Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

2021 ◽  
Author(s):  
Narayanasamy Tamilarasan ◽  
S.B. Lenin ◽  
N. Jayapandian ◽  
P. Subramanian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Optimization Algorithm ◽  
Energy Stability ◽  
Wireless Sensor ◽  
Lifetime Maximization ◽  
Shuffled Frog Leaping

Distributed Network Lifetime Maximization with Newton Method in Static Wireless Sensor Networks

2013 ◽  
Vol 12 (5) ◽  
pp. 997-1003
Author(s):  
Tiaojuan Ren ◽  
Yourong Chen ◽  
Zhangquan Wang ◽  
Lizhe Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Newton Method ◽  
Network Lifetime ◽  
Wireless Sensor ◽  
Distributed Network ◽  
Lifetime Maximization ◽  
Static Wireless Sensor Networks

Lifetime Maximization Routing Based on Genetic Algorithm for Wireless Sensor Networks

2011 ◽  
Vol 230-232 ◽  
pp. 283-287
Author(s):  
You Rong Chen ◽  
Tiao Juan Ren ◽  
Zhang Quan Wang ◽  
Yi Feng Ping
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Lifetime Maximization

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

Network lifetime maximization and localized routing tradeoff in wireless sensor networks

2013 ◽  
Author(s):  
E. Uzun ◽  
A. Aksac ◽  
O. Ozturk ◽  
H. E. Kiziloz ◽  
D. Incebacak ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Wireless Sensor ◽  
Lifetime Maximization

scholarly journals An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2251
Author(s):  
Amir Masoud Rahmani ◽  
Saqib Ali ◽  
Mohammad Sadegh Yousefpoor ◽  
Efat Yousefpoor ◽  
Rizwan Ali Naqvi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Sensor Node ◽  
Area Coverage ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

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