Mobile Sink-Based Data Aggregation Protocol Using Genetic Algorithm Employing Binary String (MSDAP-GABS) for Extended Network Lifetime in Wireless Sensor Networks

2021 ◽  
pp. 203-212
Author(s):  
Amiya Bhusan Bagjadab ◽  
Santosh Majhi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Aggregation ◽  
Mobile Sink ◽  
Binary String ◽  
Wireless Sensor

scholarly journals GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 43
Author(s):  
Muhammad K. Shahzad ◽  
S. M. Riazul Islam ◽  
Mahmud Hossain ◽  
Mohammad Abdullah-Al-Wadud ◽  
Atif Alamri ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Smart Cities ◽  
Hot Spot ◽  
Time Instant ◽  
Wireless Sensor ◽  
Injection Attacks ◽  
Fixed Path

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

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.

Energy-Aware Data Aggregation for Grid-Based Wireless Sensor Networks with a Mobile Sink

2007 ◽  
Vol 43 (4) ◽  
pp. 1539-1551 ◽  
Author(s):  
Nen-Chung Wang ◽  
Yung-Fa Huang ◽  
Jong-Shin Chen ◽  
Po-Chi Yeh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Mobile Sink ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Grid Based

scholarly journals Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877468 ◽  
Author(s):  
Edson Ticona-Zegarra ◽  
Rafael CS Schouery ◽  
Leandro A Villas ◽  
Flávio K Miyazawa
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Aggregation ◽  
Energy State ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Network Nodes ◽  
Battery Energy ◽  
Routing Tree

Wireless sensor networks consist of hundreds or thousands of nodes with limited energy resources, and thus, efficient use of energy is necessary for these networks. Given that transmissions are the most energy-demanding operation, routing algorithms should consider efficient use of transmissions in their designs in order to extend the network lifetime. To tackle these challenges, a centralized algorithm is proposed, called improved continuous enhancement routing (ICER), for computing routing trees of refined quality, based on data aggregation while being aware of the battery energy state. Comparisons between ICER and other known solutions in the literature are performed. Our experiments show that ICER is able to ensure, on average, the survival of 99.6% and the connectivity of 99.3% of the network nodes compared to 90.2% and 72.4% in relation to the best-compared algorithm. The obtained results show that ICER significantly extends the network lifetime while maintaining the quality of the routing tree.

scholarly journals Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yourong Chen ◽  
Zhangquan Wang ◽  
Tiaojuan Ren ◽  
Yaolin Liu ◽  
Hexin Lv
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Optimization Model ◽  
Data Gathering ◽  
Mobile Sink ◽  
Path Selection ◽  
Wireless Sensor ◽  
Lifetime Optimization

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

Sign in / Sign up

Export Citation Format

Share Document