Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing

Abstract

The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-ofdimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices.

Study and Analysis of Multiconnectivity for Ultrareliable and Low‐Latency Features in Networks and V2X Communications

“…For the general case and to improve the efficiency of searching for an extremum, it is proposed to use an integer binary search algorithm [19].…”

“…The proposed algorithm in Figure 8 consists of cyclically dividing the entire set of possible values of k and discarding the half in which there cannot be an extremum until the search range is narrowed down to a single value at the next 7 Wireless Communications and Mobile Computing iteration. The idea of the method is similar to the dichotomy method [19], with the difference that the search for a solution is performed on the set of integer values of the argument.…”

“…However, URLLC implementation requires a delay to be decreased from 10 ms to 1 ms. This is achieved by the technical features involved in building a network at the level of access [12][13][14][15], in particular by changing the format of transmitted frames or by changing the structure of the network at other levels [3] primarily because of the "approaching" point of service provided by the user (cloud and edge organization) [16][17][18][19][20].…”

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

“…ey also used the powerful computing of the MEC platform to solve the optimization of video application migration performance [22] and resource constraints of industrial IoT equipment [23] and other issues.…”

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

“…For detection of COVID-19 disease, a deep learning approach is adopted with an augmented approach [18] and it achieved 100% accuracy. In industrial mobile edge computing [19], the deep ensemble learning approach is used for resource allocation and data security. e importance of a smart healthcare recommendation system is increasing day by day for better and timely prediction.…”