Search

The increasing number of Internet of Things (IoT) sensors and the need for real-time data processing have led to the emergence of Fog Computing as a promising paradigm to complement cloud services. However, optimizing energy consumption while ensuring reliable data routing remains a critical challenge. This paper proposes an Energy-aware Two-Way Trust Routing (ETWTR) scheme tailored for fog computing environments. This scheme can perform routing to improve data transmission dynamically, taking into account reliability and energy availability. ETWTR can simultaneously consider security, reliability, quality of service, and energy consumption, overcoming the limitations of fog computing. The ETWTR-based trust management system allows both requesters and service providers to check each other's reliability simultaneously to improve security. To address the challenge of energy optimization, a Deep Q-Network (DQN) is employed to predict available energy levels in nodes and optimize their usage efficiently. Our ETWTR scheme has been compared with baseline schemes to confirm its merit with various criteria. The simulation results show that ETWTR has reached higher reliability and trust with less energy consumption. [ABSTRACT FROM AUTHOR]

Background: The Industrial Internet of Things (IIoT) has revolutionized operations for businesses, and fog computing is a valuable resource management and job scheduling tool that has facilitated the transformation. In this regard, efficient resource usage will be more useful for the performance and energy cost-saving limit of IIoT system. Objective: The article proposed a new energy-aware metaheuristic approach to enhancing the performance and efficiency of IIoT systems in fog computing environments. The study aims to come up with a methodology that strikes a balance between efficiency in compute requirements and energy consumption. Methodology: A meta-heuristic method inspired by natural processes such as genetic algorithms and simulated annealing is applied to optimize the selection of which jobs should be scheduled. This approach takes into account several parameters like when the job is needed, availability of resources, and usage patterns to efficiently schedule jobs across the network. Results: The results show that the proposed approach drastically improves energy efficiency and system performance. In this paper, the fog orchestration master intends to divide the workload between fog and cloud in an excellent manner and resolve specific Issues of fog computing in IIoT environment. This manages to keep energy usage low and the operating efficiency high. Conclusions: Metaheuristic optimization techniques integrate into fog computing environments for IIoT job schedule complexity. This methodology enhances the sustainability of IIoT operations, and their ability to meet robust performance requirements over time. Our findings provide the crucial insight needed to enable industries that need seamless IIoT integration and plan further research in the field of energy-efficient fog computing.

Summary: The rise of the Internet of Things (IoT) has given rise to an era marked by interconnected devices and substantial data generation. This has led to an increased reliance on cloud computing for data processing and storage, primarily due to its cost‐effective pay‐for‐use model. However, this dependence has prompted critical inquiries into the optimal replication of data: what data to replicate, when to replicate it, and where to place new replicas strategically. Conventional cloud data replication often results in resource overutilization, performance bottlenecks, increased workloads, energy consumption, prolonged user wait times, and suboptimal response times. In response to these challenges, this paper introduces a novel approach named Multiobjective Optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA). This approach employs multiobjective optimization (MOO) alongside Harris Hawks Optimization (HHO) and IoT‐based Salp Swarm Algorithm (SSA) for cloud computing environments. MOHHOSSA efficiently identifies data replication opportunities and strategically allocates them across nodes in cloud computing infrastructures. The algorithm aims to enhance key performance metrics, including energy consumption, carbon dioxide emission rate, and mean service time. Extensive experimental validation demonstrates MOHHOSSA's superior performance compared to alternative algorithms. It excels in optimizing energy efficiency, load distribution, mean service time, and the establishment of cost‐effective communication paths between nodes. This research represents a significant advancement in addressing challenges related to IoT data replication in cloud computing, ultimately promoting more sustainable, efficient, and responsive cloud‐based services. [ABSTRACT FROM AUTHOR]

The transmission of point cloud video streaming requires the scheduling of various computing and transmission resources. Existing research rarely considers the energy consumption issues caused by the computing tasks of terminal display devices. To solve this problem, a point cloud video streaming transmission scheme assisted by mobile edge computing (MEC) was proposed, which offloaded part of computing tasks to the MEC server based on the access bandwidth and point cloud video content. A joint optimization model was established in this scheme to maximize the quality of user’s viewing experience and minimize the energy consumption of terminal device under the constraints of network resources, terminal and edge computing resources. Experimental results show that the proposed scheme can improve the viewing quality of users and reduce the energy consumption of terminal equipment compared with the contrast scheme under different conditions.

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs’ limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates. [ABSTRACT FROM AUTHOR]

Solving remanufacturing process planning and scheduling problem collaboratively and leveraging the complementary attributes of process planning and shop scheduling to attain improved production flow and process routes, are crucial for further enhancing the environmental and economic benefits of remanufacturing. Most of the existing works regard these two segments as independent and solve them separately, which hinder the further improvements of remanufacturing system performance. Besides, studies on energy-aware remanufacturing scheduling have employed machine turn on/off strategy to achieve energy reductions. However, not all machines are suitable for the turn on/off strategy. Therefore, a new energy-aware remanufacturing process planning and scheduling model with process sequence flexibility is proposed. This model not only simultaneously solves the remanufacturing process planning and scheduling problem, but also employs machine speed-switching strategy to reduce energy consumption. To solve this model, a reinforcement learning-based particle swarm optimization algorithm with an efficient multi-dimensional encoding scheme is proposed, in which, a hybrid population initialization strategy, a novel reinforcement learning-based multi-directional guide position-updating mechanism, a local search strategy, and a restart mechanism are devised to enhance the performance. Simulation experiments were conducted on 18 sets of instances with different scales to compare the proposed algorithm with other advanced algorithms. The experimental results confirmed the superiority of the proposed algorithm. • A new energy-aware remanufacturing process planning and scheduling problem is investigated. • Machine, process plan, and process sequence flexibility is considered to improve the practicality and flexibility of the model. • A speed-switching strategy is employed in the model to reduce energy waste. • A reinforcement learning-based particle swarm optimization algorithm is proposed to solve the model. • Experimental results verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]