Cost Based Optimal Data Sampling Rate in Wireless Sensor Network

Wireless Sensor Network (WSN) has an enormous scope of utilizations in detecting different parameters such as temperature, pressure, sound, pollution, etc. The sensed data in each sensor node are a valuable one. To communicate the information to the base station for further processing, a lot of strategies are available. Each sensor senses the data in different sampling rate depending upon the sudden raise in the sensing parameters. Data communication to the base station is very critical due to the dynamicity of the environment during the stipulated time.The sensed data should reach the base station before the data becomes invalid due to the violation of the deadline. In order to avoid deadline violation so that the sensed data becomes useless, this paper proposing a novel data collection algorithm based on the popular Earliest Deadline First (EDF) scheduling algorithm. The various simulation parameters are taken into account to verify the performance of the proposed method and the result shows that it achieves high throughput, low delay, high Packet Delivery Ratio (PDR) and low energy consumption.

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DATA TRAFFIC TRUST MODEL FOR CLUSTERED WIRELESS SENSOR NETWORK

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i1.261 ◽

2021 ◽

Vol 9 (1) ◽

pp. 1225-1229

Author(s):

Dr. Senthilkumar A, Dr. Lekashri S, Abhay Chaturvedi, Dr. R. Manikandan

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Trust Management ◽

Large Scale ◽

Sampling Rate ◽

Trust Model ◽

Sensor Nodes ◽

Wireless Sensor ◽

Data Traffic ◽

Data Packets

Trust is an essential parameter among sensor nodes to provide secured and successful communication. Many trust management schemes have been proposed earlier for large scale Wireless Sensor Network (WSN) however not cooperates well in terms of low dependability, memory overheads, large communication etc, therefore a system called Data Traffic Trust Scheme (DTTS) for clustered WSN is proposed here. Here the trust nodes are identified through the data traffic sampling rate. The trust rate is identified through the number of sent and receive data packets and the malicious packets are diagnosed through the un-matching packet rate. The simulation results are evaluated to show the efficiency for the proposed scheme.

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Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

Sensors ◽

10.3390/s18092974 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2974 ◽

Cited By ~ 5

Author(s):

Chunsheng Liu ◽

Hong Shan ◽

Bin Wang

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

High Efficiency ◽

Matrix Completion ◽

Sampling Rate ◽

Low Rank ◽

Wireless Sensor ◽

Bregman Divergence ◽

Positioning Accuracy ◽

Pulse Noise

One of the main challenges faced by wireless sensor network (WSN) localization is the positioning accuracy of the WSN node. The existing algorithms are arduous to use for dealing with the pulse noise that is universal and ineluctable in practical considerations, resulting in lower positioning accuracy. Aimed at this problem and introducing Bregman divergence, we propose in this paper a novel WSN localization algorithm via matrix completion (LBDMC). Based on the natural low-rank character of the Euclidean Distance Matrix (EDM), the problem of EDM recovery is formulated as an issue of matrix completion in a noisy environment. A regularized matrix completion model is established, smoothing the pulse noise by leveraging L 1 , 2 -norm and the multivariate function Bregman divergence is defined to solve the model to obtain the EDM estimator. Furthermore, node localization is available based on the multi-dimensional scaling (MDS) method. Multi-faceted comparison experiments with existing algorithms, under a variety of noise conditions, demonstrate the superiority of LBDMC to other algorithms regarding positioning accuracy and robustness, while ensuring high efficiency. Notably, the mean localization error of LBDMC is about ten times smaller than that of other algorithms when the sampling rate reaches a certain level, such as >30%.

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Dynamic definition of the sampling rate of data in Wireless Sensor Network with Adaptive Automata

IEEE Latin America Transactions ◽

10.1109/tla.2011.6096980 ◽

2011 ◽

Vol 9 (6) ◽

pp. 963-968 ◽

Cited By ~ 6

Author(s):

Ivairton Monteiro Santos ◽

Mara Andrea Dota ◽

Carlos Eduardo Cugnasca

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Sampling Rate ◽

Wireless Sensor ◽

Definition Of

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A Wireless Sensor Network for Precise Volatile Organic Compound Monitoring

International Journal of Distributed Sensor Networks ◽

10.1155/2012/820716 ◽

2012 ◽

Vol 8 (4) ◽

pp. 820716 ◽

Cited By ~ 10

Author(s):

Gianfranco Manes ◽

Giovanni Collodi ◽

Rosanna Fusco ◽

Leonardo Gelpi ◽

Antonio Manes

Keyword(s):

Wireless Sensor Network ◽

Organic Compound ◽

Sensor Network ◽

Volatile Organic Compound ◽

Sampling Rate ◽

Wireless Sensor ◽

Plant Management ◽

Petrochemical Plant ◽

Process Data ◽

Volatile Organic

A variety of methods have been developed to monitor VOC concentration in hazardous sites. The methods range from calculation to measurement, point measuring to remote sensing. Some are suited for leak detection, others for estimation of the annual emission or both. None of the following available methods comes close to the ideal method. A distributed instrument providing precise monitoring of Volatile Organic Compound (VOC) concentration in a petrochemical plant is described; it consists of a Wireless Sensor Network (WSN) platform whose nodes are equipped with meteorological/climatic sensors and VOC detectors. Internet connectivity is provided in real time at a one-minute sampling rate, thus providing environmental authorities and plant management with an unprecedented tool for immediate warning in case of critical events. The paper describes the WSN platform, detailing various units (gateways, nodes, detectors) and shows the features of scalability and reconfigurability, with minimal intrusiveness or obtrusiveness. Environmental and process data are forwarded to a remote server and made available to the authenticated users through a rich user interface that provides data rendering in various formats and worldwide access to data. A survey of the VOC detector technologies involved is also provided.

