scispace - formally typeset
Search or ask a question

Showing papers on "Data aggregator published in 2003"


Proceedings ArticleDOI
01 Jan 2003
TL;DR: The main idea is to turn off the radio of all leaf nodes to save power, and thereby extending the network lifetime, which makes the EADAT algorithm very efficient and effective, as demonstrated by the simulation experiments with NS2.
Abstract: Large-scale wireless sensor networks are expected to play an increasingly important role in future civilian and military settings. Collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem. In this paper we propose heuristics to construct and maintain an aggregation tree in sensor networks. This aggregation tree can be used to facilitate data-centric routing. The main idea is to turn off the radio of all leaf nodes to save power, and thereby extending the network lifetime. Therefore, in order to save the number of broadcasting messages, only the nonleaf nodes in the tree are in charge of data aggregation and traffic relaying. In this paper, we propose an efficient energy-aware distributed heuristic to generate the aggregation tree, which we refer to as EADAT. Our EADAT algorithm makes no assumption on local network topology, and is based on residual power. It makes use of neighboring broadcast scheduling and distributed competition among neighbors. These novel concepts make EADAT very efficient and effective, as demonstrated by our simulation experiments with NS2.

274 citations


Proceedings ArticleDOI
22 Oct 2003
TL;DR: Simulations results show that as data redundancy increases, the amount of data transmitted from sensor nodes to cluster-head decreases up to 45% when compared to conventional algorithms.
Abstract: Secure data transmission and data aggregation are critical in designing cluster-based sensor networks This paper presents an Energy-efficient and Secure Pattern-based Data Aggregation protocol (ESPDA) for wireless sensor networks ESPDA is energy and bandwidth efficient because cluster-heads prevent the transmission of redundant data from sensor nodes ESPDA is also secure because it does not require the encrypted data to be decrypted by cluster-heads to perform data aggregation In ESPDA, cluster-head first requests sensor nodes to send the corresponding pattern code for the sensed data If multiple sensor nodes send the same pattern code to the cluster-head, then only one of them is permitted to send the data to the cluster-head Hence, ESPDA has advantages over the conventional data aggregation techniques with respect to energy, bandwidth efficiency and security Simulations results show that as data redundancy increases, the amount of data transmitted from sensor nodes to cluster-head decreases up to 45% when compared to conventional algorithms

129 citations


26 Nov 2003
TL;DR: In this article, the authors proposed a data aggregation scheme that allows nodes to process data collected from sensors and subsequently aggregate the data even in completely unfamiliar environments by including entire query definitions within interest messages.
Abstract: Wireless sensor networks are formed of tiny, energy-constrained sensor nodes that could be mobile and may be deployed in unfamiliar environments in large numbers. Considering these unique characteristics, our network architecture is modelled around a data-centric approach that allows us to make use of in-network processing and data aggregation which in turn helps to maximize network lifetime. This paper suggests methods to improve network efficiency by combining Directed Diffusion [2] with clustering and by introducing a more elaborate data aggregation scheme. Our data aggregation scheme allows nodes to process data collected from sensors and subsequently aggregate the data even in completely unfamiliar environments by including entire query definitions within interest messages. We also describe certain novel design features such as interest transformation, layered data aggregation and dynamic data aggregation points all of which would improve overall system performance.

74 citations


Proceedings ArticleDOI
11 Jun 2003
TL;DR: The design and implementation of a programming primitive -- Data Aggregation Call (DAC) -- to exploit partition parallelism for cluster-based Internet services and the effectiveness of the proposed optimization techniques for reducing response time, improving throughput, and gracefully handling server unresponsiveness are validated.
Abstract: Large-scale cluster-based Internet services often host partitioned datasets to provide incremental scalability. The aggregation of results produced from multiple partitions is a fundamental building block for the delivery of these services. This paper presents the design and implementation of a programming primitive -- Data Aggregation Call (DAC) -- to exploit partition parallelism for cluster-based Internet services. A DAC request specifies a local processing operator and a global reduction operator, and it aggregates the local processing results from participating nodes through the global reduction operator. Applications may allow a DAC request to return partial aggregation results as a tradeoff between quality and availability. Our architecture design aims at improving interactive responses with sustained throughput for typical cluster environments where platform heterogeneity and software/hardware failures are common. At the cluster level, our load-adaptive reduction tree construction algorithm balances processing and aggregation load across servers while exploiting partition parallelism. Inside each node, we employ an event-driven thread pool design that prevents slow nodes from adversely affecting system throughput under highly concurrent workload. We further devise a staged timeout scheme that eagerly prunes slow or unresponsive servers from the reduction tree to meet soft deadlines. We have used the DAC primitive to implement several applications: a search engine document retriever, a parallel protein sequence matcher, and an online parallel facial recognizer. Our experimental and simulation results validate the effectiveness of the proposed optimization techniques for reducing response time, improving throughput, and gracefully handling server unresponsiveness. We also demonstrate the ease-of use of the DAC primitive and the scalability of our architecture design.

37 citations


01 Jan 2003
TL;DR: This paper proposes a generalized selfclustering protocol, called Low-energy Localized Clustering (LLC), which incorporates the best features of two other recently proposed self-configuring protocols for sensor networks: the Localized protocol and the Low Energy Adaptive Clustered Hierarchy (LEACH) protocol.
Abstract: Microsensors operate under severe energy constraints and should be deployed in large numbers without any pre-configuration. The main contribution of this paper is a generalized selfclustering protocol, called Low-energy Localized Clustering (LLC). It incorporates the best features of two other recently proposed self-configuring protocols for sensor networks: the Localized protocol and the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. LLC covers a range of behaviors from the better-clustering performance of the Localized method to the more energy-efficient operation of the LEACH method. As experimental results show, the main advantage of LLC is that it can be energy-efficient while maintaining a high cluster quality. We outline data aggregation approaches such as summarization, finding representative data items, and pattern matching. Data aggregation is a necessity in microsensor networks to avoid transmitting huge volumes of raw data, which is energy-intensive. Finally, an energy-efficient Randomized Data Authentication protocol is designed specifically for microsensor applications.

13 citations