WO2011011811A1 - Acquisition comprimée tenant compte des aspects énergétiques - Google Patents

Acquisition comprimée tenant compte des aspects énergétiques Download PDF

Info

Publication number
WO2011011811A1
WO2011011811A1 PCT/AU2010/000917 AU2010000917W WO2011011811A1 WO 2011011811 A1 WO2011011811 A1 WO 2011011811A1 AU 2010000917 W AU2010000917 W AU 2010000917W WO 2011011811 A1 WO2011011811 A1 WO 2011011811A1
Authority
WO
WIPO (PCT)
Prior art keywords
energy
node
wireless sensor
nodes
rechargeable wireless
Prior art date
Application number
PCT/AU2010/000917
Other languages
English (en)
Inventor
Rajib Kumar Rana
Wen Hu
Original Assignee
Commonwealth Scientific And Industrial Research Organisation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2009903536A external-priority patent/AU2009903536A0/en
Application filed by Commonwealth Scientific And Industrial Research Organisation filed Critical Commonwealth Scientific And Industrial Research Organisation
Publication of WO2011011811A1 publication Critical patent/WO2011011811A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/20—Arrangements in telecontrol or telemetry systems using a distributed architecture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/40—Arrangements in telecontrol or telemetry systems using a wireless architecture
    • H04Q2209/43—Arrangements in telecontrol or telemetry systems using a wireless architecture using wireless personal area networks [WPAN], e.g. 802.15, 802.15.1, 802.15.4, Bluetooth or ZigBee
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
    • H04Q2209/88—Providing power supply at the sub-station
    • H04Q2209/886—Providing power supply at the sub-station using energy harvesting, e.g. solar, wind or mechanical
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
    • H04W52/0287—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level changing the clock frequency of a controller in the equipment
    • H04W52/029—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level changing the clock frequency of a controller in the equipment reducing the clock frequency of the controller
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/18—Network protocols supporting networked applications, e.g. including control of end-device applications over a network