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A Two-Tiered Architecture of Wireless Sensor Network Using Multitier Protocols for Structural Health Monitoring

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.42 ◽

2012 ◽

Vol 468-471 ◽

pp. 42-45 ◽

Cited By ~ 1

Author(s):

Yue Zhou ◽

Shuai Liu ◽

Shi Tang

Keyword(s):

Energy Consumption ◽

Wireless Sensor Network ◽

Structural Health Monitoring ◽

Sensor Network ◽

Health Monitoring ◽

Local Area Network ◽

Sampling Rate ◽

Wireless Sensor ◽

Area Network ◽

Structural Health

The design of wireless sensor network for structural health monitoring systems requires high sampling rate, real-time communication, low-energy consumption, particularly in large-scale networks. However, some commonly available architecture and protocols are not fully suitable for this special application. In this paper, the requirements of wireless sensor network for structural health monitoring application is studied and a two-tiered architecture network where is an IEEE 802.11 wireless local area network on top of an IEEE 802.15.4 structure area network is proposed as a solution. OPNET Modeler is applied to analyze the performance of the network, and the simulation results show that the two-tiered architecture network provides more reliable services with reduced end-to-end delays and lower energy consumption in the underlying sensor network.

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A passive energy-based method for footstep impact localization, using an underfloor accelerometer sensor network with Kalman filtering

Journal of Vibration and Control ◽

10.1177/1077546319890520 ◽

2020 ◽

Vol 26 (11-12) ◽

pp. 941-951 ◽

Cited By ~ 1

Author(s):

Sa’ed Alajlouni ◽

Pablo Tarazaga

Keyword(s):

Sensor Network ◽

Kalman Filtering ◽

Sampling Rate ◽

Data Sampling ◽

Accelerometer Sensor ◽

Localization Accuracy ◽

New Energy ◽

Data Sampling Rate ◽

Floor Vibration ◽

Impact Localization

An underfloor accelerometer sensor network can be used to track occupants in an indoor environment using measurements of floor vibration induced by occupant footsteps. To achieve occupant tracking, each footstep impact location must first be estimated. This paper proposes a new energy-based algorithm for footstep impact localization. Compared to existing energy-based algorithms, the new algorithm achieves higher localization accuracy and removes a previously required calibration step (removal of the need to estimate floor-dependent parameters). Furthermore, the algorithm uses a much smaller data sampling rate compared to time of flight/arrival localization methods, which greatly reduces data and data-processing time. The new algorithm is a two-step location estimator: the first step is a coarse location estimate, with the second step as a fine location search through a nonlinear minimization problem. The performance of the proposed algorithm is evaluated using a single occupant walking experiment on an instrumented floor inside an operational smart building. This paper also demonstrates that higher localization accuracy is obtained using an additional Kalman filtering scheme.

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Compressed Sensing Based on the Characteristic Correlation of ECG in Hybrid Wireless Sensor Network

International Journal of Distributed Sensor Networks ◽

10.1155/2015/325103 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Weiwei Li ◽

Ting Jiang ◽

Ning Wang

Keyword(s):

Wireless Sensor Network ◽

Compressed Sensing ◽

Sensor Network ◽

Cellular Network ◽

Sampling Rate ◽

Wireless Sensor ◽

Signal Acquisition ◽

Biomedical Data ◽

Ecg Compression ◽

Successful Recovery

Hybrid wireless sensor network made up of wireless body area networks (WBANs) and cellular network provides support for telemedicine. In order to facilitate early diagnosis and treatment, WBANs collect and transmit crucial biomedical data to provide a continuous health monitoring by using various biomedical wireless sensors attached on or implanted in the human body. And then, collected signals are sent to a remote data center via cellular network. One of the features of WBAN is that its power consumption and sampling rate should be restricted to a minimum. Compressed sensing (CS) is an emerging signal acquisition/compression methodology which offers a prominent alternative to traditional signal acquisition. It has been proved that the successful recovery rate of multiple measurement vectors (MMV) model is higher than the single measurement vector (SMV) case. In this paper, we propose a simple algorithm of transforming the SMV model into MMV model based on the correlation of electrocardiogram (ECG), such that the MMV model can be used for general ECG signals rather than only several special signals. Experimental results show that its recovery quality is better than some existing CS-based ECG compression algorithms and sufficient for practical use.

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