Improving fault tolerance and load balancing in Wireless Networks

Abstract

In this Distributed Fault-Tolerant Quality of Wireless Networks approach they proposed EFDCB that unifies modified GDMAC and FDCB protocols and uses CFSR for QoS routing. Here in the proposed system, proposing the weighted clustering algorithm, leads to a high degree of stability in the network and improves the load balancing in [1][2]GDMAC. The load balancing is accomplished by determining a pre-defined threshold on the number of nodes that a clusterhead can cover ideally. This ensures that none of the clusterheads are overloaded at any instance of time. Moreover the stability can be accomplished by reducing the number of nodes detachmentfrom its current cluster and connect to another existing cluster. In this approach, each node is assigned weights (a real number above zero) based on its suitability of being a clusterhead. A node is chosen to be a clusterhead if its weight is higher than any of its neighbor's weight; otherwise, it joins a neighboring clusterhead. The smaller ID node id is chosen in case of a tie. The DCA makes an assumption that the network topology does not change during the execution of the algorithm. To verify the performance of the system, the nodes were assigned weights which varied linearly with their speeds but with negative slope. Results proved that the number of updates required is smaller than the [3][4]Highest-Degree and Lowest-ID heuristics. Since node weights were varied in each simulation cycle, computing the clusterheads becomes very expensive and there are no optimizations on the system parameters such as throughput and power control. The Weighted Clustering Algorithm (WCA) takes the factors into consideration and makes the selection of clusterhead and maintenance of cluster more reasonable. The factors are node degree, distance summation to all its neighboring nodes, mobility and remaining battery power respectively. And their corresponding weights are wl to w4. Besides, it converts the clustering problem into an optimization problem since an objective function is formed.