A Mobility Prediction-based Weighted Clustering Algorithm Using Local Cluster-heads Election for QoS in MANETs

In this paper, we propose a new distributed mobility prediction-based weighted clustering algorithm with local cluster-heads election (MPWCA-L) based on an on-demand distributed clustering algorithm for multi-hop packet radio networks. The multi-hop packet radio networks, also named mobile ad hoc networks (MANETs) have a dynamic topology due to the mobility of their nodes. This mobility makes the challenge harder for routing protocol. Moreover, the well known routing protocols are not able to offer QoS that is why we need to manage MANETs. Such task can be done using clustering techniques but the association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The nodes called cluster-heads form a dominant set and determine the topology and its stability. Simulation experiments are conducted to evaluate the stability of the dominant set in terms of updates of the dominant set, handovers of a node between two clusters and the QoS in terms of packet delivery rate, end-to-end delay and overhead provided by both our algorithm (MPWCA-L) and the weighted clustering algorithm (WCA), which does not consider prediction and local cluster-heads election. Results show that our algorithm performs better than WCA