Chapters authored
Agent Based Load Balancing in Grid Computing
Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.
Part of the book: Artificial Intelligence
K-Means Efficient Energy Routing Protocol for Maximizing Vitality of WSNs
The progress of wireless communication and microelectronics create wireless sensor network, which is a very important field of research, The utilization of Wireless Sensor Network is growing and have a diversity applications like Military applications, Agriculture, Health care, Medical monitoring. The main issue of WSN is energy consumption, where prolonged network lifetime, is important necessity. From the solution proposed the Clustering with k-means is a successful technique for achieving these goals. This work is adaptation of one of the most famous protocol in WSN witch is Low Energy Adaptive Clustering Hierarchy (LEACH) in the clustering phase where the choice of number of clusters and their CHs.sing the k-means method and the distance between nodes and residual energy. Clustering k-means given a best partition with cluster separation. This chapter regulated as below, in section two we discussed related work used k-means to improved vitality of WSN. In the next section, we introduce the proposed adaptation protocol. The simulation resultsusing MATLAB have shown that the proposed protocol outperforms LEACH protocol and optimizes the nodes energy and thenetwork lifetime.
Part of the book: Computational Optimization Techniques and Applications