An Energy-Efficient Dynamic Power Management in Wireless Sensor Networks

Lin, Chuan; He, Yanxiang; Xiong, Naixue

doi:10.1109/ispdc.2006.8

Cited by 74 publications

(52 citation statements)

References 20 publications

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“…In [31], empirical analysis results are used to reveal the relationship between the configuration parameters and the quality of the tracking application. It shows that empirical models [14], [28] can be effectively applied without sacrificing the data prediction accuracy. One useful insight from an empirical modeling approach is that data correlation can be utilized for other purposes [34], [35], such as monitoring applications.…”

Section: Related Workmentioning

confidence: 99%

“…Depending on system lifetime and the data fidelity requirement of an application, the empirical model can be constructed in different ways. Similar to the work of [14], we developed a cycle-based empirical model [15], which has been proven to be efficient for environment monitoring applications. The error predictor is mainly responsible for generating prediction errors, defined as e i , for each node i.…”

Section: Local Error Predictormentioning

confidence: 99%

“…However, these design approaches are isolated in that the management of data accuracy can only be used for each individual sensor. Thus, these techniques do not fully exploit the potential of network collaboration for data accuracy management under rapidly changing conditions in the environment, and their ability to tradeoff data accuracy with energy reduction is thus inherently limited [14], [28], [29]. Our work incorporates the advantages of both dynamic scheduling and sensors collaboration, seeking to optimize the tradeoff between energy and the accuracy of predictions.…”

Section: Related Workmentioning

confidence: 99%

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Collaborative Scheduling in Dynamic Environments Using Error Inference

Zhang¹,

Gü

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in WisconsinMinnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

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Section: Related Workmentioning

confidence: 99%

Section: Local Error Predictormentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Collaborative Scheduling in Dynamic Environments Using Error Inference

Zhang¹,

Gü

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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“…The switching and scheduling schemes help in power saving by controlling idle periods of the system [25]. The sleep state policy also improves energy efficiency to the greatest extent [26]. The anticipated workload of the different subsystems and the estimation of the task arrival rate at the scheduler are crucial preconditions for a DPM technique.…”

Section: Dynamic Power Managementmentioning

confidence: 99%

A review on stochastic approach for dynamic power management in wireless sensor networks

Pughat

Sharma

2015

Hum. Cent. Comput. Inf. Sci.

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Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states. During DPM, it is also required that the deadline of task execution and performance are not compromised. It is seen that operational level change can improve the energy efficiency of a system drastically (up to 90%). Hence, DPM policies have drawn considerable attention. This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power consumption. This survey paper is expected to trigger ideas for future research projects in power aware wireless sensor network arenas.

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“…Several approaches have been proposed at hardware and software levels to design energy efficient CPU, OS, algorithms, and communication protocols [1]. Dynamic power management (DPM) schemes have been proposed in [13][14][15] to reduce the power consumption by selectively turning off idle components, such as radio frequency (RF) transmitter, RF receiver, sensing device, A/D converter, and the sensor node.…”

Section: Related Workmentioning

confidence: 99%

DI-GEP: A New Lifetime Extending Algorithm for Target Tracking in Wireless Sensor Networks

Dai

Chen

2012

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are widely used in detecting, locating, and tracking moving objects. The cheap, low-powered, and energy-limited sensors that are set up in large areas may consume large portion of energy and disable the whole network. In this paper, a new energy-efficient method based on Distributed Incremental Gene Expression Programming (DI-GEP) is proposed to collaboratively mine moving patterns of moving targets in order to turn on/off some sensor nodes at certain time to save energy further. Meanwhile, an adjustable sliding window is designed to quickly train the latest collected location data in order to improve the efficiency of DI-GEP. The simulation results show that the proposed method effectively prolongs the network lifetime by around 25% compared with the EKF and ECPA.

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An Energy-Efficient Dynamic Power Management in Wireless Sensor Networks

Cited by 74 publications

References 20 publications

Collaborative Scheduling in Dynamic Environments Using Error Inference

Collaborative Scheduling in Dynamic Environments Using Error Inference

A review on stochastic approach for dynamic power management in wireless sensor networks

DI-GEP: A New Lifetime Extending Algorithm for Target Tracking in Wireless Sensor Networks

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