Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a c...Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.展开更多
Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vita...Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vitalfor cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encryptedtext.Therefore,the S-box design is essential for the robustness of cryptographic systems,especially for the dataencryption standard(DES)and advanced encryption standard(AES).This study focuses on the application of theffreffy algorithm(FA)and metaheuristic lion optimization algorithm(LOA),thereby proposing a hybrid approachcalled the metaheuristic lion ffreffy(ML-F)algorithm.FA,inspired by the blinking behavior of ffreffies,is a relativelynew calculation technique that is effective for various optimization problems.However,FA offen experiences earlyconvergence,limiting the ability to determine the global optimal solution in complex search areas.To address thisproblem,the ML-F algorithm was developed by combining the strengths of FA and LOA.This study identiffesa research gap in enhancing S-box nonlinearity and resistance to differential attacks,which the proposed ML-Faims to address.The main contributions of this paper are the enhanced cryptographic robustness of the S-boxesdeveloped with ML-F,consistently outperforming those generated by FA and other methodsregarding nonlinearityand overall cryptographic properties.The LOA,inspired by the social hunting behavior of lions,uses the collectiveintelligence of a pride of lions to explore and exploit the search space more effectively.The experimental analysis ofthisstudy focused on the main encryption criteria,namely,nonlinearity,the bit independence criterion(BIC),strictavalanche criterion(SAC),differential probability(DP),and maximum expected linear probability(MELP).Thesecriteria ensure that the S-boxes provide robust security against various cryptanalytic attacks.The ML-F algorithmconsistently surpassed the FA and other optimization algorithms in generating S-boxes with higher nonlinearityand better overall cryptographic properties.In case of ML-F-based S-boxes,the results indicated a better averagenonlinear score and more resistance against several cryptographic attacks for quite a number of criteria.Therefore,they were considered more reliable while dealing with secured encryption.The values generated by the ML-FS-boxes are near ideal in both SAC and BIC,indicating better diffusion properties and consequently,enhancedsecurity.The DP analysisfurthershowed that the ML-F-generated S-boxes are highly resistant to differential attacks,which is a crucial requirement for secure encryption systems.展开更多
As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optim...As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.展开更多
The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the v...The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.展开更多
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)the Soonchunhyang University Research Fund.
文摘Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.
文摘Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vitalfor cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encryptedtext.Therefore,the S-box design is essential for the robustness of cryptographic systems,especially for the dataencryption standard(DES)and advanced encryption standard(AES).This study focuses on the application of theffreffy algorithm(FA)and metaheuristic lion optimization algorithm(LOA),thereby proposing a hybrid approachcalled the metaheuristic lion ffreffy(ML-F)algorithm.FA,inspired by the blinking behavior of ffreffies,is a relativelynew calculation technique that is effective for various optimization problems.However,FA offen experiences earlyconvergence,limiting the ability to determine the global optimal solution in complex search areas.To address thisproblem,the ML-F algorithm was developed by combining the strengths of FA and LOA.This study identiffesa research gap in enhancing S-box nonlinearity and resistance to differential attacks,which the proposed ML-Faims to address.The main contributions of this paper are the enhanced cryptographic robustness of the S-boxesdeveloped with ML-F,consistently outperforming those generated by FA and other methodsregarding nonlinearityand overall cryptographic properties.The LOA,inspired by the social hunting behavior of lions,uses the collectiveintelligence of a pride of lions to explore and exploit the search space more effectively.The experimental analysis ofthisstudy focused on the main encryption criteria,namely,nonlinearity,the bit independence criterion(BIC),strictavalanche criterion(SAC),differential probability(DP),and maximum expected linear probability(MELP).Thesecriteria ensure that the S-boxes provide robust security against various cryptanalytic attacks.The ML-F algorithmconsistently surpassed the FA and other optimization algorithms in generating S-boxes with higher nonlinearityand better overall cryptographic properties.In case of ML-F-based S-boxes,the results indicated a better averagenonlinear score and more resistance against several cryptographic attacks for quite a number of criteria.Therefore,they were considered more reliable while dealing with secured encryption.The values generated by the ML-FS-boxes are near ideal in both SAC and BIC,indicating better diffusion properties and consequently,enhancedsecurity.The DP analysisfurthershowed that the ML-F-generated S-boxes are highly resistant to differential attacks,which is a crucial requirement for secure encryption systems.
基金supported by the National Natural Science Foundation of China(61771293)the Key Project of Shangdong Province(2019JZZY010111)。
文摘As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.
文摘The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.
