Energy Aware Clustering Scheme in Wireless Sensor Network Using Neuro-Fuzzy Approach

Robinson, Y. Harold; Julie, E. Golden; Balaji, S.; Ayyasamy, A.

doi:10.1007/s11277-016-3793-8

Cited by 47 publications

(11 citation statements)

References 21 publications

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“…This denotes a network of energy usage as a continuous parameter. The Best quality node contact is determined by the frequency of the radio signal identified by formula [35]. Trust Factor (R f ) : The Cluster Header (CR) will packets fell consecutive and the number of packets dropped was used to evaluate the True Factor (R f ) E x ( b, y)= h x ( b, y) +C y Where M= number of packets fell consecutive by the CR R f = 100* y log2 (M) Eventually, the effective quality of the link as well as the total energy sets are trained to select powerful nodes for unpredictable cluster heads which are the secure transmission between endpoints.…”

Section: A Neural Network Systemmentioning

confidence: 99%

Software Defined Networking Based Protection against DDOS in IoT

Pajila,

P.,

C.*

et al. 2020

IJITEE

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Safety has become enormously important with the proliferation of internet of Things(IoT) technologies. Most of the IoT devices are linked with the DDoS attack, there are many risk nowadays for IoT because of DDoS attack.The new software-defined everything(SDx) model offers a way to handle IoT devices securely. The proposed S-IOT framework consists of a pool that includes S-IoT controllers, S-IoT switches and IoT devices. A new ENeFS algorithm is proposed to identify and reduce the DDoS attack. The proposed algorithm uses neuro fuzzy instruct rule to identify the DDoS attack and the number of data packets count also considered for the identification. The simulation results shows that the proposed algorithm performs better to improve the reliability of the IoT with different and unsafe gadgets.

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Section: A Neural Network Systemmentioning

confidence: 99%

Software Defined Networking Based Protection against DDOS in IoT

Pajila,

P.,

C.*

et al. 2020

IJITEE

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“…Most papers in the literature concentrate on energy efficient communication protocols at the network level. These protocols take into account communication model, network structure and topology, reliable routing schemes [17][18][19][20][21]. An important issue is also detailed energy consumption analysis at the network node level (neglected in the literature) which is the goal of our studies.…”

Section: Existing Communication Schemesmentioning

confidence: 99%

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Kozłowski

Sosnowski

2019

Wireless Pers Commun

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Energy consumption has become dominant issue for wireless internet of things (IoT) networks with battery-powered nodes. The prevailing mechanism allowing to reduce energy consumption is duty-cycling. In this technique the node sleeps most of the time and wakes up only at selected moments to extend the lifespan of nodes up to 5-10 years. Unfortunately, the scheduled duty-cycling technique is always a trade-off between energy consumption and delay in delivering data to the target node. The delay problem can be alleviated with an additional wake-up radio (WuR) channel. In the paper we present original power consumption models for various duty-cycling schemes. They are the basis for checking whether WuR approach is competitive with scheduled duty-cycling techniques. We determine the maximum energy level that an additional wake-up radio can consume to become a reasonable alternative of widely used duty-cycling techniques for typical IoT networks.

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“…The problem of virtual sensor provisioning appears similar to the sensor allocation problem [ 5 , 23 , 25 , 26 , 27 , 28 , 29 , 30 ], which is traditionally treated in the wireless sensor network context. However, these works consider only homogeneous sensor nodes (with the same physical characteristics such as the number and type of sensors) and a single application running on these nodes (for example, an application that periodically sends its measurements to the sink node).…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

Lemos

Filho

Rabelo

et al. 2018

Sensors

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Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.

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Energy Aware Clustering Scheme in Wireless Sensor Network Using Neuro-Fuzzy Approach

Cited by 47 publications

References 21 publications

Software Defined Networking Based Protection against DDOS in IoT

Software Defined Networking Based Protection against DDOS in IoT

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

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