Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Energy-efficient data gathering algorithm in sensor networks with partial aggregation

Abstract: In sensor networks, data aggregation at intermediate nodes can significantly reduce redundant data and reduce communication load. However, there are scenarios where data aggregation is restricted. In this paper, we study the problem of building an energy-efficient tree structure that can be used for both aggregate data and non-aggregate data. Such a tree provides a transition between the optimal solutions for both aggregate data and for non-aggregate data. A single parameter can be used to control the transition. We proposed a new algorithm Balanced Aggregation Tree (BAT) for tree construction and also suggested how to determine the value of the control parameter for the highest energy efficiency of a given network.

Delay-efficient data aggregation scheduling in duty-cycled wireless sensor networks

Abstract: Data aggregation is an essential operation in wireless sensor networks (WSNs) in which sensed data are aggregated and transmitted to the sink. In many applications, reducing the latency of data aggregation is an important target. In addition, one of the primary challenges in WSNs is energy scarcity and reducing energy consumption is a problem. Recently, duty cycling, i.e., periodically switching on and off communication and sensing capabilities, has been considered to significantly reduce the sensor's energy consumption and extend a network lifetime. In this paper, we consider the minimum-latency aggregation scheduling problem in duty-cycled WSNs. We propose a Delay-Efficient Data Aggregation Scheduling (DEDAS) scheme to generate a collision-free schedule and minimize the delay for data aggregation in duty-cycled WSNs. Our analysis and comprehensive simulation results indicate that our solution performs better than existing schemes.

Delay-Constrained Data Aggregation in VANETs

Abstract: Data aggregation has been recognized as an effective technique for reducing communication costs while obtaining useful aggregated information. In this paper, we study the crucial problem of delay-constrained data aggregation in vehicular ad hoc networks (VANETs), which has not been well studied in the literature. With the analysis based on real traces, we observe that there is heterogeneity with node contact patterns, which indicates that some nodes contact other nodes more frequently. Motivated by this observation, we propose an approach called aTree . The centralized aTree first constructs a data aggregation tree based on the shortest path tree and then assigns a waiting time budget to each node on the tree based on dynamic programming. We further develop a distributed aTree , in which a shortest path tree is built in a distributed fashion, and nodes determine their waiting time budgets collaboratively. We have performed extensive simulations on real taxi traces, and results show that our aTree schemes incur much lower transmission overhead while achieving the same performance compared with other schemes.

Sensor Scheduling for Aggregate Monitoring inWireless Sensor Networks

Abstract: Most of the applications of wireless sensor networks involve primarily data collection with in-network processing in which continuous aggregate queries are posed and processed. There are two principle concerns with this type of applications. First, due to the use of batteries, limited power resource has been identified as a major challenge in deploying wireless sensor networks. Second, data is usually expected to be gathered as soon as possible to facilitate the monitoring of and the response to the physical phenomena. In this paper, we tackle these challenges through sensor state scheduling. The proposed technique is based on the observation that there are two types of traffic in sensor networks designed for data aggregation, bottom-up and top-down within an abstract tree structure. We show that it is possible to achieve deterministic schedules for data aggregation with very good performance. Specifically, we develop greedy algorithms to schedule transmission and listening operations for each sensor node to achieve collision- free communication. We show that the schedules can maximize the time sensor nodes spent on low-power states which helps achieve great energy efficiency, as well as allow fast data aggregation.