Optimal Cooperative Offloading Scheme for Energy Efficient Multi-Access Edge Computation

Abstract

The distributed cooperative offloading technique with wireless setting and power transmission provides a possible solution to meet the requirements of next-generation Multi-access Edge Computation (MEC). MEC is a model which avails cloud computing the aptitude to smoothly compute data at the edge of a largely dense network and in nearness to smart communicating devices (SCDs). This paper presents a cooperative offloading technique based on the Lagrangian Suboptimal Convergent Computation Offloading Algorithm (LSCCOA) for multi-access MEC in a distributed Internet of Things (IoT) network. A computational competition of the SCDs for limited resources which tends to obstructs smooth task offloading for MEC in an IoT high demand network is considered. The proposed suboptimal computational algorithm is implemented to perform task offloading which is optimized at the cloud edge server without relocating it to the centralized network. These resulted in a minimized weighted sum of transmit power consumption and outputs as a mixed-integer optimization problem. Also, the derived fast-convergent suboptimal algorithm is implemented to resolve the non-deterministic polynomial-time (NP)-hard problem. In conclusion, simulation results are performed to prove that the proposed algorithm substantially outperforms recent techniques with regards to energy efficiency, energy consumption reduction, throughput, and transmission delay performance.

Resolving Multitask Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game

“…There are many offloading methods for edge computing scenarios. Among them, more efforts have been dedicated to utilizing edge collaboration [5], [6], artificial intelligence [9]- [11] and additional assistance (phones, drones, etc.) [12]- [14].…”

“…For example, a collaborative task offloading mechanism (CTOM) for mobile cloudlet networks was designed in [5] to achieve more efficient and low-cost load balancing. Anajemba et al [6] introduced a distributed multi-access MEC collaborative offloading technology based on the sub-optimal Lagrangian method. Whereas, these edge collaborations consider situations with sufficient network status information and cannot handle the changing network environment well.…”

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

“…However, for an anomaly network-based IDS (A-NIDS), the authors in [17,18] proposed a primal dependable hybrid approach that incorporates the Adaboost meta-algorithm and artificial bee colony (ABC). This is intended to achieve optimal detection rate (DR) at a minimized false positive rate (FPR) [19]. In the study by [20], the ABC algorithm is implemented for selection of features, while the Adaboost meta-algorithm is used for feature classification and evaluation.…”

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

“…R ECENT advancements in mobile technology and high data-rate wireless networks have surged the innovation of new smart devices, including smartphones, fitness bands, connected cars, and smart-watches. These devices are also collectively known as mobile devices [1], smart mobile devices [2] or Internet of Things (IoT) devices [3]; however we refer them as smart devices (SDs). SDs can collect data from the surroundings and take collective decisions by sharing data with other devices.…”