Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications. Группа авторов

Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications - Группа авторов


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edge to overcome this challenge by taking user and base station as requirements. The DRL helps reduce power and bandwidth from the base station to the user, thus making the system energy efficient [33].

S. no. Existing methods Inference
1. Joint task allocation and Resource allocation with multi-user Wi-Fi. To minimize the energy consumption at the mobile terminal, a Q-learning algorithm is proposed. In this method, energy efficiency is not considered, which leads to additional costs for the system.
2. Joint task allocation-Decoupling bandwidth configuration and content source selection. An algorithm was proposed for avoiding frequent information exchange, which was proven to be less versatile and hence cannot be used in large applications.
3. Fog computing method for mobile traffic growth and better user experience. As users are located in different geographical places, implementing fog becomes challenging and requires high maintenance and increased costs.
4. Deterministic mission arrival scenario After successfully completing the present mission, each mission is completed, which cannot work as the data source generates tasks continuously, which cannot be handled by the deterministic method.
5. Random task arrival model This method works on task arrived and not on the queue tasks, which fails the system to work efficiently.

      Computation offloading is a great mechanism to offload extensive tasks at the nearby server and communicate cloud with important/filtered data. With edge, computation offloading has excellent applications for mobile devices by enhancing efficiency.

      In a study, a dynamic computing offloading mechanism is performed. The objective of the study was to reduce the cost of computational resources. Mobile edge computing is considered (MEC). A Deep Learning method, i.e., Deep Supervised Learning (DSL) is considered. A network of a mobile-based computer system is considered. A pre-calculated offloading solution is proposed. A continuous offloading decision problem is formulated as a multi-label classification problem. After experimental analysis, it is inferred that as the exhaustive strategy suffers exponentially with the increase in the “n” fine-grained components.

      Usually, an evolutionary algorithm is used to solve the NP-hard problem, where solving the problem by a traditional optimization mechanism is impossible. This evolutionary algorithm takes some ransom solution vector from the solution space and tries to get optimal solution by the n number iteration by slowly evolving towards an optional one in each iteration through some cost or reward function. There are many evolutionary algorithms like 1) particle swarm optimization, 2) Genetic algorithm, 3) colony optimization algorithm.

      In edge computing, many NP-hard optimization problems could be solved using those evolutionary algorithms.

      In Mobile edge computing (MEC), offloading makes low latency and energy-efficient. Security critical tasks involve more computation and take more time. If we offload them, we can achieve a good performance. To minimize task completion time and energy consumption, particle swarm optimization algorithms are proposed [35]. Position-based mapping is carried out to map the particle solution of scheduling. a new slow down particle movement process mechanism is reported in the update particles step of the algorithm. Tthey have proved that the new update mechanism with slow down process achieved better performance compared to that of conventional particle swarm optimization algorithm.

Schematic illustration of offloading in vehicular node.

      The chapter has given a basic introduction to edge computing with its associated research challenges. The various mathematical models for solving the edge computing problem are also explored. An insight on computational offloading and multiple approaches for computational offloading are discussed. The Markov chain-based decision-making approach is an efficient mathematical approach. The applicability of it for the computational offloading problem in edge computing is also explored. A game theory-based dynamic approach for offload decision making is provided with available solutions. Achieving target


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