We introduce a novel strategy of designing a chaotic coverage path planner for the mobile robot based on the Che- byshev map for achieving special missions. The designed chaotic path planner consists of a two-dimensio...We introduce a novel strategy of designing a chaotic coverage path planner for the mobile robot based on the Che- byshev map for achieving special missions. The designed chaotic path planner consists of a two-dimensional Chebyshev map which is constructed by two one-dimensional Chebyshev maps. The performance of the time sequences which are generated by the planner is improved by arcsine transformation to enhance the chaotic characteristics and uniform distribution. Then the coverage rate and randomness for achieving the special missions of the robot are enhanced. The chaotic Chebyshev system is mapped into the feasible region of the robot workplace by affine transformation. Then a universal algorithm of coverage path planning is designed for environments with obstacles. Simulation results show that the constructed chaotic path planner can avoid detection of the obstacles and the workplace boundaries, and runs safely in the feasible areas. The designed strategy is able to satisfy the requirements of randomness, coverage, and high efficiency for special missions.展开更多
Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological developments.As a result,tens of billions or possibly...Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological developments.As a result,tens of billions or possibly hundreds of billions of devices will be linked together.These smart devices will be able to gather data,process it,and even come to decisions on their own.Security is the most essential thing in these situations.In IoT infrastructure,authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit(e.g.,via client identification and provision of secure communication).It is still challenging to create secure,authenticated key exchange techniques.The majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing,hashing,or modular exponentiation processes.The focus of this paper is to propose an efficient three-party authenticated key exchange procedure(AKEP)using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned above.The proposed three-party AKEP is protected from several attacks.The proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications,according to statistical experiments and low processing costs.To protect client identification when transferring data over an insecure public network,our three-party AKEP may also offer client anonymity.Finally,the presented procedure offers better security features than the procedures currently available in the literature.展开更多
Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resou...Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.展开更多
基金Project supported by thc National Natural Science Foundation of China （Nos. 61473179, 61573213, and 61233014）, the Natural Sci- ence Foundation of Shandong Province, China （Nos. ZR2014FM007 and ZR2015CM016）, and the Key Research and Development Project of Shandong Province, China （No. 2016GGX101027）
文摘We introduce a novel strategy of designing a chaotic coverage path planner for the mobile robot based on the Che- byshev map for achieving special missions. The designed chaotic path planner consists of a two-dimensional Chebyshev map which is constructed by two one-dimensional Chebyshev maps. The performance of the time sequences which are generated by the planner is improved by arcsine transformation to enhance the chaotic characteristics and uniform distribution. Then the coverage rate and randomness for achieving the special missions of the robot are enhanced. The chaotic Chebyshev system is mapped into the feasible region of the robot workplace by affine transformation. Then a universal algorithm of coverage path planning is designed for environments with obstacles. Simulation results show that the constructed chaotic path planner can avoid detection of the obstacles and the workplace boundaries, and runs safely in the feasible areas. The designed strategy is able to satisfy the requirements of randomness, coverage, and high efficiency for special missions.
文摘Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological developments.As a result,tens of billions or possibly hundreds of billions of devices will be linked together.These smart devices will be able to gather data,process it,and even come to decisions on their own.Security is the most essential thing in these situations.In IoT infrastructure,authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit(e.g.,via client identification and provision of secure communication).It is still challenging to create secure,authenticated key exchange techniques.The majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing,hashing,or modular exponentiation processes.The focus of this paper is to propose an efficient three-party authenticated key exchange procedure(AKEP)using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned above.The proposed three-party AKEP is protected from several attacks.The proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications,according to statistical experiments and low processing costs.To protect client identification when transferring data over an insecure public network,our three-party AKEP may also offer client anonymity.Finally,the presented procedure offers better security features than the procedures currently available in the literature.
文摘Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.
