Analysis of wireless sensor networks with sleep mode and threshold activation

In order to reduce the energy consumption of wireless sensor networks and control the workload of necessary topology maintenance, the sleep mode and the threshold activation process in the energy saving strategy are considered. Combining with practice, factors such as environmental interferences and physical damages are considered. A repairable M/M/2 vacation queueing model with negative customers, feedback, N-strategy and working breakdown is established. Using quasi birth-and-death process and Gauss-Seidel iterative method, the expressions of performance indicators are given. Then, using MATLAB software for numerical analysis, the influence of system parameters on performance indicators is analysed. Finally, the social optimal parameters are found by constructing benefit functions. Under certain conditions, when $$\lambda =0.64$$ λ = 0.64 and $${{\beta }_{1}}=1.1$$ β 1 = 1.1 , the social benefit F can take the maximum value $$F=12.7729$$ F = 12.7729 .

[1]  Hamida Seba,et al.  New data aggregation approach for time-constrained wireless sensor networks , 2014, The Journal of Supercomputing.

[2]  Muttukrishnan Rajarajan,et al.  A1: An energy efficient topology control algorithm for connected area coverage in wireless sensor networks , 2012, J. Netw. Comput. Appl..

[3]  Seyed Ebrahim Dashti,et al.  Integration of geographic and hierarchical routing protocols for energy saving in wireless sensor networks with mobile sink , 2019, Wirel. Networks.

[4]  Michele Garetto,et al.  An Analytical Model for Wireless Sensor Networks with Sleeping Nodes , 2006, IEEE Transactions on Mobile Computing.

[5]  Shan Gao,et al.  Discrete-time GIX/Geo/1/N queue with negative customers and multiple working vacations , 2013 .

[6]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[7]  Natalia Kryvinska,et al.  Intelligent Network Analysis by Closed Queuing Models , 2004, Telecommun. Syst..

[8]  Zhan Shi,et al.  Design of wireless sensor network node for carbon monoxide monitoring , 2013, Telecommun. Syst..

[9]  Sayyed Majid Mazinani,et al.  MCFL: an energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network , 2018, Wirel. Networks.

[10]  Chao-Tung Yang,et al.  Lifetime elongation for wireless sensor network using queue-based approaches , 2010, The Journal of Supercomputing.

[11]  Wei Sun,et al.  Equilibrium and optimal balking strategies of customers in Markovian queues with multiple vacations and N-policy , 2016 .

[12]  Teng Gao,et al.  Clustering Algorithm Based on Fuzzy Comprehensive Evaluation for Wireless Sensor Networks , 2016, International Journal of Wireless Information Networks.

[13]  Amrit Mukherjee,et al.  Low-energy PSO-based node positioning in optical wireless sensor networks , 2019, Optik.

[14]  J. S. Dhillon,et al.  Dual head static clustering algorithm for wireless sensor networks , 2018 .

[15]  Muttukrishnan Rajarajan,et al.  Poly: A reliable and energy efficient topology control protocol for wireless sensor networks , 2011, Comput. Commun..

[16]  Jun Wang,et al.  Research and Improvement of Wireless Sensor Network Secure Data Aggregation Protocol Based on SMART , 2018, Int. J. Wirel. Inf. Networks.

[17]  Jau-Chuan Ke,et al.  On an unreliable-server retrial queue with customer feedback and impatience , 2018 .

[18]  A. Mahabub Basha,et al.  Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network , 2018, The Journal of Supercomputing.

[19]  Mohamed Elhoseny,et al.  Trust-based secure clustering in WSN-based intelligent transportation systems , 2018, Comput. Networks.

[20]  Xiaohong Jiang,et al.  On throughput capacity of large-scale ad hoc networks with realistic buffer constraint , 2017, Wirel. Networks.

[21]  D. K. Lobiyal,et al.  Energy Consumption Reduction in S-MAC Protocol for Wireless Sensor Network , 2018 .

[22]  Wuyi Yue,et al.  Advances in Queueing Theory and Network Applications , 2009 .

[23]  Doo Ho Lee,et al.  The M/G/1 queue with disasters and working breakdowns , 2014 .

[24]  Chakchai So-In,et al.  A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks , 2020, Expert Syst. Appl..

[25]  Raphaël Couturier,et al.  Multiround Distributed Lifetime Coverage Optimization protocol in wireless sensor networks , 2018, The Journal of Supercomputing.

[26]  Mohamed Elhoseny,et al.  Energy efficient collaborative proactive routing protocol for Wireless Sensor Network , 2018, Comput. Networks.

[27]  Abdorasoul Ghasemi,et al.  Joint spectrum load balancing and handoff management in cognitive radio networks: a non-cooperative game approach , 2016, Wirel. Networks.

[28]  Fathi E. Abd El-Samie,et al.  Node-power-based MAC protocol with adaptive listening period for wireless sensor networks , 2018 .