Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

2020 ◽  
Vol 35 (2) ◽  
pp. 96
Author(s):  
R. Leela Velusamy ◽  
A. Kavitha
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Large Range ◽  
Hybrid Approach ◽  
Wireless Sensor ◽  
Energy Aware

scholarly journals An energy aware scheme for layered chain in underwater wireless sensor networks using genetic algorithm

2021 ◽  
Vol 11 (5) ◽  
pp. 4272
Author(s):  
Sihem Souiki ◽  
Sidi Mohamed Hadj Irid ◽  
Mourad Hadjila
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Energy Aware ◽  
Chain Transmission ◽  
Selection Of

Extending the network lifetime is a very challenging problem that needs to be taken into account during routing data in wireless sensor networks in general and particularly in underwater wireless sensor networks (UWSN). For this purpose, the present paper proposes a multilayer chain based on genetic algorithm routing (MCGA) for routing data from nodes to the sink. This algorithm consists to create a limited number of local chains constructed by using genetic algorithm in order to obtain the shortest path between nodes; furthermore, a leader node (LN) is elected in each chain followed by constructing a global chain containing LNs. The selection of the LN in the closest chain to the sink is as follows: Initially, the closest node to sink is elected LN in this latter because all nodes have initially the same energy value; then the future selection of the LN is based on the residual energy of the nodes. LNs in the other chains are selected based on the proximity to the previous LNs. Data transmission is performed in two steps: intra-chain transmission and inter-chain transmission. Furthermore, MCGA is simulated for different scenarios of mobility and density of nodes in the networks. The performance evaluation of the proposed technique shows a considerable reduction in terms of energy consumption and network lifespan.

scholarly journals Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks

2012 ◽  
Vol 8 (10) ◽  
pp. 790459 ◽  
Author(s):  
Placido Rogerio Pinheiro ◽  
Andre Luis Vasconcelos Coelho ◽  
Alexei Barbosa Aguiar ◽  
Alvaro de Menezes Sobreira Neto
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Sensor Networks ◽  
Optimization Problem ◽  
Hybrid Approach ◽  
Wireless Sensor ◽  
Coverage And Connectivity ◽  
Mathematical Programming Problems

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.

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