Design of Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networks

The research on various issues in Mobile ad hoc networks are getting popularity because of its challenging nature and all time connectivity to communicate. MANET (Mobile Ad-hoc Networks) is a random deployable network where devices are mobile with dynamic topology. In the network topology, each device is termed as a node and the virtual connectivity among each node is termed as the link .Nodes in a network are dynamically organized into virtual partitions called clusters. Network simulators provide the platform to analyse and imitate the working of computer networks along with the typical devices, traffic and other entities. Cluster heads being the communication hotspots tend to drain its battery power rapidly while serving its member nodes. Further, energy consumption is a key factor that hinders the deploy ability of a real ad hoc and sensor network. It is due to the limited life time of the battery powered devices that motivates intense research into energy efficient design of operating systems, protocols and hardware devices. Clustering is a proven solution to preserve the battery power of certain nodes. In the mechanism of clustering, there exists a cluster head in every cluster that works similar to a base station in the cellular architecture. Cluster heads being the communication hotspots tend to drain its battery power rapidly while serving its member nodes. Further, energy consumption is a key factor that hinders the deploy ability of a real ad hoc and sensor network. It is due to the limited life time of the battery powered devices that motivates intense research into energy efficient design of operating systems, protocols and hardware devices. The mobile ad hoc network can be modelled as a unidirectional graph G = (V, L) where V is the set of mobile nodes and L is the set of links that exist between the nodes. We assume that there exists a bidirectional link L between the nodes and when the distance between the nodes < (transmission range) of the nodes. In the dynamic network the cardinality of the nodes remains constant, but the cardinality of links changes due to the mobility of the nodes. Network simulators are used by researchers, developers and engineers to design various kinds of networks, simulate and then analyze the effect of various parameters on the network performance. A typical network simulator encompasses a wide range of networking technologies and can help the users to build complex networks from basic building blocks such as a variety of nodes and links. The objective of our work is to design a simulator for energy efficient clustering so that the data flow as well as the control flow could be easily handled and maintained. The proposed energy efficient clustering algorithm is a distributed algorithm that takes into account the consumed battery power of a node and its average transmission power for serving the neighbour nodes as the parameters to decide its suitability to act as a cluster head. These two parameters are added with different weight factors to find the weights of the individual nodes. After the clusters are formed, gateway nodes are selected in the network that help for the inter cluster communication. The graph for the number of cluster heads selected for different number of nodes are also drawn to study the functionality of the simulator

Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation

Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size

Design and Analysis of SD_DWCA - A Mobility based clustering of Homogeneous MANETs

This paper deals with the design and analysis of the distributed weighted clustering algorithm SD_DWCA proposed for homogeneous mobile ad hoc networks. It is a connectivity, mobility and energy based clustering algorithm which is suitable for scalable ad hoc networks. The algorithm uses a new graph parameter called strong degree defined based on the quality of neighbours of a node. The parameters are so chosen to ensure high connectivity, cluster stability and energy efficient communication among nodes of high dynamic nature. This paper also includes the experimental results of the algorithm implemented using the network simulator NS2. The experimental results show that the algorithm is suitable for high speed networks and generate stable clusters with less maintenance overhead