DQN-based mobile edge computing for smart Internet of vehicle

Abstract

In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user’s quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes.

Multiple Dimensional Encoding/Modulation Shift-and-Addition Design for Distributed Systems

“…The addition and subtraction operation [22] of finite fields are the XOR operation in polynomial calculation. The rule for adding and subtracting is to XOR coefficients of the same order in two polynomials, and there is no difference between the two operations, such as ðz 2 + zÞ + ðz + 1Þ = z 2 + 1, ðz 2 + zÞ − ðz + 1Þ = z 2 + 1.…”

“…Multidimensional encoding/modulation promises high data rate [18,19], which has been used as promising technique in communication [20] and distributed systems [22,23]. As a result, to further reduce the overhead, based on the idea of multidimensional encoding/modulation, we propose the idea of one element taking part in the encoding process multiple times when constructing one encoded packet.…”

Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks

“…Especially, outage opportunity was actually really analyzed in [5,6]. SER of TWRN in Rayleigh fading systems was actually really acquired in [7,8] was actually really made use of. To improve the body system effectiveness, the procedure of interact option was actually really advised on TWRN [?…”

An Optimized Approach for Industrial IoT Based on Edge Computing

“…Relying on hundreds of millions of seamlessly deployed sensors, collectors, and controllers, the Industrial Internet of Things can simulate, predict, and control the full cycle of the manufacturing process [1]. As the "brain" of the IIoT, the edge computing network provides more sufficient computing processing capabilities for wireless acquisition devices, effectively reducing processing and transmission delays, and is useful for digital twin (DT) [2], virtual reality (VR), etc. Enterprise high-level applications have laid a solid foundation [3].…”