Proceedings ArticleDOI
Compressive data gathering for large-scale wireless sensor networks
Chong Luo,Feng Wu,Jun Sun,Chang Wen Chen +3 more
- pp 145-156
TLDR
This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks and shows the efficiency and robustness of the proposed scheme.Abstract:
This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.read more
Citations
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Journal ArticleDOI
Compressive sensing: From theory to applications, a survey
TL;DR: Compressive sensing is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem and different areas of its application are highlighted with a major emphasis on communications and network domain.
Proceedings ArticleDOI
Compressed data aggregation for energy efficient wireless sensor networks
TL;DR: This paper investigates the application of CS to data collection in wireless sensor networks, and aims at minimizing the network energy consumption through joint routing and compressed aggregation, and proposes a mixed-integer programming formulation along with a greedy heuristic.
Journal ArticleDOI
EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks
TL;DR: This work develops one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection, and proposes both a centralized heuristic to reduce its computational overhead and a distributed heuristics to make the algorithm scalable for large-scale network operations.
Journal ArticleDOI
CDC : Compressive Data Collection for Wireless Sensor Networks
TL;DR: This paper adopts a power-law decaying data model verified by real data sets and proposes a random projection-based estimation algorithm for this data model, which requires fewer compressed measurements and greatly reduces the energy consumption.
Journal ArticleDOI
Practical data compression in wireless sensor networks: A survey
TL;DR: A data compression scheme is one that can be used to reduce transmitted data over wireless channels, which leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
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An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
The capacity of wireless networks
Piyush Gupta,P. R. Kumar +1 more
TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.