An energy conservation method for wireless sensor networks employing a blue noise spatial sampling technique

Perillo, M.; Ignjatović, Željko; Heinzelman, Wendi

doi:10.1145/984622.984640

Cited by 29 publications

(22 citation statements)

References 18 publications

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“…TinyOS [38] introduces random delays to break synchronization. Blue Noise Sampling [39] selects well-distributed nodes to awaken in order to achieve optimal field coverage.…”

Section: Related Workmentioning

confidence: 99%

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Younis

Fahmy

2004

IEEE Trans. on Mobile Comput.

4,235

2,207

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Abstract-Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in Oð1Þ iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

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“…TinyOS [38] introduces random delays to break synchronization. Blue Noise Sampling [39] selects well-distributed nodes to awaken in order to achieve optimal field coverage.…”

Section: Related Workmentioning

confidence: 99%

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Younis

Fahmy

2004

IEEE Trans. on Mobile Comput.

4,235

2,207

View full text Add to dashboard Cite

show abstract

“…Our work can be thought of as analogous to set k-cover, for the distinct regime of sampling-interpolation applications. Numerous efforts on efficiently building a spatial model for the sensed phenomenon also exist in the literature [12,24,34]. They are not applicable in our scenario, since they make some kind of assumption on the underlying phenomenon.…”

Section: Relation To Previous Workmentioning

confidence: 99%

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

Liaskovitis

Schurgers

2010

J Sign Process Syst

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We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.

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“…In particular, several papers deal with energy efficiency issues, aiming at prolonging the network lifetime as much as possible. The work in [16] considers that sensors can enter a low-power operational state (i.e., a sleep mode) and presents an algorithm to determine which sensor subsets should be selected to acquire data from an area of interest and which nodes should remain inactive to save energy. Note that in our paper we consider an irregular topology, which may be caused by nodes moving into a sleep state; however, we do not directly address energy efficiency or scheduling of the node sleep/activity periods.…”

Section: Related Workmentioning

confidence: 99%

Performance of Linear Field Reconstruction Techniques With Noise and Uncertain Sensor Locations

Nordio

Chiasserini

Viterbo

2008

IEEE Trans. Signal Process.

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Abstract-We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a high quality reconstruction of the field, with the mean square error (MSE) of the estimate as performance metric. In particular, we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the MSE, as well as its asymptotic expression in the case where the number of field-harmonics and the number of sensors grow to infinity, while their ratio is kept constant. Through numerical simulations, we show the validity of the asymptotic analysis, even for a small number of sensors. We provide some novel guidelines for the design of sensor networks when many parameters, such as field bandwidth, number of sensors, reconstruction quality, and sensor displacement characteristics, to be traded off.

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An energy conservation method for wireless sensor networks employing a blue noise spatial sampling technique

Cited by 29 publications

References 18 publications

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

Performance of Linear Field Reconstruction Techniques With Noise and Uncertain Sensor Locations

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