Self-Organization Based Clustering in MANETs Using Zone Based Group Mobility

The dynamic network topology and mobile nature of nodes can cause challenges regarding connectivity and routing. Clustering in mobile ad-hoc networks (MANETs) is one of the effective ways to organize a network according to the network topological changes. In this paper, we propose a self-organization-based clustering scheme in MANET using zone-based group mobility to improve scalability and stability of overall network. This proposed algorithm utilizes the bio-inspired behavioral study of birds flocking for the formation and maintenance of clusters in MANETs. A dynamic mechanism for cluster size management is taken into account to reduce network congestion and improve the performance of the MANETs in group mobility. For proper use of resources and to reduce extra energy consumption, an algorithm is also proposed to handle the isolated nodes. Simulation result shows that proposed algorithm reduces the energy consumption and improves the network lifetime along with more robustness.

[1]  Keping Long,et al.  On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches , 2014, IEEE Communications Surveys & Tutorials.

[2]  Keping Long,et al.  Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey , 2013, IEEE Wireless Communications.

[3]  Mauro Leoncini,et al.  Self Organization and Self Maintenance of Mobile Ad Hoc Networks through Dynamic Topology Control , 2009, WADS.

[4]  Xiaoling Wu,et al.  RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks , 2014, Int. J. Distributed Sens. Networks.

[5]  Samir Al-Khayatt,et al.  An efficient weighted distributed clustering algorithm for mobile ad hoc networks , 2010, The 2010 International Conference on Computer Engineering & Systems.

[6]  Yang Tao,et al.  An Enhanced Maximum Stability Weighted Clustering Algorithm in Ad Hoc Network , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[7]  Thierry Mora,et al.  Local equilibrium in bird flocks , 2015, Nature Physics.

[8]  Peng Xu,et al.  Topology control algorithm based on overlapping clustering , 2010, 2010 International Conference on Networking, Sensing and Control (ICNSC).

[9]  Abdelhak Bentaleb,et al.  A new topology management scheme for large scale mobile ad hoc networks , 2015, 2015 IEEE International Conference on Electro/Information Technology (EIT).

[10]  Bo Zhou,et al.  Parallel simulation of group behaviors , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[11]  J. Sathiamoorthy,et al.  Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs , 2017, Wirel. Networks.

[12]  Xu Xu,et al.  Self-organization approaches for optimization in cognitive radio networks , 2014, China Communications.

[13]  Xiaonan Wang,et al.  Constructing a MANET Based on Clusters , 2013, Wireless Personal Communications.

[14]  Zhou Yiqing,et al.  Advanced coverage optimization techniques for small cell clusters , 2015, China Communications.

[15]  Xuesong Qiu,et al.  Group mobility based clustering algorithm for mobile ad hoc networks , 2015, 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[16]  M. P. Sebastian,et al.  (k, r)-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks , 2011, IET Commun..

[17]  Qi Zhang,et al.  Bio-inspired low-complexity clustering in large-scale dense wireless sensor networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[18]  Li-Chen Fu,et al.  An ant colony optimization algorithm for multi-objective clustering in mobile ad hoc networks , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[19]  Subramaniam Shamala,et al.  Neighbor-Based Dynamic Connectivity Factor Routing Protocol for Mobile Ad Hoc Network , 2016, IEEE Access.

[20]  Charalampos Konstantopoulos,et al.  Lowest-ID with adaptive ID reassignment: a novel mobile ad-hoc networks clustering algorithm , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[21]  Tanzila Saba,et al.  Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function , 2017, IEEE Access.

[22]  Ahmad Khademzadeh,et al.  A Hybrid Algorithm for Preserving Energy and Delay Routing in Mobile Ad-Hoc Networks , 2015, Wireless Personal Communications.

[23]  Hui Cheng.  Genetic algorithms with hyper-mutation for dynamic load balanced clustering problem in mobile ad hoc networks , 2012, 2012 8th International Conference on Natural Computation.

[24]  Marília Curado,et al.  Smart and Balanced Clustering for MANETs , 2011, ADHOC-NOW.

[25]  P.H.J. Chong,et al.  A survey of clustering schemes for mobile ad hoc networks , 2005, IEEE Communications Surveys & Tutorials.

[26]  Victor C. M. Leung,et al.  Self-Organized Relay Selection for Cooperative Transmission in Vehicular Ad-Hoc Networks , 2017, IEEE Transactions on Vehicular Technology.

[27]  Yoshiaki Kakuda,et al.  An Inter-Cluster Communication Scheme for Self-Organized Transmission Power Control in MANET Clustering , 2015, 2015 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops.

[28]  Pascal Lorenz,et al.  Connectivity, Energy and Mobility Driven Clustering Algorithm for Mobile Ad Hoc Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[29]  Zhangdui Zhong,et al.  MPBC: A Mobility Prediction-Based Clustering Scheme for Ad Hoc Networks , 2011, IEEE Transactions on Vehicular Technology.

[30]  I. Couzin,et al.  Collective memory and spatial sorting in animal groups. , 2002, Journal of theoretical biology.