Optimal probabilistic encryption for distributed detection in wireless sensor networks based on immune differential evolution algorithm

Chen, Wen; Zhao, Hui; Li, Tao; Liu, Yuling

doi:10.1007/s11276-017-1484-3

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…DE is applied as clustering algorithm in order to ensure minimal energy consumption and network resilience and sustainability [37]. Other applications can be seen in [38][39][40].…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

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“…DE is applied as clustering algorithm in order to ensure minimal energy consumption and network resilience and sustainability [37]. Other applications can be seen in [38][39][40].…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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“…The motivation behind using the DE is taken from the work of Chen et al 43 We have used DE to tune SVM parameters. The adjusted parameters are penalty ratio (C), acceptable error, and the deviation of the Gaussian kernel function.…”

Section: ) Differential Evolution Algorithmmentioning

confidence: 99%

Congestion avoidance and fault detection in WSNs using data science techniques

Kazmi

Javaid

Awais

et al. 2019

Trans Emerging Tel Tech

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Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE-SVM and GWO-SVM are more proficient than other classification techniques. Moreover, DE-SVM and GWO-SVM have outperformed the benchmark technique genetic algorithm-SVM by producing 3% and 1% less classification errors, respectively.For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

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“…Optimum flipping can be obtained using DE to minimize the fusion error and ensuring secure data transmission [184]. DE was applied to obtain optimal power schedule in wireless networks thereby, minimizing the occurrence of the denial of service (DoS) attacks [185].…”

Section: Securitymentioning

confidence: 99%

Differential Evolution in Wireless Communications: A Review

Okagbue¹,

Adamu

Anake

2019

Int. J. Onl. Eng.

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<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p>

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Optimal probabilistic encryption for distributed detection in wireless sensor networks based on immune differential evolution algorithm

Cited by 9 publications

References 18 publications

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Congestion avoidance and fault detection in WSNs using data science techniques

Differential Evolution in Wireless Communications: A Review

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