Power aware routing for sensor databases

Buragohain, Chiranjeeb; Agrawal, Divyakant; Suri, Subhash

doi:10.1109/infcom.2005.1498455

Cited by 86 publications

(79 citation statements)

References 13 publications

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“…Given the traffic load, an integer programming can be formulated to minimize the maximum node energy consumption [4], where the data routes and the corresponding power levels are identified. Followup studies with different objectives or constraints can be found in [13] [3]. They generally have assumed that the deployment of the network nodes is given, which often follows a random distribution.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In many such networks, the sensor nodes are randomly deployed in massive quantities, and each node may act both as a data collector and a traffic relay. This is also a common assumption made in many existing works on modeling and protocol optimization, and the focus thus has been put on optimizing topology control [18] [22][11] [12] and routing design [20][4] [13] [3] with the given network topologies.…”

Section: Introductionmentioning

confidence: 99%

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Traffic-Aware Relay Node Deployment for Data Collection in Wireless Sensor Networks

Feng

Wang

Liu

2009

2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

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Abstract-Wireless sensor networks have been widely used for ambient data collection in diverse environments. While in many such networks the sensor nodes are randomly deployed in massive quantity, there is a broad range of applications advocating manual deployment. A typical example is structure health monitoring, where the sensors have to be placed at critical locations to fulfill civil engineering requirements. The raw data collected by the sensors can then be forwarded to a remote base station (the sink) through a series of relay nodes.In the wireless communication context, the operation time of a battery-limited relay node depends on its traffic volume and communication range. Hence, although not bounded by the civilengineering-like requirements, the locations of the relay nodes have to be carefully planned to achieve the maximum network lifetime. The deployment has to not only ensure connectivity between the data sources and the sink, but also accommodate the heterogeneous traffic flows from different sources and the dominating many-to-one traffic pattern.Inspired by the uniqueness of such application scenarios, in this paper, we present an in-depth study on the trafficaware relay node deployment problem. We develop optimal solutions for the simple case of one source node, both with single and multiple traffic flows. We show however that the general form of the deployment problem is difficult, and the existing connectivity-guaranteed solutions cannot be directly applied here. We then transform our problem into a generalized version of the Euclidean Steiner Minimum Tree problem (ESMT). Nevertheless, we face further challenges as its solution is in continuous space and may yield fractional numbers of relay nodes, where simple rounding of the solution can lead to poor performance. We thus develop algorithms for discrete relay node assignment, together with local adjustments that yield high-quality practical solutions. Our solution has been evaluated through both numerical analysis and ns-2 simulations and compared with state-of-the-art approaches. The results show that it achieves up to 6 to 14 times improvement on the network lifetime over the existing trafficoblivious strategies.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic-Aware Relay Node Deployment for Data Collection in Wireless Sensor Networks

Feng

Wang

Liu

2009

2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

View full text Add to dashboard Cite

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“…Elkin et al [6] gave an Ω( 1/ log n )-approximation for the discrete version of Multiple Topology Convergecast problem. Regarding the case without aggregation, some partial results were given in [25], [1] and [14]. The paper [25] considered the conditional aggregation where data from one node can be compressed in the presence of data from other nodes.…”

Section: Previous Workmentioning

confidence: 99%

Improved Approximation Algorithms for Maximum Lifetime Problems in Wireless Networks

Nutov

Segal

2009

Algorithmic Aspects of Wireless Sensor Networks

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A wireless ad-hoc network is a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited battery charge. The battery charge is then reduced after each transmission, depending on the transmission distance. One of the major problems in wireless network design is to route network traffic efficiently so as to maximize the network lifetime, i.e., the number of successful transmissions. In this paper we consider Rooted Maximum Lifetime Broadcast/Convergecast problems in wireless settings. The instance consists of a directed graph G = (V, E) with edge-weights {w(e) : e ∈ E}, node capacities {b(v) : v ∈ V }, and a root r. The goal is to find a maximum size collection {T 1 , . . . , T k } of Broadcast/Convergecast trees rooted at r so that, where δ T (v) is the set of edges leaving v in T . In the Single Topology version all the Broadcast/Convergecast trees T i are identical. We present a number of polynomial time algorithms giving constant ratio approximation for various broadcast and convergecast problems, improving previously known result of Ω( 1/ log n )-approximation by [6]. We also consider a generalized Rooted Maximum Lifetime Mixedcast problem, where we are also given an integer γ ≥ 0, and the goal is to find the maximum integer k so that k Broadcast and γk Convergecast rounds can be performed.

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“…Ordered properties such as QUANTILE are studied in [9]. A recent result in [6] considers power-aware routing and aggregation query processing together, building energy-efficient routing trees explicitly for aggregation queries.…”

Section: A Related Workmentioning

confidence: 99%

“…Our goal is to specify the layer number l which will reduce the number of sensors, as well as the number of messages, used in responding to a query. Once l is determined, the distribution of the query and the collection of the data can be performed in a number of ways, such as that proposed in [6]. In fact, once the layer to be used for a particular query has been identified, the particular routing/aggregation algorithm to be used is transparent to our algorithm.…”

Section: Data Collection and Aggregationmentioning

confidence: 99%

A layered architecture for delay sensitive sensor networks

Wang

Long

Ergün

2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 20

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Abstract-Sensor networks are powerful tools for performing monitoring and surveillance tasks over large areas. A sensor is a cheap, simple device with low power and limited capabilities. In a sensor network a large number of sensors are deployed to span the whole area to be monitored. Due to the simplicity and the large quantity of the sensors involved, collecting data from a sensor network can be time and energy inefficient.In this paper, we investigate making the data gathering task from a sensor network more efficient by using a randomized, layered architecture. The layers in our architecture are constructed in a distributed fashion, with each sensor deciding locally on what layers it will exist. The key property of our technique is that the information is collected from one layer of the architecture containing a small subset of the sensors, resulting in fewer hops and thus smaller data in data aggregation. We provide provably correct results for the delay incurred and the accuracy of the results.In the context of our new techniques, we also explore ways to speed up the data gathering process even further, such as using history information. In addition, we consider how to optimize the structure of our system so that the energy consumption will be evenly distributed among each sensor, thus extending the overall lifetime of the entire network.

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Power aware routing for sensor databases

Cited by 86 publications

References 13 publications

Traffic-Aware Relay Node Deployment for Data Collection in Wireless Sensor Networks

Traffic-Aware Relay Node Deployment for Data Collection in Wireless Sensor Networks

Improved Approximation Algorithms for Maximum Lifetime Problems in Wireless Networks

A layered architecture for delay sensitive sensor networks

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