A hybrid approach for the optimization of quality of service metrics of WSN

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN.

[1]  Jyoteesh Malhotra,et al.  EEICCP—Energy Efficient Protocol for Wireless Sensor Networks , 2013 .

[2]  Jalel Ben-Othman,et al.  Energy efficient and QoS based routing protocol for wireless sensor networks , 2010, J. Parallel Distributed Comput..

[3]  Chenn-Jung Huang,et al.  A power-efficient routing protocol for underwater wireless sensor networks , 2011, Appl. Soft Comput..

[4]  Khushboo Tripathi,et al.  Performance of DSDV Protocol over Sensor Networks , 2010 .

[5]  Yi Shang,et al.  A biologically-inspired clustering protocol for wireless sensor networks , 2007, Comput. Commun..

[6]  Bara'a Ali Attea,et al.  A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks , 2012, Appl. Soft Comput..

[7]  Yasushi Kambayashi,et al.  A Review of Routing Protocols Based on Ant-Like Mobile Agents , 2013, Algorithms.

[8]  Pratyay Kuila,et al.  Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: an improved genetic algorithm based approach , 2018, Wirel. Networks.

[9]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

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

[11]  Uthman A. Baroudi,et al.  Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart grid , 2018, J. Ambient Intell. Humaniz. Comput..

[12]  Qilian Liang,et al.  An energy-efficient protocol for wireless sensor networks , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[13]  Ahmed Farouk,et al.  K-Coverage Model Based on Genetic Algorithm to Extend WSN Lifetime , 2017, IEEE Sensors Letters.

[14]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[15]  Umakant P. Kulkarni,et al.  Context-Aware Information Processing in Visual Sensor Network , 2012, IAIT 2012.

[16]  Sunil Kr. Jha,et al.  An energy optimization in wireless sensor networks by using genetic algorithm , 2018, Telecommun. Syst..

[17]  Xiaohui Yuan,et al.  A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity , 2016, Journal of Network and Systems Management.

[18]  Sengar Abhishek,et al.  Performance Evaluation of AODV and DSDV Routing protocols for Ad-hoc Networks , 2012 .

[19]  Ganapati Panda,et al.  Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques , 2009, Expert Syst. Appl..