The energy-efficient taskoffloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computationaltasks to be solved via a mass...
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The energy-efficient taskoffloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computationaltasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint taskoffloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computationaltask allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computationaltasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.
Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited co...
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Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloadingtasks. In this work, we develop an optimization framework, named VESBELT, for offloadingtasks from V essel users to nearby E dge S ervers or the cloud server, aiming to reduce E nergy consumption and service L atency T rade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long- short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide realtime solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.
In multi-access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, ...
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In multi-access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers;(2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process;(3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS-DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, D4PG$$ {\mathrm{D}}<^>4\mathrm{PG} $$ algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous D$$ D $$ time slots
offloadingcomputationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer visio...
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offloadingcomputationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer vision tasks, such as object segmentation and detection, are computationally heavy algorithms, requiring large memory and computing power. Many UAVs are using (pretrained) off-the-shelf versions of such algorithms. offloading such power-hungry algorithms to a remote server could help UAVs save power significantly. However, deep learning based algorithms are susceptible to noise, and a wireless communication system, by its nature, introduces noise to the original signal. When the signal represents an image, noise affects the image. There has not been much work studying the effect of the noise introduced by the communication system on pretrained deep networks. In this work, we first analyze how reliable it is to offload deep learning based computer vision tasks (including both object segmentation and detection) by focusing on the effect of various parameters of a 5G wireless communication system on the transmitted image and demonstrate how the introduced noise of the used 5G system reduces the performance of the offloaded deep learning task. Then solutions are introduced to eliminate (or reduce) the negative effect of the noise. Proposed framework starts with introducing many classical techniques as alternative solutions, and then introduces a novel deep learning based solution to denoise the given noisy input image. The performance of various denoising algorithms on offloading both object segmentation and object detection tasks are compared. Our proposed deep transformer-based denoiser algorithm (NR-Net) yields state-of-the-art results in our experiments.
Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computationaltasks are offloaded from UEs to the base sta...
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Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computationaltasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon's test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.
Within the realm of smart manufacturing, the growing complexity and scale of computationaltasks expose the limitations of conventional computing architectures with rigid task allocation and fragmented collaboration, ...
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Within the realm of smart manufacturing, the growing complexity and scale of computationaltasks expose the limitations of conventional computing architectures with rigid task allocation and fragmented collaboration, which impede the efficiency and security of smart manufacturing systems. To address these challenges, this paper presents an innovative cloud-fog-edge-terminal collaborative strategy that provides an adaptive and comprehensive offloading solution for tasks with complex dependencies and large scale, thereby enhancing the seamless collaborative potential of hierarchical computing structures. Additionally, a joint optimization mathematical model for collaborative computationaloffloading is developed, aiming to minimize taskoffloading time and assess manufacturing risk. To refine the solution, an advanced multi-objective optimization algorithm is formulated to identify the optimal solutions. The effectiveness and practical applicability of the proposed method are substantiated through simulation experiments and empirical case studies, demonstrating a performance enhancement of 12-29% over other benchmarks. The joint optimization method effectively synchronizes cloud-fog-edge-terminal computing resources, realizing efficient and secure taskoffloading and execution in smart manufacturing scenarios.
Applications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicle...
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Applications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. computationaloffloading based on Vehicle-to-Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computationaltasks. Therefore, a decision-making and intelligent taskoffloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete-time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy-efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy-efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100-500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.
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