Definitions

  • This invention concerns an energy-aware compressive sensing approach (ECSA)for rechargeable wireless sensor networks (WSN), for instance to capture high frequency micro-climate signals.
  • ECSA energy-aware compressive sensing approach
  • WSN rechargeable wireless sensor networks
  • the invention is a method for operating the network, software for performing the method, and a sensor node for such a network.
  • WSN Wireless Sensor Networks
  • Such networks may have up to two hundred nodes to collect microclimate data for enhancing knowledge of rain forest restoration processes.
  • Fig. 1 is an aerial photograph showing eight sensor nodes deployed to collect temperature, humidity, soil moisture, wind speed and wind direction observations. Of these microclimate signals, wind speed and wind direction are the most important for studying the forest regeneration processes.
  • Table 1 summarizes the sampling frequencies required by different micro climate signals:
  • Fig. 2 shows the current harvested by sensor 5 of Fig. 1 which is located in the forest. It can be seen that this location predicates the desired sampling frequency cannot be ensured.
  • the conventional energy conservation approach in wireless sensor networks is duty- cycling between samples.
  • This approach causes the duty cycle of the sensors to be low, and it can usefully be applied to maintain the low sampling frequency of microclimate signals like soil moisture and temperature.
  • the sampling rate across the network is governed by the energy profile of the lowest energy profile sensor. This make high frequency sensing difficult. For instance, duty cycling sensors for high frequency signals like wind speed and wind direction encounters the problem that turning the sensors on and off between samples must be so rapid that it interferes with the proper sensing of these phenomena.
  • the wind sensors are turned on only for five seconds in every five minutes. During the five seconds the sensors sample at 4 Hz and the average of the twenty samples collected during this five seconds is recorded as the reading for the five minute interval.
  • the invention is a rechargeable wireless sensor network comprising plural distributed sensor nodes that are each configured to harvest energy from local renewable energy resources, and to periodically switch on and employ compressive sensing techniques to collect data samples representing a sensed phenomenon; wherein the compressive sensing employs a sparse projection matrix.
  • sensor nodes using the invention will capture only sampled information about the phenomenon being sensed.
  • Downstream processing for instance at the network server, reconstructs the complete signal from that transmitted samples using known compressive sensing techniques.
  • the reconstruction framework is especially useful for reconstructing high frequency micro-climate signals, such as wind speed or wind direction signals, from sparse samples.
  • the invention may deliver improved reconstruction accuracy with higher probability for high frequency phenomenon. Alternatively, or in addition, it may increase the supported duty cycle of the sensor nodes.
  • the degree of sparsity of the projection matrix used for compressive sensing may be selected in order to reduce the sampling frequency (probability) of a sensor node.
  • the sampling rates of sensor nodes across the network will not be uniform, but may take into account the energy profile of each sensor.
  • the technique may therefore be said to be an 'energy-aware' CS approach (ECSA) which distributes sampling workload to different nodes based on their solar current harvesting capacity.
  • ECSA 'energy-aware' CS approach
  • the projection matrix may use the following distribution function:
  • ⁇ controls the sparsity of the projection matrix.
  • a sampling probability is assigned to each node based on its energy profile, with a higher sampling probability being assigned to nodes that have higher energy harvesting opportunity.
  • the sampling probability gi for node i is given by:
  • This model ensures that even the minimum current harvesting node does not have a sampling probability of zero; rather the probability is much smaller compared with those nodes with a higher energy harvesting opportunity.
  • the projection matrix may use the following distribution function:
  • the nodes use information about the number of projections to distributedly generate projections and send their measurements to central server.
  • Any suitable transform domain may be for the sensed signals provided they result in a compressible (that is redundant) representation of the sensed signal.
  • Examples include: Discrete Cosine Transform (DCT), Fourier Transform, as well as different wavelets such as Haar, Daubechies (D4), Symlets, Coiflets, and Splines.
  • the energy-aware CS within a given energy budget, may achieve about 80% higher reconstruction accuracy compared to traditional CS.
  • This invention has the potential to dramatically improve the utilisation of sensor nodes across a sensor network. Power is a limiting factor of nearly all outdoor sensor networks and maximising the duty cycle of all the nodes given the power generation and capture constraints is a major research challenge.
  • This invention could deliver significant gains across all outdoor sensor networks, resulting in either higher quality data or potentially fewer nodes being needed for a given application.
  • the invention is a method for operating a rechargeable wireless sensor network having plural distributed sensor nodes, comprising the steps of:
  • While the node is activated, employing compressive sensing techniques to collect data samples representing a sensed phenomenon.
  • the compressive sensing employs a sparse projection matrix.
  • the invention is software for performing the method.
  • the invention is a rechargeable wireless sensor node in a distributed network, the sensor node comprising:
  • a facility to periodically activate compressive sensing techniques to collect data samples representing a sensed phenomenon A facility to periodically activate compressive sensing techniques to collect data samples representing a sensed phenomenon.
  • a transmitter to transmit collected data samples.
  • Fig. 1 is an aerial map of eight Sensor Nodes deployed in a trial network.
  • Fig. 2 is a graph showing solar current harvested from the eight sensors of the trial network.
  • Fig. 3 (a) is a graph showing the percentage of largest coefficients required to approximate wind speed signals using different transform domains.
  • Fig. 3(b) is a graph showing the percentage of largest coefficients required to approximate wind direction signals using different transform domains.
  • Fig. 4(a) is a graph comparing the compressibility of the DCT and Haar coefficients with the selected projection matrix for wind speed signals.
  • Fig. 4(b) is a graph comparing the compressibility of the DCT and Haar " coefficients with the selected projection matrix for wind direction signals.
  • Fig. 5(a) is a graph comparing the reconstruction accuracy of wind speed signals using CS and adaptive CS.
  • Fig. 5(b) is a graph comparing the reconstruction accuracy of wind direction signals using CS and adaptive CS.
  • Fig. 6(a) is a graph comparing the reconstruction accuracy of wind speed signals generated using CS with a benchmark.
  • Fig. 6(b) is a graph comparing the reconstruction accuracy of wind direction signals generated using CS with a benchmark.
  • Fig. 7(a) is a graph comparing the reconstruction of wind speed signals using CS and Energy aware CS.
  • Fig. 7(b) is a graph comparing the reconstruction of wind direction signals using CS and Energy aware CS.
  • Fig. 8(a) is a graph showing the reconstruction accuracy of wind speed signals at different duty cycles.
  • Fig. 8(b) is a graph showing the reconstruction accuracy of wind direction signals at different duty cycles.
  • Compressive Sensing Provided a signal has sufficient redundancy, Compressive Sensing (CS) is able to reconstruct the signal from a small number of measurements.
  • CS Compressive Sensing
  • CS is able to exploit this correlation and reconstruct the signal form partial captured samples. The underlying objective is to reduce the sampling cost of energy-hungry sensors.
  • CS assumes that every natural signal has a compressible (or redundant) representation in some transform domain.
  • a signal u e R N has a compressible representation in a domain ⁇ , if the coefficients of u in ⁇ decays exponentially.
  • the best k-term approximation of u is achieved by keeping the largest k transform coefficients and discarding the remaining as zero.
  • CS also may also use a more general form of sampling. Instead of sampling the elements of the unknown vector w e R ⁇ (a multidimensional signal can be easily turned into a vector), CS uses projections.
  • the projection of a vector u on another vector ⁇ e R ⁇ is given by the inner product of ⁇ and u. Assuming that we perform L such projections using L linearly independent projection vectors, we can compactly write the outcome of these projections by using the equation:
  • Such projections matrices are dense, since all the elements of the projection matrix are non-zero, and therefore they require the complete signal to be captured.
  • ⁇ controls the sparsity of the projection matrix.
  • 1/ ⁇ MN which gives the expected number of non-zero coefficients per row to be one. Therefore, having an L * N projection matrix, the expected number of sensors needed to be queried is L.
  • the reconstruction error can also be calculated. And, it is also possible to establish a relation between the sampling probability and the reconstruction accuracy.
  • Fig. 1 shows the spatial position of the nodes 10 of a trial deployment. It can be observed that node 5 is in the deep forest, whereas the rest of the nodes are in open fields. Most environmental sensing deployments, such as [8, 11] have similar node placements.
  • nodes are assigned a model based sampling probability. In this model we assign a sampling probability g t to a node based on its energy profile. A higher value of g ⁇ is assigned to a node i that has higher current harvest opportunity.
  • the projection matrix for the energy-aware sensing approach has the following entries:
  • Each sensor i e J ⁇ generates a set of independent random variables ⁇ ⁇ , ..., ⁇ ,w. For each i, if ⁇ y ⁇ 0, sensory ' asks sensor i to take a sample at time t + 1.
  • Each sensors/ e J ⁇ also send the corresponding index i, where ⁇ y ⁇ 0 to the central server.
  • the values of/ are used to create the corresponding row in the projection matrix for the sensor /.
  • a number of candidate transform domains including DCT, Fourier and different wavelets such as Haar, Daubechies (D4), Symlets, Coiflets, and Splines etc.
  • Result presented in Figs. 3 show the different percentage of coefficients required to approximate the signals within a given reconstruction error. This result is averaged over all the 4320 snapshots.
  • Fig. 5 shows the reconstruction performance of the CS based reconstruction framework for both of the microclimate signals.
  • the reconstruction accuracy is dependent on the compressibility of the signals. For instance, from Figs. 3 we observe that using 40% of the Haar coefficients the reconstruction error is 0.25 and 0.2 for wind speed and wind direction respectively, i.e., wind direction signal is more compressible in compared to the wind speed signal. Therefore, the wind direction signal being relatively more compressible than the wind speed signal has lower sampling requirements.
  • CS can offer an average 80% reduction in reconstruction error over different sampling probabilities where the reduction decreases gradually from lower duty cycle to higher duty cycle.
  • Actuators A Physical, 147(2):449 - 455, 2008. 17 [16] Sundeep Pattern, Bhaskar Krishnamachari, and Ramesh Govindan. The impact of spatial correlation on routing with compression in wireless sensor networks. ACM Trans. Sen. Netw., 4(4): 1-33, 2008.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Arrangements For Transmission Of Measured Signals (AREA)

