QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network

Panimozhi, K; Mahadevan, G.

doi:10.11591/ijece.v7i5.pp2713-2720

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The first one has increased orthogonality of the basis vectors. This is due to the similarity of the criterion [23] to SVD. Removing the positivity constraint but keeping the rank constraint, an orthogonal eigenvector basis is a solution.…”

Section: Image Compression Using Pnmfmentioning

confidence: 99%

RTL Implementation of image compression techniques in WSN

Deepthi

Rao²,

Prasad

2019

IJECE

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<p>The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.</p>

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Section: Image Compression Using Pnmfmentioning

confidence: 99%

RTL Implementation of image compression techniques in WSN

Deepthi

Rao²,

Prasad

2019

IJECE

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“…Routing is a method to find out a path between the source node and the destination node. Routing in WSN is really challenging due to the intrinsic characteristics that differentiate these networks from the other network and also considered the act of sending the information source to destination [15]. This section describes the most four Routing protocols in WSN, these are Low Energy Adaptive Clustering Hierarchy (LEACH), AODV (Ad-hoc on-demand vector routing), OLSR (Optimized Link State Routing) and DSR (Dynamic source routing).…”

Section: Routing Protocolsmentioning

confidence: 99%

Qualnet performances of grid-based clustering For WSN's routing protocols

Ismail

Mohsin

2020

IJEECS

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<span>The wireless sensor networks (WSNs) believed to be as a hot research area due to their ranges in various fields of applications. Clustering is the more efficient techniques considered to resolve the problems of energy consumption in WSNs. The clustering has assured its effectiveness, specifically with high dynamic networks at which all nodes are represented in the clustering and all the data value can be collecting by sensor nodes with a cluster head (CH). The energy consumption as well as network traffic are balancing, the (CH) must be exchanged between all sensor nodes and the clustering size could be closely determined at various portions of the network. The key point at issue in WSN is to improve the energy-efficient algorithms with Grid based clustering for extending the lifetime of the networks, reducing cost, in addition to increasing the reliability of the network. The sensor nodes prepared with a power sources at limited period, so that the Grid Based Clustering algorithm (GBC) which efficiently employing sensor nodes energy have the ability to preserve an extended network lifetime. Sensor node Performances of the network mainly depend on the routing protocols, which are application based, four routing protocols LEACH, OLSR, AODV, and DSR in clustering WSN are estimated with Qualnet simulator. The results demonstrate that DSR outperforms in term of throughput, and end to end delay which compared to others while OLSR have highest value of half number of alive nodes per round.</span>

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“…Habitually, the network of wireless sensors is usage within situation of "high-end applications" like radioactivity and "nuclear-threat detection systems", ''over-the-horizon'' defense detection for boats, and so on. A small list of using the sensors can be summarized as in the fields of :-"Military applications, Monitoring inimical forces, Monitoring friendly forces and equipment, Military-theater or battlefield surveillance, Targeting, Battle damage assessment, Nuclear, biological, and chemical attack detection, Environmental applications, Microclimates, Forest fire detection, Flood detection, Precision agriculture, Health applications, Remote monitoring of physiological data, Tracking and monitoring doctors and patients inside a hospital, Drug administration, Elderly assistance, Home applications, Home automation, Instrumented environment, Automated meter reading, Commercial applications, Environmental control in industrial and office buildings, Inventory control, Vehicle tracking and detection, Traffic flow surveillance" [10][11][12][13][14][15].…”

Section: The Uses Of Wsnmentioning

confidence: 99%

An active technique for power saving in WSN under additive white gaussian noise channel

Chisab¹

2019

IJECE

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The work with feature of self-power supply by solar cell or by the battery or together at the same time. Therefore, the power dissipation is the big problem in wireless sensor network (WSN) especially when it is works for long time. The efficient method for reducing the power consumption within working is needed. The process of reducing waste power is one of the top priorities of scientists and designers of wireless sensor networks. The aim of this paper is to find the dominant method to reduce the power consumption in the wireless sensor network in order to stay works for long time and maintain the links with other nodes without loss of connection and transfer the information correctly. In this paper, a modified method was invented to minimize power utilization per data bit in a connection. This new method depends on the optimization process for reducing the power consumption as low as possible. All the tests of simulation process were done in additive white Gaussian noise (AWGN) channel. Numerical results demonstrated that the new method reduce the power when different values of noise are present with different types of modulation. Also the distance that the WSN will reach the information to it will be increase with presence of various noise amounts with different types of modulation. As a result, the power was decreased and the signal was reach more distance.

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QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network

Cited by 9 publications

References 12 publications

RTL Implementation of image compression techniques in WSN

RTL Implementation of image compression techniques in WSN

Qualnet performances of grid-based clustering For WSN's routing protocols

An active technique for power saving in WSN under additive white gaussian noise channel

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