Load Balanced Clustering Technique in MANET using Genetic Algorithms

Mobile adhoc network (MANET) has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load-balanced clustering problem (DLBCP). Load-balancing and reliable data transfer between all the nodes are essential to prolong the lifetime of the network. MANET can also be partitioned into clusters for maintaining the network structure. Generally, Clustering is used to reduce the size of topology and to accumulate the topology information. It is necessary to have an effective clustering algorithm for adapting the topology change. In this, we used energy metric in genetic algorithm (GA) to solve the DLBCP. It is important to select the energy- efficient cluster head for maintaining the cluster structure and balance the load effectively. In this work, we used genetic algorithms such as elitism based immigrants genetic algorithm (EIGA) and memory enhanced genetic algorithm (MEGA) to solve DLBCP. These schemes select an optimal cluster head by considering the distance and energy parameters. We used EIGA to maintain the diversity level of the population and MEGA to store the old environments into the memory. It promises the load -balancing in cluster structure to increase the lifetime of the network. Experimental results show that the proposed schemes increases the network lifetime and reduces the total energy consumption. The simulation results show that MEGA and EIGA give a better performance in terms of load-balancing

An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks

Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Enhanced Load Balanced Clustering Technique for VANET Using Location Aware Genetic Algorithm

The vehicular Adhoc Network has unique charac-teristics of frequent topology changes, traffic rule-based node movement, and speculative travel pattern. It leads to stochastic unstable nature in forming clusters. The re-liable routing process and load balancing are essential to improve the network lifetime. Cluster formation is used to split the network topology into small structures. The reduced size network leads to accumulating the topology information quickly. Due to the absence of centralised management, there is a pitfall in network topology man-agement and optimal resource allocation, resulting in ineffective routing. Hence, it is necessary to develop an effective clustering algorithm for VANET. In this paper, the Genetic Algorithm (GA) and Dynamic Programming (DP) are used in designing load-balanced clusters. The proposed Angular Zone Augmented Elitism-Based Im-migrants GA (AZEIGA) used elitism-based immigrants GA to deal with the population and DP to store the out-come of old environments. AZEIGA ensures clustering of load-balanced nodes, which prolongs the network lifetime. Experimental results show that AZEIGA works appreciably well in homogeneous resource class VANET. The simulation proves that AZEIGA gave better perfor-mance in packet delivery, network lifetime, average de-lay, routing, and clustering overhead

Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

With the fast development of wireless technologies, wireless mesh networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation (PSO) in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method (CM), random inertia weight method (RIWM), linearly decreasing Vmax method (LDVM) and linearly decreasing inertia weight method (LDIWM). Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component (SGC) and the number of covered mesh clients (NCMC). The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.Peer ReviewedPostprint (author's final draft