Abstract

Cette invention concerne une approche d'acquisition comprimée tenant compte des aspects énergétiques (ECSA) pour des réseaux de capteurs sans fil (WSN) rechargeables, par exemple pour capturer des signaux microclimatiques haute fréquence. Selon un premier aspect, l'invention se rapporte à un réseau de capteurs sans fil rechargeables. Le réseau de capteurs sans fil rechargeables comprend une pluralité de nœuds de capteurs répartis qui sont chacun configurés pour recueillir l'énergie à partir de ressources énergétiques renouvelables locales. Ils fonctionnent également pour se mettre en marche de façon périodique et utilisent des techniques d'acquisition comprimée pour collecter des échantillons de données représentant un phénomène détecté. L'acquisition compressive utilise une matrice de projection clairsemée. Selon d'autres aspects, l'invention se rapporte à un procédé permettant de faire fonctionner le réseau, à un logiciel permettant d'exécuter le procédé et à un nœud de capteur pour un tel réseau.
PCT/AU2010/000917 2009-07-29 2010-07-19 Acquisition comprimée tenant compte des aspects énergétiques WO2011011811A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2009903536A AU2009903536A0 (en) 2009-07-29 Energy-Aware Compressive Sensing
AU2009903536 2009-07-29

Publications (1)

Publication Number Publication Date
WO2011011811A1 true WO2011011811A1 (fr) 2011-02-03

Family

ID=43528623

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2010/000917 WO2011011811A1 (fr) 2009-07-29 2010-07-19 Acquisition comprimée tenant compte des aspects énergétiques

