TY - JOUR
T1 - A hybrid multiobjective evolutionary approach for improving the performance of wireless sensor networks
AU - Martins, Flávio V.C.
AU - Carrano, Eduardo G.
AU - Wanner, Elizabeth F.
AU - Takahashi, Ricardo H.C.
AU - Mateus, Geraldo R.
PY - 2011/3
Y1 - 2011/3
N2 - The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.
AB - The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.
KW - Evolutionary algorithms
KW - multiobjective optimization
KW - performance evaluation
KW - wireless sensor networks (WSNs)
UR - https://ieeexplore.ieee.org/document/5482092
UR - http://www.scopus.com/inward/record.url?scp=79251607125&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2010.2048897
DO - 10.1109/JSEN.2010.2048897
M3 - Article
AN - SCOPUS:79251607125
SN - 1530-437X
VL - 11
SP - 545
EP - 554
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 3
M1 - 5482092
ER -