Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Energy harvesting(EH)technology in wireless communication is a promising approach to extend the lifetime of future wireless networks.A cross-layer optimal adaptation policy for a point-to-point energy harvesting(EH)wi...Energy harvesting(EH)technology in wireless communication is a promising approach to extend the lifetime of future wireless networks.A cross-layer optimal adaptation policy for a point-to-point energy harvesting(EH)wireless communication system with finite buffer constraints over a Rayleigh fading channel based on a Semi-Markov Decision Process(SMDP)is investigated.Most adaptation strategies in the literature are based on channeldependent adaptation.However,besides considering the channel,the state of the energy capacitor and the data buffer are also involved when proposing a dynamic modulation policy for EH wireless networks.Unlike the channeldependent policy,which is a physical layer-based optimization,the proposed cross-layer dynamic modulation policy is a guarantee to meet the overflow requirements of the upper layer by maximizing the throughput while optimizing the transmission power and minimizing the dropping packets.Based on the states of the channel conditions,data buffer,and energy capacitor,the scheduler selects a particular action corresponding to the selected modulation constellation.Moreover,the packets are modulated into symbols according to the selected modulation type to be ready for transmission over the Rayleigh fading channel.Simulations are used to test the performance of the proposed cross-layer policy scheme,which shows that it significantly outperforms the physical layer channel-dependent policy scheme in terms of throughput only.展开更多
In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to...In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety.The exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and security.At the time of the attack,it appears the women were immobilized and needed immediate support.Only self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or button.The proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud Technology.It is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud database.WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location settings.WSG has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 s.The average activation time is 2.079 s.展开更多
Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and ro...Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.展开更多
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
基金The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No-R-2021-60.
文摘Energy harvesting(EH)technology in wireless communication is a promising approach to extend the lifetime of future wireless networks.A cross-layer optimal adaptation policy for a point-to-point energy harvesting(EH)wireless communication system with finite buffer constraints over a Rayleigh fading channel based on a Semi-Markov Decision Process(SMDP)is investigated.Most adaptation strategies in the literature are based on channeldependent adaptation.However,besides considering the channel,the state of the energy capacitor and the data buffer are also involved when proposing a dynamic modulation policy for EH wireless networks.Unlike the channeldependent policy,which is a physical layer-based optimization,the proposed cross-layer dynamic modulation policy is a guarantee to meet the overflow requirements of the upper layer by maximizing the throughput while optimizing the transmission power and minimizing the dropping packets.Based on the states of the channel conditions,data buffer,and energy capacitor,the scheduler selects a particular action corresponding to the selected modulation constellation.Moreover,the packets are modulated into symbols according to the selected modulation type to be ready for transmission over the Rayleigh fading channel.Simulations are used to test the performance of the proposed cross-layer policy scheme,which shows that it significantly outperforms the physical layer channel-dependent policy scheme in terms of throughput only.
基金The authors extend their appreciation to the deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number(IFP-2020-19).
文摘In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety.The exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and security.At the time of the attack,it appears the women were immobilized and needed immediate support.Only self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or button.The proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud Technology.It is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud database.WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location settings.WSG has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 s.The average activation time is 2.079 s.
基金The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No-R-2021-154.
文摘Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.
