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331
An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks
, 2003
"... A wireless network consisting of a large number of small sensors with lowpower transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to de ..."
Abstract

Cited by 390 (1 self)
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A wireless network consisting of a large number of small sensors with lowpower transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the Hmited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks
 Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks
, 2001
"... this paper, we propose an ondemand distributed clustering algorithm for multihop packet radio networks. These types of networks, also known as ad hoc networks, are dynamic in nature due to the mobility of the nodes. The association and dissociation of nodes to and from clusters perturb the stab ..."
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Cited by 275 (9 self)
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this paper, we propose an ondemand distributed clustering algorithm for multihop packet radio networks. These types of networks, also known as ad hoc networks, are dynamic in nature due to the mobility of the nodes. The association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence a reconguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The clusterheads, form a dominant set in the network, determine the topology and its stability. The proposed weightbased distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a predened threshold to facilitate the optimal o
A Cooperative Intrusion Detection System for Ad Hoc Networks
, 2003
"... Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANETs are highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, lack of centralized ..."
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Cited by 193 (3 self)
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Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANETs are highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. In this paper, we report our progress in developing intrusion detection (ID) capabilities for MANET. Building on our prior work on anomaly detection, we investigate how to improve the anomaly detection approach to provide more details on attack types and sources. For several wellknown attacks, we can apply a simple rule to identify the attack type when an anomaly is reported. In some cases, these rules can also help identify the attackers. We address the runtime resource constraint problem using a clusterbased detection scheme where periodically a node is elected as the ID agent for a cluster. Compared with the scheme where each node is its own ID agent, this scheme is much more efficient while maintaining the same level of effectiveness. We have conducted extensive experiments using the ns2 and MobiEmu environments to validate our research. 1.
Power Control and Clustering in Ad Hoc Networks
 In INFOCOM
, 2003
"... In this paper, we consider the problem of power control when nodes are nonhomogeneously dispersed in space. In such situations, one seeks to employ per packet power control depending on the source and destination of the packet. This gives rise to a joint problem which involves not only power contro ..."
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Cited by 178 (3 self)
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In this paper, we consider the problem of power control when nodes are nonhomogeneously dispersed in space. In such situations, one seeks to employ per packet power control depending on the source and destination of the packet. This gives rise to a joint problem which involves not only power control but also clustering. We provide three solutions for joint clustering and power control.
An extended localized algorithm for connected dominating set formation in ad hoc wireless networks
 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
, 2004
"... Efficient routing among a set of mobile hosts is one of the most important functions in ad hoc wireless networks. Routing based on a connected dominating set is a promising approach, where the search space for a route is reduced to the hosts in the set. A set is dominating if all the hosts in the sy ..."
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Cited by 143 (15 self)
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Efficient routing among a set of mobile hosts is one of the most important functions in ad hoc wireless networks. Routing based on a connected dominating set is a promising approach, where the search space for a route is reduced to the hosts in the set. A set is dominating if all the hosts in the system are either in the set or neighbors of hosts in the set. The efficiency of dominatingsetbased routing mainly depends on the overhead introduced in the formation of the dominating set and the size of the dominating set. In this paper, we first review a localized formation of a connected dominating set called marking process and dominatingsetbased routing. Then, we propose a dominant pruning rule to reduce the size of the dominating set. This dominant pruning rule (called Rule k) is a generalization of two existing rules (called Rule 1 and Rule 2, respectively). We prove that the vertex set derived by applying Rule k is still a connected dominating set. Rule k is more effective in reducing the dominating set derived from the marking process than the combination of Rules 1 and 2 and, surprisingly, in a restricted implementation with local neighborhood information, Rule k has the same communication complexity and less computation complexity. Simulation results confirm that Rule k outperforms Rules 1 and 2, especially in networks with relatively high vertex degree and high percentage of unidirectional links. We also prove that an upper bound exists on the average size of the dominating set derived from Rule k in its restricted implementation.
