An Evolutionary Dynamic Approach for Designing Wireless Sensor Networks for Real Time Monitoring

Martins, Flávio Vinícius Cruzeiro; Carrano, Eduardo G.; Wanner, Elizabeth F.; Takahashi, Ricardo H. C.; Mateus, Geraldo Robson

doi:10.1109/ds-rt.2010.25

Cited by 5 publications

(12 citation statements)

References 15 publications

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“…All instances were generated at random, following a uniform probability distribution, in a 50 m × 50 m square. Besides, the 100-sensor instance is the same one that was used in previous with Martins et al (2010Martins et al ( , 2011.…”

Section: Resultsmentioning

confidence: 99%

“…The algorithm is employed to maximize the network lifetime, given a minimum coverage required (set by the user). This algorithm is a further development of the one proposed by Martins et al (2010) and, as in that algorithm, it is also built upon the concept of geometric operators. The main novelty of the algorithm presented here is related to the usage of the concepts of subspaces and descent directions in the construction of some new operators.…”

Section: A Review On the Wsn-dccpmentioning

confidence: 97%

“…In the conference paper Martins et al (2010), the authors proposed a dynamic GA for performing sensor scheduling in WSN. In this algorithm, the solutions are encoded as sequences of node activations which are always performed when it is necessary to enable more sensors for reaching the required coverage.…”

Section: A Review On the Wsn-dccpmentioning

confidence: 99%

“…The following statements are assumed to hold in this work: (i) Each node has a unique ID and its location is known; (ii) The application requires continuous monitoring and the data can be disseminated periodically; (iii) The traffic on the WSN is caused only by data of the considered application; and (iv) The set of sensor nodes can cover the target area with some redundancy. Under such assumptions, it becomes possible to model the WSN as a dynamic system (Martins et al, 2010), as represented in Equations (1) to (9).…”

Section: Wsn-dccp Statementmentioning

confidence: 99%

“…There are, in addition, noncontrolled events of deactivation of the nodes, due to the depletion of batteries. As discussed in Martins et al (2010), the network lifetime can be modeled as follows:…”

Section: Wsn-dccp Statementmentioning

confidence: 99%

See 4 more Smart Citations

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Martins

Carrano

Wanner

et al. 2014

Evolutionary Computation

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Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

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Section: Resultsmentioning

confidence: 99%

Section: A Review On the Wsn-dccpmentioning

confidence: 97%

Section: A Review On the Wsn-dccpmentioning

confidence: 99%

Section: Wsn-dccp Statementmentioning

confidence: 99%

Section: Wsn-dccp Statementmentioning

confidence: 99%

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On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Martins

Carrano

Wanner

et al. 2014

Evolutionary Computation

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Multiobjective planning of wireless local area networks (WLAN) using genetic algorithms

Lima

Carrano

Takahashi

2012

2012 IEEE Congress on Evolutionary Computation

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A new approach for wireless local area network (WLAN) installation planning is proposed in this paper. This approach is based on a multiobjective genetic algorithm and on greedy algorithms, and it is composed of two steps: network structure design and channel assignment. In the first step, the quantity, position and load balance of the access points are planned taking into account a minimum coverage level, the AP capacity and the traffic demand in the WLAN. In the second step, the channel of each access point is assigned in such a way that the network presents minimal interference and high throughput. An approximation of the efficient solution set, taking into account two design criteria, is delivered by the proposed algorithm. These solutions can be used to provide cost reduction and quality improvement on WLAN design. Results obtained by the proposed algorithm in two reasonable scenarios are presented in order to evaluate its efficacy.U.S. Government work not protected by U.S.

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Distance based NSGA-II for earliness and tardiness minimization in Parallel Machine Scheduling

Tepedino¹,

Takahashi

Carrano

2013

2013 IEEE Congress on Evolutionary Computation

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An Evolutionary Dynamic Approach for Designing Wireless Sensor Networks for Real Time Monitoring

Cited by 5 publications

References 15 publications

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Multiobjective planning of wireless local area networks (WLAN) using genetic algorithms

Distance based NSGA-II for earliness and tardiness minimization in Parallel Machine Scheduling

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