Proceedings ArticleDOI
Max-min d-cluster formation in wireless ad hoc networks
Alan D. Amis,Ravi Prakash,T.H.P. Vuong,D.T. Huynh +3 more
- Vol. 1, pp 32-41
TLDR
A heuristic to form d-clusters in a wireless ad hoc network that tends to re-elect existing clusterheads even when the network configuration changes and has a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes.Abstract:
An ad hoc network may be logically represented as a set of clusters. The clusterheads form a d-hop dominating set. Each node is at most d hops from a clusterhead. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Previous heuristics restricted themselves to 1-hop clusters. We show that the minimum d-hop dominating set problem is NP-complete. Then we present a heuristic to form d-clusters in a wireless ad hoc network. Nodes are assumed to have a non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. When the heuristic terminates, a node either becomes a clusterhead, or is at most d wireless hops away from its clusterhead. The value of d is a parameter of the heuristic. The heuristic can be run either at regular intervals, or whenever the network configuration changes. One of the features of the heuristic is that it tends to re-elect existing clusterheads even when the network configuration changes. This helps to reduce the communication overheads during transition from old clusterheads to new clusterheads. Also, there is a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic is fair and stable. Simulation experiments demonstrate that the proposed heuristic is better than the two earlier heuristics, namely the LCA and degree-based solutions.read more
Citations
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Issues and challenges in clustering techniques for wireless mesh networks
TL;DR: Clustering techniques were classified in this paper according to their implementation criteria such as heuristic, weighted, emergent and hierarchical while the advantages and weaknesses are technically highlighted.
Journal ArticleDOI
Multi-Hub Location Heuristic for Alert Routing
TL;DR: Several heuristic algorithms selecting the initial set H of hubs are combined with the greedy and k-means-like approaches adapted from Euclidean to graph (i.e. geodesic) distance to produce results that can cut the worst case number of hops required to route a warning.
Proceedings ArticleDOI
An Application Scheme of Publish/Subscribe System over Clustering Mobile Ad Hoc Networks
TL;DR: This application scheme implements mobile support and data distribution for the Publish/Subscribe system in the clustering MANET, which can reduce the communication overload and improve the utilization and reliability of MANET.
Proceedings ArticleDOI
Optimization of energy efficient cellular learning automata algorithm for heterogeneous wireless sensor networks
C.P. Subha,S. Malarkkan +1 more
TL;DR: This paper proposes a method to select the cluster head based on the residual energy of the nodes based on Cellular Learning Automata and Heterogeneous-Hybrid Energy Efficient Distributed Distributed(H-HEED)technique.
Proceedings ArticleDOI
New framework of back diffusion-based autonomous decentralized control and its application to clustering scheme
TL;DR: A new autonomous decentralized control scheme is introduced that creates the spatial structures of finite size by using renormalization transformation and back diffusion drift and is used to realize autonomous decentralized clustering in ad hoc networks.
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