A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks
 In International Workshop on Wireless Networks and Mobile Computing (WNMC2001
, 2001
"... This paper presents a novel mobility metric for mobile ad hoc networks (MANET) that is based on the ratio between the received power levels of successive transmissions measured at any node from all its neighboring nodes. This mobility metric is subsequently used as a basis for cluster formation wh ..."
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Cited by 132 (3 self)
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This paper presents a novel mobility metric for mobile ad hoc networks (MANET) that is based on the ratio between the received power levels of successive transmissions measured at any node from all its neighboring nodes. This mobility metric is subsequently used as a basis for cluster formation which can be used for improving the scalability of services such as routing in such networks. We propose a distributed clustering algorithm, MOBIC, based on the use of this mobility metric for selection of clusterheads, and demonstrate that it leads to more stable cluster formation than the LowestID clustering algorithm 1 which is a well known clustering algorithms for MANETs. We show reduction of as much as 33% in the number of clusterhead changes owing to the use of the proposed technique. In a MANET that uses scalable clusterbased services, the network performance metrics such as throughput and delay are tightly coupled with the frequency of cluster reorganization. Therefore, we believe that since using MOBIC results in a more stable conguration, it will directly lead to improvement of performance. Keywords: Ad hoc networks, Mobility, Clustering. 1
ACE: An Emergent Algorithm for Highly Uniform Cluster Formation
 in Proceedings of the First European Workshop on Sensor Networks (EWSN
, 2004
"... Abstract. The efficient subdivision of a sensor network into uniform, mostly nonoverlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing. We present ACE, ..."
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Cited by 111 (1 self)
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Abstract. The efficient subdivision of a sensor network into uniform, mostly nonoverlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing. We present ACE, an algorithm that results in highly uniform cluster formation that can achieve a packing efficiency close to hexagonal closepacking. By using the selforganizing properties of three rounds of feedback between nodes, the algorithm induces the emergent formation of clusters that are an efficient cover of the network, with significantly less overlap than the clusters formed by existing algorithms. The algorithm is scaleindependent — it completes in time proportional to the deployment density of the nodes regardless of the overall number of nodes in the network. ACE requires no knowledge of geographic location and requires only a small constant amount of communications overhead. 1
Distributed and mobilityadaptive clustering for multimedia support in multihop wireless networks, in:
 Proc. of IEEE Vehicular Tech. Conf.
, 1999
"... ..."
Geometric Spanners for Wireless Ad Hoc Networks
 IEEE Transactions on Parallel and Distributed Systems
, 2003
"... We propose a new geometric spanner for static wireless ad hoc networks, which can be constructed efficiently in a localized manner. It integrates the connected dominating set and the local Delaunay graph to form a backbone of the wireless network. ..."
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Cited by 93 (25 self)
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We propose a new geometric spanner for static wireless ad hoc networks, which can be constructed efficiently in a localized manner. It integrates the connected dominating set and the local Delaunay graph to form a backbone of the wireless network.
Discrete Mobile Centers
 Discrete and Computational Geometry
, 2001
"... We propose a new randomized algorithm for maintaining a set of clusters among moving nodes in the plane. Given a specified cluster radius, our algorithm selects and maintains a variable subset of the nodes as cluster centers. This subset has the property that (1) balls of the given radius centered a ..."
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Cited by 92 (13 self)
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We propose a new randomized algorithm for maintaining a set of clusters among moving nodes in the plane. Given a specified cluster radius, our algorithm selects and maintains a variable subset of the nodes as cluster centers. This subset has the property that (1) balls of the given radius centered at the chosen nodes cover all the others and (2) the number of centers selected is a constantfactor approximation of the minimum possible. As the nodes move, an eventbased kinetic data structure updates the clustering as necessary. This kinetic data structure is shown to be responsive, efficient, local, and compact. The produced cover is also smooth, in the sense that wholesale cluster rearrangements are avoided. The algorithm can be implemented without exact knowledge of the node positions, if each node is able to sense its distance to other nodes up to the cluster radius. Such a kinetic clustering can be used in numerous applications where mobile devices must be interconnected into an adhoc network to collaboratively perform some tasks. 1