In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech recognition.With the increasing demand for computing resources,these...In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech recognition.With the increasing demand for computing resources,these applications become more complicated.Cloud Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of use.Mobile Cloud Computing(MCC)aimed at overcoming drawbacks of smart phone devices.The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance.For remote execution,recent studies suggested downloading all or part of mobile application from mobile device.On the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed.To address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during offloading.First,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’spectrum.Next,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary user.Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in MCC.展开更多
文摘In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech recognition.With the increasing demand for computing resources,these applications become more complicated.Cloud Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of use.Mobile Cloud Computing(MCC)aimed at overcoming drawbacks of smart phone devices.The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance.For remote execution,recent studies suggested downloading all or part of mobile application from mobile device.On the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed.To address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during offloading.First,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’spectrum.Next,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary user.Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in MCC.
