Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

被引:0
作者
P. V. Pravija Raj
Ahmed M. Khedr
Zaher Al Aghbari
机构
[1] University of Sharjah,Department of Computer Science
[2] Zagazig University,Mathematics Department
来源
Wireless Networks | 2020年 / 26卷
关键词
Ant colony optimization (ACO); Data gathering (DG); Game theory (GT); Rendezvous points (RPs); Mobile sink (MS) trajectory; Wireless sensor network (WSN);
D O I
暂无
中图分类号
学科分类号
摘要
Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.
引用
收藏
页码:2983 / 2998
页数:15
相关论文
共 145 条
[31]  
Kaswan A(2019)An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs IEEE Access 7 26752-undefined
[32]  
Nitesh K(2016)A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks Wireless Networks 71 377-undefined
[33]  
Jana PK(2017)Energy-efficient clustering algorithm based on game theory for wireless sensor networks International Journal of Distributed Sensor Networks 17 2654-undefined
[34]  
Alsaafin A(2019)A coalitional game-theoretic framework for cooperative data exchange using instantly decodable network coding IEEE Access 74 6633-undefined
[35]  
Khedr AM(2018)Mobile sink based data collection for energy efficient coordination in wireless sensor network using cooperative game model Telecommunication Systems 69 528-undefined
[36]  
Aghbari ZA(2017)A game theoretic approach for balancing energy consumption in clustered wireless sensor networks Sensors 15 3484-undefined
[37]  
Ketshabetswe LK(2017)An improved ant colony optimization-based approach with mobile sink for wireless sensor networks The Journal of Supercomputing 15 169-undefined
[38]  
Zungeru AM(2019)Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks EURASIP Journal on Wireless Communications and Networking 26 150-undefined
[39]  
Mangwala M(2018)R ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints Applied Soft Computing 73 52-undefined
[40]  
Chuma JM(2019)A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink Wireless Networks undefined undefined-undefined