Country Status (1)

Country Link
WO (1) WO2011011811A1 (fr)

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102802199A (zh) * 2012-06-28 2012-11-28 重庆大学 基于压缩感知的无线传感器实时监测系统的数据处理方法
US20130151924A1 (en) * 2011-12-08 2013-06-13 Harris Corporation, Corporation Of The State Of Delaware Data system for interfacing with a remote data storage facility using compressive sensing and associated methods
CN103209436A (zh) * 2013-01-28 2013-07-17 南开大学 一种基于压缩传感理论的多参量信息融合稀疏模型
CN103220778A (zh) * 2013-03-11 2013-07-24 哈尔滨工业大学 一种基于无线传感器网络的移动节点队形变换方法及实现装置
CN103249064A (zh) * 2012-02-08 2013-08-14 无锡国科微纳传感网科技有限公司 一种无线传感器网络数据收集方法及系统
WO2014096501A1 (fr) * 2012-12-17 2014-06-26 Nokia Corporation Procédés, appareil et programmes d'ordinateur pour l'obtention de données
CN104219683A (zh) * 2014-08-20 2014-12-17 北京农业信息技术研究中心 农田无线传感器网络可再生能源节点部署方法及系统
CN107708086A (zh) * 2017-08-16 2018-02-16 昆明理工大学 一种无线传感器和执行器网络的移动能量补充方法
WO2018140405A1 (fr) * 2017-01-24 2018-08-02 Intel Corporation Détection de compression pour agrégation de données à faible consommation d'énergie dans un réseau de capteurs sans fil
CN108924148A (zh) * 2018-07-18 2018-11-30 中南大学 一种多源信号协同压缩感知数据恢复方法
CN109195164A (zh) * 2018-09-27 2019-01-11 南京航空航天大学 无线传感器网络中基于扰动压缩感知的数据传输安全防护方法
CN109612534A (zh) * 2019-01-11 2019-04-12 中灌顺鑫华霖科技发展有限公司 农事数据采集传输方法
US10282875B2 (en) 2015-12-11 2019-05-07 International Business Machines Corporation Graph-based analysis for bio-signal event sensing
CN111405516A (zh) * 2020-03-25 2020-07-10 西安电子科技大学 无线传感网中基于随机游走的数据收集方法
CN112004238A (zh) * 2020-08-07 2020-11-27 天津师范大学 一种基于nmf和2-svd-qr的无线传感器网络优化方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MENG, J. ET AL.: "SPARSE EVENT DETECTION IN WIRELESS SENSOR NETWORKS USING COMPRESSIVE SENSING", 43RD ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, 2009, CISS 2009, 18-20 MARCH 2009, 18 March 2009 (2009-03-18) - 20 March 2009 (2009-03-20), pages 181 - 185, XP031468596 *
SOMASUNDARA, AA ET AL.: "CONTROLLABLY MOBILE INFRASTRUCTURE FOR LOW ENERGY EMBEDDED NETWORKS", IEEE TRANSACTIONS ON MOBILE COMPUTING, vol. 5, no. 8, 8 August 2006 (2006-08-08), pages 958 - 973, XP001546036, DOI: doi:10.1109/TMC.2006.109 *

