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Showing papers by "Sonia Fahmy published in 2004"


Journal ArticleDOI
TL;DR: It is proved that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks.
Abstract: Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

4,889 citations


Proceedings ArticleDOI
07 Mar 2004
TL;DR: A protocol is presented, HEED (hybrid energy-efficient distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree, which outperforms weight-based clustering protocols in terms of several cluster characteristics.
Abstract: Prolonged network lifetime, scalability, and load balancing are important requirements for many ad-hoc sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new energy-efficient approach for clustering nodes in ad-hoc sensor networks. Based on this approach, we present a protocol, HEED (hybrid energy-efficient distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED does not make any assumptions about the distribution or density of nodes, or about node capabilities, e.g., location-awareness. The clustering process terminates in O(1) iterations, and does not depend on the network topology or size. The protocol incurs low overhead in terms of processing cycles and messages exchanged. It also achieves fairly uniform cluster head distribution across the network. A careful selection of the secondary clustering parameter can balance load among cluster heads. Our simulation results demonstrate that HEED outperforms weight-based clustering protocols in terms of several cluster characteristics. We also apply our approach to a simple application to demonstrate its effectiveness in prolonging the network lifetime and supporting data aggregation.

1,373 citations


Journal ArticleDOI
TL;DR: Creating an experimental infrastructure for developing next-generation information security technologies and assessing the impact it has on existing and new technologies is being proposed.
Abstract: Creating an experimental infrastructure for developing next-generation information security technologies.

71 citations


Proceedings ArticleDOI
07 Jun 2004
TL;DR: This work proves that REED can asymptotically achieve k tolerance if certain constraints on node density are satisfied, and investigates via simulations the clustering properties of REED, and shows that building multiple cluster head overlays does not consume significant energy.
Abstract: Clustering sensor nodes increases the scalability and energy efficiency of communications among them. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. In this work, we propose a new approach, REED (Robust Energy Efficient Distributed clustering), for clustering sensors deployed in hostile environments. Our primary objective is to construct a k (i.e., k-connected) network, where k is a constant determined by the application. Fault tolerance can be achieved by selecting k independent sets of cluster heads (i.e., cluster head overlays) on top of the physical network, so that each node can quickly switch to other cluster heads in case of failures or attacks on its current cluster head. The independent cluster head overlays also provide multiple vertex-disjoint routing paths for load balancing and security. Network lifetime is prolonged by selecting cluster heads with high residual energy and low communication cost, and periodically reclustering the network in order to distribute energy consumption among sensor nodes. We prove that REED can asymptotically achieve k tolerance if certain constraints on node density are satisfied. We also investigate via simulations the clustering properties of REED, and show that building multiple cluster head overlays does not consume significant energy.

38 citations


Proceedings Article
23 Mar 2004
TL;DR: This poster introduces the multi-organization project funded by the National Science Foundation and the Department of Homeland Security entitled "Evaluation Methods for Internet Security Technology".
Abstract: This poster introduces the multi-organization project funded by the National Science Foundation and the Department of Homeland Security entitled "Evaluation Methods for Internet Security Technology". Its main objective is to develop scientifically rigorous testing frameworks and methodologies for representative classes of network attacks and defense mechanisms.

4 citations


Journal Article
TL;DR: The behavior of a new response strategy to TCP Explicit Congestion Notification (ECN) is investigated, which is more aggressive in the short term, but preserves TCP long term behavior - without modifying the router ECN marking rate.
Abstract: We investigate the behavior of a new response strategy to TCP Explicit Congestion Notification (ECN) The new strategy is more aggressive in the short term, but preserves TCP long term behavior - without modifying the router ECN marking rate A more aggressive short term behavior gives incentives for hosts to become ECN-compliant ECN serves as an early warning sign in this case Our analysis demonstrates the effectiveness of the new TCP ECN behavior Simulation results with short/long lived FTP, UDP and HTTP connections, multiple bottleneck configurations, and various TCP flavors and parameters, demonstrate higher throughput and reduced oscillations with the new response strategy

3 citations


01 Jan 2004
TL;DR: In iHEED, node clustering is integrated with multi-hop routing for TinyOS, in which sensor nodes are clustered prior to constructing the data aggregation tree, and network lifetime is prolonged by a factor of 2 to 4, and successful transmissions are almost doubled.
Abstract: Several sensor network applications, such as environmental monitoring, require data aggregation to an observer (eg, a base station) For this purpose, a data aggregation tree rooted at the observer is constructed in the network to reduce communication overhead and facilitate faster and more reliable results Node clustering can be employed for this purpose, to further balance load among sensor nodes and prolong the network lifetime In this paper, we design and implement a system, iHEED, in which node clustering is integrated with multi-hop routing for TinyOS In iHEED, sensor nodes are clustered prior to constructing the data aggregation tree We consider simple data aggregation operators, such as AVG or MAX We perform experiments on a sensor network testbed to quantify the benefits of integrating hierarchical routing with data aggregation Our results indicate that, by using reduced intra-cluster transmission power and exploiting intra-cluster and inter-cluster data aggregation, network lifetime is prolonged by a factor of 2 to 4, and successful transmissions are almost doubled The overhead of the clustering process is subsumed by tree construction and maintenance overhead Index Temlssensor networks, implementation, clustering, energy efficiency

3 citations


01 Jan 2004
TL;DR: A distributed clustering-based high-level time synchronization framework for multi-hop od-hoc networks that builds a two-tired synchronized network and provides a density model for validating SYNC-NET, and evaluates all protocols via extensive simulations.
Abstract: Abstrac/-Time synchronization is essential for several ad-hoc network protocols and applications, such as TDMA scheduling, dolo aggregation, caching, object tracking, and security checking. Prior work on synchronization in wireless networks has not adequately addressed rapid conver· gence and scalability requirements in dense networks serving time-sensitive applications, such as sensor networks. In this paper, we propose a distributed clustering-based high-level time synchronization framework for multi-hop od-hoc networks that builds a two-tired. synchronized network. We do not make any assumptions about node capabilities (e.g.) being GPS-enabled), or the presence of reference nodes in the network. Thus, global consensus on one time value is not our goal. Rather, we assume Lhat relative node synchronization is sufficient. We study both classes of sensor network applications. namely, source-driven and data-driven applications. We give fuUy distributed protocols for regional synchronization (nodes within 2-hops), path synchronization, and global (inter-cluster) network synchronization. Our proposed path synchronization protocol (SYNC-PATH) is reactive, while our inter-cluster network synchronization protocol (SYNC-NET) is proactive. The protocols exploit the fad Lhat for most applications, coarse-grained accuracy is sufficient at the global scale. Our framework is independent of the clustering and inter-cluster routing approach, and the underlying low-level synchronization protocol, and thus is suitable for use in conjunction wiLh both receiver-receiver and sender-receiver synchronization approaches. We analyze each protocol and prove that it terminates in 0(1) time. We also provide a density model for validating SYNC-NET, and evaluate all protocols via extensive simulations. Our framework can be employed in any ad-hoc wireless network setting. (CAREER) and lIle Sehlumbcrger Foundation technical merit award.

3 citations


Proceedings Article
01 Jan 2004
TL;DR: Hybrid Energy-Efficient Distributed Clustering (HEED) as mentioned in this paper is a protocol that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree.
Abstract: Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

3 citations