False Data Filtering in Wireless Sensor Networks

Luo, Ze; Zhu, Lingzhi; Chang, Yunjie; Luo, Qinghua; Gui-xiang, LI; Wei-Sheng, Liao

doi:10.21307/ijssis-2017-940

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In this work, we used a first order radio model for energy consumption and comparison in the WSNs. Research works [14][15][16][17] present various attack types and their counter measures in en-route filtering schemes. Some interesting works exist for improving network lifetime [18][19][20][21][22][23][24] and energy efficiency [25][26][27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

et al. 2020

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In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

et al. 2020

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“…In References [10,20,26,27,28,29,30,31], the authors proposed their scheme to enhance network lifetime in WSNs. Reference [24] presents a countermeasure for false data filtering in WSNs. A key-indexed-based routing method [25] for filtering false event reports in WSNs is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Sensor clocks are synchronised using the energy-efficient time synchronization protocol (ETSP) [23]. Recent work [20,24,25,26,27] also highlighted countermeasures against attacks in en-route filtering with an emphasis on network lifetime. Several work focused on network lifetime extension [10,28,29,30,31], energy efficiency and network lifetime [32,33,34], and energy efficiency [35,36,37,38,39] and privacy [40,41,42] using various techniques.…”

Section: Introductionmentioning

confidence: 99%

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Shahzad

Islam

Kwak

et al. 2019

Sensors

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Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

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Gafom: Genetic Algorithm Based Fuzzy Optimized Mobility in Sensor Networks

Shahzad,

Hussain,

Zeeshan

et al. 2024

Preprint

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False Data Filtering in Wireless Sensor Networks

Abstract: Abstract-Wireless Sensor Networks (WSN) is often deployed in hostile environments

Cited by 4 publications

References 18 publications

GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Gafom: Genetic Algorithm Based Fuzzy Optimized Mobility in Sensor Networks

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