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130151924A1 (en) * 2011-12-08 2013-06-13 Harris Corporation, Corporation Of The State Of Delaware Data system for interfacing with a remote data storage facility using compressive sensing and associated methods
US8516340B2 (en) * 2011-12-08 2013-08-20 Harris Corporation Data system for interfacing with a remote data storage facility using compressive sensing and associated methods
CN103249064A (zh) * 2012-02-08 2013-08-14 无锡国科微纳传感网科技有限公司 一种无线传感器网络数据收集方法及系统
CN102802199B (zh) * 2012-06-28 2014-10-29 重庆大学 基于压缩感知的无线传感器实时监测系统的数据处理方法
CN102802199A (zh) * 2012-06-28 2012-11-28 重庆大学 基于压缩感知的无线传感器实时监测系统的数据处理方法
US9838887B2 (en) 2012-12-17 2017-12-05 Nokia Technologies Oy Methods, apparatus and computer programs for obtaining data
WO2014096501A1 (fr) * 2012-12-17 2014-06-26 Nokia Corporation Procédés, appareil et programmes d'ordinateur pour l'obtention de données
CN103209436A (zh) * 2013-01-28 2013-07-17 南开大学 一种基于压缩传感理论的多参量信息融合稀疏模型
CN103220778A (zh) * 2013-03-11 2013-07-24 哈尔滨工业大学 一种基于无线传感器网络的移动节点队形变换方法及实现装置
CN103220778B (zh) * 2013-03-11 2015-08-19 哈尔滨工业大学 一种基于无线传感器网络的移动节点队形变换方法及实现装置
CN104219683B (zh) * 2014-08-20 2018-05-18 北京农业信息技术研究中心 农田无线传感器网络可再生能源节点部署方法及系统
CN104219683A (zh) * 2014-08-20 2014-12-17 北京农业信息技术研究中心 农田无线传感器网络可再生能源节点部署方法及系统
US10282875B2 (en) 2015-12-11 2019-05-07 International Business Machines Corporation Graph-based analysis for bio-signal event sensing
WO2018140405A1 (fr) * 2017-01-24 2018-08-02 Intel Corporation Détection de compression pour agrégation de données à faible consommation d'énergie dans un réseau de capteurs sans fil
US10149131B2 (en) 2017-01-24 2018-12-04 Intel Corporation Compressive sensing for power efficient data aggregation in a wireless sensor network
CN107708086B (zh) * 2017-08-16 2020-04-07 昆明理工大学 一种无线传感器和执行器网络的移动能量补充方法
CN107708086A (zh) * 2017-08-16 2018-02-16 昆明理工大学 一种无线传感器和执行器网络的移动能量补充方法
CN108924148A (zh) * 2018-07-18 2018-11-30 中南大学 一种多源信号协同压缩感知数据恢复方法
CN109195164A (zh) * 2018-09-27 2019-01-11 南京航空航天大学 无线传感器网络中基于扰动压缩感知的数据传输安全防护方法
CN109612534A (zh) * 2019-01-11 2019-04-12 中灌顺鑫华霖科技发展有限公司 农事数据采集传输方法
CN111405516A (zh) * 2020-03-25 2020-07-10 西安电子科技大学 无线传感网中基于随机游走的数据收集方法
CN111405516B (zh) * 2020-03-25 2022-06-10 西安电子科技大学 无线传感网中基于随机游走的数据收集方法
CN112004238A (zh) * 2020-08-07 2020-11-27 天津师范大学 一种基于nmf和2-svd-qr的无线传感器网络优化方法
CN112004238B (zh) * 2020-08-07 2024-01-26 天津师范大学 一种基于nmf和2-svd-qr的无线传感器网络优化方法

Similar Documents

Publication Publication Date Title
WO2011011811A1 (fr) Acquisition comprimée tenant compte des aspects énergétiques
Santini et al. An adaptive strategy for quality-based data reduction in wireless sensor networks
Liu et al. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation
Cheng et al. O (ε)-approximation to physical world by sensor networks
Ukil et al. IoT data compression: Sensor-agnostic approach
Tayeh et al. A spatial-temporal correlation approach for data reduction in cluster-based sensor networks
US20070118301A1 (en) System to monitor the health of a structure, sensor nodes, program product, and related methods
Chen et al. Energy efficient signal acquisition via compressive sensing in wireless sensor networks
Wang et al. Multiresolution spatial and temporal coding in a wireless sensor network for long-term monitoring applications
Al-Qurabat et al. Important extrema points extraction-based data aggregation approach for elongating the WSN lifetime
Rana et al. Energy-aware sparse approximation technique (east) for rechargeable wireless sensor networks
Yang et al. Joltik: enabling energy-efficient" future-proof" analytics on low-power wide-area networks
Padalkar et al. Data gathering in wireless sensor network for energy efficiency with and without compressive sensing at sensor node
Le Borgne et al. Unsupervised and supervised compression with principal component analysis in wireless sensor networks
Aram et al. Improving lifetime in wireless sensor networks using neural data prediction
Alexandrov Ad-hoc Kalman filter based fusion algorithm for real-time wireless sensor data integration
Zordan et al. Rate-distortion classification for self-tuning IoT networks
Maia et al. A framework for processing complex queries in wireless sensor networks
Skhiri et al. Power aware wireless sensor networks based on compressive sensing
Alwakeel et al. Exploiting temporal correlation of sparse signals in wireless sensor networks
Verma et al. Latent variables based data estimation for sensing applications
Goyal et al. Sparse signal recovery through regularized orthogonal matching pursuit for WSNs applications
Abdal-Kadhim et al. Investigation of wireless sensor node power consumption profile powered by heterogeneous hybrid energy harvesters with EPDD management algorithm
Liaskovits et al. Leveraging redundancy in sampling-interpolation applications for sensor networks
Zhao et al. A systematic probabilistic approach to energy-efficient and robust data collections in wireless sensor networks

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10803720

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10803720

Country of ref document: EP

Kind code of ref document: A1