Exploiting Overlapping Channels for Minimum Power Configuration in Real-Time Sensor Networks

Wang, Xiaodong; Wang, Xiaorui; Xing, Guoliang; Yao, Yanjun

doi:10.1007/978-3-642-11917-0_7

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Consider that the links are lossy and unreliable, we use expected transmission counts (ETX) as the link quality indicator, as well as a measure of delay. End-to-end transmission count is a commonly used metric to represent end-to-end delay, as a larger transmission count leads to a longer delay [5]. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes.…”

Section: Introductionmentioning

confidence: 99%

A Lifetime-Preserving and Delay-Constrained Data Gathering Tree for Unreliable Sensor Networks

Li¹

2012

KSII TIIS

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A tree routing structure is often adopted for many-to-one data gathering and aggregation in sensor networks. For real-time scenarios, considering lossy wireless links, it is an important issue how to construct a maximum-lifetime data gathering tree with delay constraint. In this work, we study the problem of lifetime-preserving and delay-constrained tree construction in unreliable sensor networks. We prove that the problem is NP-complete. A greedy approximation algorithm is proposed. We use expected transmissions count (ETX) as the link quality indicator, as well as a measure of delay. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes by pruning and grafting its sub-tree. The complexity of the proposed algorithm is O(N 4 ). Finally, extensive simulations are carried out to verify our approach. Simulation results show that our algorithm provides longer lifetime in various situations compared to existing data gathering schemes.

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Section: Introductionmentioning

confidence: 99%

A Lifetime-Preserving and Delay-Constrained Data Gathering Tree for Unreliable Sensor Networks

Li¹

2012

KSII TIIS

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“…Five sensors are used for each rack because it is usually preferable not to put too many wireless sensors on a rack for the considerations of space and cost, due to the very dense installation of high-density servers (e.g., up to 128 blade servers per rack). In addition, a highly dense deployment of sensors may cause the wireless network to have significantly increased levels of channel contention and thus, unacceptably long communication delays [9] [10]. However, such a simplistic sensor deployment strategy may result in an unnecessarily degraded detection probability.…”

Section: Introductionmentioning

confidence: 99%

Towards Optimal Sensor Placement for Hot Server Detection in Data Centers

Wang

Xing

et al. 2011

2011 31st International Conference on Distributed Computing Systems

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Abstract-Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its non-intrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problem of sensor placement for hot server detection in a data center as a constrained optimization problem. We then propose a novel placement scheme based on Computational Fluid Dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results also show that the proposed solution outperforms a commonly used placement solution in terms of detection probability.

show abstract

“…Five sensors are used for each rack because it is usually preferable not to put too many wireless sensors on a rack for the considerations of space and cost, due to the very dense installation of high-density servers (e.g., up to 128 blade servers per rack). In addition, a highly dense deployment of sensors may cause the wireless network to have significantly increased levels of channel contention and thus, unacceptably long communication delays [13], [14]. However, such a simplistic sensor placement strategy may result in an unnecessarily degraded detection probability.…”

mentioning

confidence: 99%

Intelligent Sensor Placement for Hot Server Detection in Data Centers

Wang

Xing

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its nonintrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problems of sensor placement for hot server detection in a data center as constrained optimization problems in two different scenarios. We then propose a novel placement scheme based on computational fluid dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results in a large-scale data center with 32 racks also show that the proposed solution outperforms several commonly used placement solutions in terms of detection probability.

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Exploiting Overlapping Channels for Minimum Power Configuration in Real-Time Sensor Networks

Cited by 7 publications

References 28 publications

A Lifetime-Preserving and Delay-Constrained Data Gathering Tree for Unreliable Sensor Networks

A Lifetime-Preserving and Delay-Constrained Data Gathering Tree for Unreliable Sensor Networks

Towards Optimal Sensor Placement for Hot Server Detection in Data Centers

Intelligent Sensor Placement for Hot Server Detection in Data Centers

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