Energy-efficient Offloading for Mission-critical IoT Services Using EVT-embedded Intelligent Learning

Abstract : Mobile edge computing (MEC) is a promising technique to alleviate the energy limitation of Internet of things (IoT) devices, as it can offload local computing tasks to the edge server through a cellular network. By leveraging extreme value theory (EVT), this work proposes a priority-differentiated offloading strategy that takes into account the stringent quality of service (QoS) requirements of mission-critical services and green resource allocation. Particularly, Lyapunov optimization is first introduced to derive an upper-bound queue minimization problem with the consideration of energy consumption and task priority. The peaks-over-thresholds (POT) model is then applied to evaluate the stationery status and cooperate with Wolf-PHC learning to optimize resource allocation. Finally, simulation results verify that the proposed offloading policy performs well in terms of its energy-saving capability while satisfying different demands of mission-critical IoT services.

 EXISTING SYSTEM :

 ? Increasing interconnections and usage of power networks makes the system to work at almost full loading capacities. Any type of disturbance in such scenario results in congestion of the existing network. ? In this paper we define Educational Data Learning Analytics and discussed about existing tools, how the stack holders can make use of it for student success. ? The existing systems apply cryptographic methods at a fine-grained access control and data sharing levels for the services of dynamic user groups in cloud, but it poses to be a challenging issue. ? The world of automation has already being into existence for prolong period which results in various researches and industries to develop commercial products.

 DISADVANTAGE :

 ? It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. ? The formulated multiple timescales problem has been solved by a sample average approximation (SAA) oriented technique. ? We formulated the energy cost minimization problem for the short packets transmission for mission-critical IoT in MEC system, and revealed the effect of short packet transmission on radio resource management. ? The minimization problem is a mixed integer non-linear programming (MINLP) problem, and it is challenging to solve this problem in the best way.

 PROPOSED SYSTEM :

 • The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. • Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. • The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. • The trained DNN is tested on unseen data to calculate the accuracy of the proposed technique.

 ADVANTAGE :

 ? On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. ? In addition, to provide a comprehensive evaluation, a pair of system performance metrics are considered the convergence performance and the total energy consumption, respectively. ? Therein, several sub-optimal solutions that achieve different leverages between the system performance and the time complexity are explored. ? Thereof, the optimal joint optimization solution based on branch and bound method and two time-efficient suboptimal solutions are designed, respectively.