Novel dynamic source routing protocol (DSR) based on genetic algorithm‐bacterial foraging optimization (GA‐BFO)

The goal of mobile ad hoc network (MANET) routing protocol is to establish a correct and effective route with minimal control overhead and bandwidth consumption. Dynamic source routing protocol (DSR) is a simple on‐demand routing protocol designed specifically for MANETs. The choice of routing path only adopts the simplest minimum hop count algorithm, while it did not take the node energy into account. In order to improve the control overhead of the network, we comprehensively consider the node energy information when searching the routes to the destination nodes. We propose a genetic algorithm (GA)‐bacterial foraging optimization algorithm to perform the selection of the optimal routing. After searching out multiple routes to the destination node, the paths are initialized then the GA algorithm is started. This algorithm quickly finds the positions of the maximum probability optimal paths, which are the initial positions of bacteria for the bacteria foraging optimization (BFO) algorithm. Through using the BFO algorithm, it is easy to search out the extreme value and the optimal path in order to compensate for the poor accuracy of GA algorithm. Our proposed optimized strategy improves the routing selection algorithm without change the complexity of DSR and proves the convergence of the algorithm to the global optimal solution. The simulation shows that the proposed algorithm is feasible and applicable and also has better experimental result.

[1]  Ting Zhang,et al.  Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education , 2017, J. Netw. Comput. Appl..

[2]  Imad Mahgoub,et al.  Leveraging MANET-Based Cooperative Cache Discovery Techniques in VANETs: A Survey and Analysis , 2017, IEEE Communications Surveys & Tutorials.

[3]  Jun Li,et al.  Probabilistic Small-Cell Caching: Performance Analysis and Optimization , 2017, IEEE Transactions on Vehicular Technology.

[4]  Jaswinder Singh,et al.  Energy efficient secured routing protocol for MANETs , 2017, Wirel. Networks.

[5]  Mohammad Hossein Anisi,et al.  Review on MANET Based Communication for Search and Rescue Operations , 2017, Wirel. Pers. Commun..

[6]  Ali Ghaffari,et al.  Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms , 2017, Wirel. Networks.

[7]  Xiaojun Yan,et al.  Statistical Analysis of Path Losses for Sectorized Wireless Networks , 2017, IEEE Transactions on Communications.

[8]  Sunil Pathak,et al.  An optimized stable clustering algorithm for mobile ad hoc networks , 2017, EURASIP J. Wirel. Commun. Netw..

[9]  Sunil Kumar,et al.  Direct trust-based security scheme for RREQ flooding attack in mobile ad hoc networks , 2017 .

[10]  Albert Y. Zomaya,et al.  Uplink Performance Analysis of Dense Cellular Networks With LoS and NLoS Transmissions , 2016, IEEE Transactions on Wireless Communications.

[11]  Tarik Taleb,et al.  On Performance Modeling for MANETs Under General Limited Buffer Constraint , 2017, IEEE Transactions on Vehicular Technology.

[12]  Xinbing Wang,et al.  Geographic Routing in Multilevel Scenarios of Vehicular Ad Hoc Networks , 2016, IEEE Transactions on Vehicular Technology.

[13]  Weifa Liang,et al.  Efficient Scheduling of Multiple Mobile Chargers for Wireless Sensor Networks , 2016, IEEE Transactions on Vehicular Technology.

[14]  Xiang Wang,et al.  Novel Quick Start (QS) method for optimization of TCP , 2016, Wirel. Networks.

[15]  Ashutosh Sharma,et al.  Performance comparison and detailed study of AODV, DSDV, DSR, TORA and OLSR routing protocols in ad hoc networks , 2016, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC).

[16]  Zhen Ma,et al.  New agent-based proactive migration method and system for Big Data Environment (BDE) , 2015 .

[17]  Xiang Wang,et al.  A novel multicast routing method with minimum transmission for WSN of cloud computing service , 2015, Soft Comput..

[18]  Wang Min-l,et al.  Research on Convergence of Genetic Algorithm , 2015 .

[19]  Xiang Wang,et al.  A Novel Approach to Mapped Correlation of ID for RFID Anti-Collision , 2014, IEEE Transactions on Services Computing.

[20]  Bijay Ketan Panigrahi,et al.  A Multiobjective Bacterial Foraging Algorithm to Solve the Environmental Economic Dispatch Problem , 2014 .

[21]  Mousa Shamsi,et al.  Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO) , 2014, J. Comput. Sci..

[22]  Guang Li,et al.  An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks , 2014, IEEE Transactions on Industrial Informatics.

[23]  Ma Jie-lian.  Improvements based on the degree of congestion and energy state of the DSR protocol , 2014 .

[24]  De-gan Zhang,et al.  A kind of novel method of service-aware computing for uncertain mobile applications , 2013, Math. Comput. Model..

[25]  Wenbo Dai,et al.  A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet of Things (IOT) , 2012, Comput. Math. Appl..

[26]  Mao Yong-jun.  Application of Improved Ant Colony Algorithm in WSN Routing Protocol , 2009 .

[27]  Ajith Abraham,et al.  Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications , 2009, Foundations of Computational Intelligence.

[28]  Zhao Xiqing.  Optimization of DSR Routing Protocol Based on Genetic Algorithm , 2008 .

[29]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..