A Mobile Ad Hoc Network (MANET) is an infrastructureless wireless network that can support highly dynamic mobile units. The multi-hop feature of a MANET suggests the use of clustering to simplify routing. Graph domination can be used in defining clusters in MANETs. A variant of dominating set which is more suitable for clustering MANETs is the weakly-connected dominating set. A cluster is defined to be the set of vertices dominated by a particular vertex in the dominating set. As it is NP-complete to determine whether a given graph has a weakly-connected dominating set of a particular size, we present a zonal distributed algorithm for finding small weakly-connected dominating sets. In this new approach, we divide the graph into regions, construct a weakly-connected dominating set for each region, and make adjustments along the borders of the regions to produce a weakly-connected dominating set of the entire graph. We present

Connectivity-based node clustering has wide-ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper describes a Connectivity-based Distributed Node Clustering scheme (CDC). This scheme presents a scalable and efficient solution for discovering connectivity-based clusters in peer networks. In contrast to centralized graph clustering algorithms, the CDC scheme is completely decentralized and it only assumes the knowledge of neighbor nodes instead of requiring a global knowledge of the network (graph) to be available. An important feature of the CDC scheme is its ability to cluster the entire network automatically or to discover clusters around a given set of nodes. To cope with the typical dynamics of P2P networks, we provide mechanisms to allow new nodes to be incorporated into appropriate existing clusters and to gracefully handle the departure of nodes in the clusters. These mechanisms enable the CDC scheme to be extensible and adaptable in the sense that the clustering structure of the network adjusts automatically as nodes join or leave the system. We provide detailed experimental evaluations of the CDC scheme, addressing its effectiveness in discovering good quality clusters and handling the node dynamics. We further study the types of topologies that can benefit best from the connectivitybased distributed clustering algorithms like CDC. Our experiments show that utilizing message-based connectivity structure can considerably reduce the messaging cost and provide better utilization of resources, which in turn improves the quality of service of the applications executing over decentralized peer-to-peer networks.

Abstract—In the context of mobile ad hoc networks (MANETs) routing, we propose a clustering algorithm called Connectivity, Energy and Mobility driven Clustering Algorithm (CEMCA). The aim of CEMCA consists in appropriately choosing the cluster head to reduce routing overhead. In order to reduce traffic and energy consumption, the control messages are sent only when needed, according to the speed of the node. Each node has a quality that indicates its suitability as a cluster head. This quality takes into account the node connectivity, battery energy and mobility. These parameters are very important for the stability of the cluster. Simulation experiments are carried out to validate our algorithm in terms of stability of the clusters and their members and the quality of the connectivity. The results are compared to a previous approach called Weight Clustering Algorithm (WCA) and they show that CEMCA is performing better. I.

One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper, we introduce selective sampling for energy-efficient periodic data collection in sensor networks. The main idea behind selective sampling is to use a dynamically changing subset of nodes as samplers such that the sensor readings of sampler nodes are directly collected, whereas the values of non-sampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed in an in-network manner. Selective sampling can be effectively used to increase the network lifetime while keeping quality of the collected data high, in scenarios where either the spatial density of the network deployment is superfluous relative to the required spatial resolution for data analysis or certain amount of data quality can be traded off in order to decrease the overall power consumption of the network. Our selective sampling approach consists of three main mechanisms. First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation is used to determine the sampler nodes and to calculate the parameters of probabilistic models that capture the spatial and temporal correlations among sensor readings. Last, selective data collection and model-based prediction is used to minimize the number of messages used to extract data from the n...

Abstract: Multi-hop wireless networks (MWN) consist of sets of mobile wireless nodes without the support of a pre-existing fixed infrastructure. Each host/node acts as a router and may arbitrary appear or vanish. This feature is a challenging issue for protocol design since protocols must adapt to frequent changes of network topologies. When dealing with sensor networks, the scalability also becomes a crucial aspect. In such large networks, we need not only to be able to route messages from any node to any other node but also to spread some information over the whole network. Till nowadays, it seems that these two properties have only been studied separately. In this report, we propose to use our existing cluster formation algorithm also to perform the broadcast operation. Key-words: ad hoc, sensors, wireless, self-organization, broadcast, scalability Unité de recherche INRIA Rhône-Alpes