This paper introduces an innovative multi-queue fog architecture coupled with an intelligent controller, aimed at enhancing the efficiency and adaptability of fog computing environments. Unlike conventional single-queue fog architectures that typically rely on basic first-in-first-out (FIFO) task execution models in fog servers, our approach offers heightened granularity and flexibility in task scheduling. This feature enables effective task management, catering specifically to Internet of Things (IoT) applications characterized by varying degrees of time-sensitivity and resource requirements. Our proposed deep weighted-fair fog servers (DeepWFFS) scheme comprises two key elements: the weighted-fair fog server (WFFS) framework and an intelligent deep controller (DC) leveraging deep reinforcement learning (DRL) for task prioritization. The WFFS framework adopts multiple queues within fog servers, each assigned a predefined weight representing task priority. This prevents task starvation and promotes equitable task execution. Meanwhile, the DC continuously monitors task workloads and priorities, ensuring optimal task allocation to the most suitable queues within fog and cloud servers. Through simulation, our results exhibit the superior performance of DeepWFFS compared to benchmark schemes. This advancement showcases the potential of our architecture to efficiently manage diverse tasks in fog computing environments.

This article studies the network lifetime optimization problem in a multiuser mobile-edge computing (MEC)-enabled Internet of Things (IoT) system comprising an access point (AP), a MEC server, and a set of$K$mobile devices (MDs) with limited battery capacity. Considering the residual battery energy at the MDs, stochastic task arrivals, and time-varying wireless fading channels, a soft actor–critic (SAC)-based deep reinforcement learning (DRL) lifetime maximization, called DeepLM, is proposed to jointly optimize the task splitting ratio, the local CPU-cycle frequencies at the MDs, the bandwidth allocation, and the CPU-cycle frequency allocation at the MEC server subject to the task queuing backlogs constraint, the bandwidth constraint, and maximum CPU-cycle frequency constraints at the MDs and the MEC server. Our results reveal that DeepLM enjoys a fast convergence rate and a small oscillation amplitude. We also compare the performance of DeepLM with three benchmark offloading schemes, namely, fully edge computing (FEC), fully local computing (FLC), and random computation offloading (RCO). DeepLM increases the network lifetime by 496% and 229% compared to the FLC and RCO schemes. Interestingly, it achieves such a colossal lifetime improvement when its nonbacklog probability is 0.99, while that of FEC, FLC, and RCO is 0.69, 0.53, and 0.25, respectively, showing a significant performance gain of 30%, 46%, and 74%.

The deployment of fog computing resources in industrial internet of things (IIoT) is essential to support time-sensitive applications. To utilize resources efficiently, a brand-new request dispatcher is required to sit between the IIoT devices and the pool of fog resources. The need for such a dispatcher stems from the challenges specific to these systems. Firstly, fog-enabled IIoT systems are highly dynamic and distributed. Second, fog nodes are typically power and resource limited. Finally, many IIoT applications feature critical time-sensitivity, referred to as timeliness, and cannot tolerate response delay beyond a specific threshold. This paper proposes an efficient dispatching algorithm to minimize energy consumption and deadline misses while keeping the system stability at a satisfactory level. We leverage Lyapunov Optimization technique to tackle the problem and handle the system dynamics. We perform extensive simulations to verify the effectiveness of the proposed method and provide sensitivity, scalability and model parameter analysis. The simulation results prove the superiority of the proposed method over the state-of-the-art method up to 22% and 10% in terms of average deadline misses and energy consumption, respectively. Further, we perform practical experiments to prove the validity of the proposed method in a real testbed.

Fog computing introduces a distributed processing capability close to end users. The proximity of computing to end users leads to lower service time and bandwidth requirements. Energy consumption is a matter of concern in such a system with a large number of computing nodes. Renewable energy sources can be utilized to lessen the burden on the main power grid and reduce the carbon footprint, but due to fluctuations, the effective utilization of renewable energy sources needs proper resource management. In this article, we deal with properly managing the resources in a fog environment where the fog nodes are equipped with onsite renewable energy. This article aims to design an efficient mechanism to dynamically dispatch requests among computing nodes and scale frequency and modulation level, based on the current workload and the availability of renewable energy sources, to minimize the service time while keeping the renewable energy utilization and stability at a satisfactory level. We state the problem as the design of a controller for a system with time-varying nonlinear state equations. Accordingly, we borrow the Lyapunov optimization technique from the control theory to design the request dispatching mechanism and prove its asymptotic optimality. We perform extensive simulations to evaluate the effectiveness of the proposed method. The simulation results demonstrate that our proposed method outperforms the naive time-aware baseline scheme up to 26% and 39%, respectively, in terms of service time and renewable energy utilization.

In this paper, we revisit the concept of fractionally spaced equalization (FSE) for broadband single-carrier amplify-and-forward (AaF) cooperative systems. Particularly, we investigate fractionally spaced frequency domain equalization (FS-FDE) for cooperative multi-relay systems. Our motivation stems from the elegant properties reported for the FSE in the point-to-point communication systems (i.e., its robustness to sampling phases and potential in achieving the optimum performance) and the scalability of the FDEs. In particular, we propose a TS/2-spaced equalizer that transforms the temporal sample sequence of the received signal to the frequency domain, applies linear/decision-feedback equalization, and returns the resulting signal back to the time domain for detection. The vital importance of using FS-FDE method in cooperative systems is disclosed in practical scenarios where the transmitted signals have nonzero roll-off components and sampling phase error may occur in relay(s) and destination terminals. Our results demonstrate that, under specific channel realizations and sampling errors, the cooperative systems with symbol spaced FDE (SS-FDE) fail to harvest the available cooperative diversity and the performance approaches to that of no relay scenario. On the other hand, the performance of cooperative system with FS-FDE method becomes independent of sampling phase errors and full benefit of cooperation is retained.

High-frequency (HF) radio communication has been recognized as the primary means for long-range wireless communications since the advent of radio. With its unique features, HF communication continues to be used for a wide range of civilian, government and military applications as a powerful alternative to a myriad of more sophisticated communication systems. However demanding requirements of high-speed data communications impose new requirements on the HF system design and innovative approaches are required. In this paper, building upon the promising concept of cooperative transmission, we investigate the performance of a multicarrier coded HF system with amplify-and-forward relaying. Specifically, we consider orthogonal frequency division multiplexing (OFDM) with bit-interleaved coded modulation (BICM) and demonstrate the achievable diversity through the derivation of pairwise error probability. We also conduct Monte Carlo simulations to confirm the analytical derivations and present performance comparisons.