Auction-Based Data Gathering Scheme for Wireless Sensor Networks

Althunibat, Saud; Abu-Al-Aish, Amir; Shehab, Wael Abu; Al-Sawalmeh, Wael

doi:10.1109/lcomm.2016.2557793

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Al [10] projected a unique information gathering scheme. It simulates a public sale, wherever nodes broadcast their neighbour information consecutively.…”

Section: Related Workmentioning

confidence: 99%

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Investigation on Energy Based Data Gathering Approach for WSN

Bharathidasan¹,

Ramamoorthy²

2017

JAC

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“…Al [10] projected a unique information gathering scheme. It simulates a public sale, wherever nodes broadcast their neighbour information consecutively.…”

Section: Related Workmentioning

confidence: 99%

“…We compare our proposed approach REEDGA with NSTCDG [7], ADGS [10] and MDF [12] in presence of data gathering environment. Figure. 3 shows the results of packet delivery ratio while mobility.…”

Section: Control Overheadmentioning

confidence: 99%

Investigation on Energy Based Data Gathering Approach for WSN

Bharathidasan¹,

Ramamoorthy²

2017

JAC

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“…Switching the transmission mode of WSN from radio frequency (RF) to free space optical (FSO) has overcome many drawbacks. Although RF‐based WSN can cover a large area, it suffers mainly from the induced interference, attack vulnerability, limited bandwidth, and the relatively high energy consumption . As such, FSO‐based WSNs with its many appealing advantages has been proposed as viable solution that can circumvent all the aforementioned drawbacks of RF‐based WSNs …”

Section: Introductionmentioning

confidence: 99%

Performance analysis of free space optical–based wireless sensor networks using corner cube retroreflectors

Althunibat

Altarawneh

Mesleh

2019

Trans Emerging Tel Tech

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Asymmetric free space optical wireless sensor networks (FSO-WSNs) have gained momentous attention lately due to the promise of minimal energy consumption at the distributed sensors along with eminent performance. Asymmetric FSO is accomplished using corner cube retroreflector (CCR) that is mounted on each sensor node. The CCR either reflects or absorbs the received laser beam from the fusion center to indicate its local decision regarding the monitored target. In this study, a bistatic channel is assumed where the transmitter and the receiver are located at different locations such that the double pass channel is modeled as a product of two independent turbulence channels. Novel performance analysis of the CCR-based FSO-WSN system in terms of the average bit error rate (BER) and the average decision error rate (DER) is presented. In particular, closed-form expressions for the BER and the DER are derived using the Fox H-function under different bistatic turbulent channel models including negative exponential, K-turbulence, Gamma-Gamma, and Malaga channel models.Monte Carlo simulation results are obtained to corroborate the accuracy of the derived analytical formulas where an exact match is reported over wide range of system and channel parameters. In addition, the influence of transmit power, distance, detection threshold, and turbulence parameters on the performance of the system is investigated and thoroughly discoursed.Trans Emerging Tel Tech. 2019;30:e3707.wileyonlinelibrary.com/journal/ett considered as the most energy efficient since CCRs are very compact and operate at extremely low power consumption in the order of 0.1 nJ/bit. 11 The basic principle of CCR-based FSO-WSN implies that the fusion center (FC) emits a continuous laser beam toward each node in the WSNs to collect its binary decision. If the decision is 1, the CCR reflects the laser beam back to the FC. Alternatively, if the decision is 0, the CCR will not be activated and no signal will be reflected to the FC. Thus, the decision of the corresponding node is detected based on the intensity of the reflected beam. It is discernible that the energy consumption at the distributed nodes is at its minimum value. In literature, extensive research works have focused on fabricating the CCR and other modulating retroreflector (MRR) devices. 12,13 The establishment of the experimental CCR FSO systems is performed in other works, 14-17 which adequately demonstrates the feasibility of the CCR FSO technology. The signal to-noise ratio (SNR) of a transceiver and a single CCR in a WSN based on a passive MRR is analyzed in the work of Hsu et al. 11 Noonpakdee et al 14 propose an optical wireless identification scheme employing a thin film CCR (TCCR). The system was found to have potential advantages over hybrid RFID systems. In the work of Noonpakdee, 15 an indoor optical wireless communication system using CCR was proposed for health monitoring applications. Noonpakdee 16 proposed a passive-active optical wireless transmission for personal health monitoring sys...

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“…An auction-based scheme was introduced in [7] for improving data aggregation with minimum energy utilization and hence enhanced the NL of the SNs. Though the method minimizes data loss, the data classification was not performed.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

J.Srimathi¹,

Mayil²

2019

IJCNC

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The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL, data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP. The results observed that FGEORBC technique improves the data gathering and NL with minimum time as well as EC than the state-of-the-art methods.

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Auction-Based Data Gathering Scheme for Wireless Sensor Networks

Cited by 16 publications

References 8 publications

Investigation on Energy Based Data Gathering Approach for WSN

Investigation on Energy Based Data Gathering Approach for WSN

Performance analysis of free space optical–based wireless sensor networks using corner cube retroreflectors

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

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