Sustainable Computing-Informatics & Systems最新文献

In this paper, an uncertainty-aware task scheduling approach capable of dynamically applying on multiprocessor embedded systems called ”DyUnS” is presented. This method is based on a type-2 fuzzy inference system to consider all design challenges of multiprocessor embedded systems along with their unavoidable uncertainty caused by the differences in models and measurements. The proposed method employs a fuzzy inference system to approximate the appropriate assignment of the application’s tasks to processing cores based on a defined rank including the main design challenges of the system including execution time, temperature, power consumption, and reliability. Moreover, an uncertainty level is defined for various design challenges as the footprint of uncertainty during the scheduling process to tackle the existing inaccuracy between the static models and dynamic environment. Thus, the generated uncertainty-aware solution could be efficiently employed as a dynamic scheduling at runtime. To demonstrate the effectiveness of DyUnS in tolerating uncertainty, several experiments on various application graphs are performed and its effectually is compared to related studies. Based on these experiments, DyUnS jointly optimizes the main design parameters, and its generated solution could be employed dynamically without violating the system’s thresholds. Moreover, its average difference compared to Monte Carlo uncertainty analysis is about 0.2 for all design parameters in three levels of uncertainty.

In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In this context, swarm intelligence-based algorithms are identified to be suitable and ideal for determining near-optimal and optimal solutions in the search space. On the other hand, APTEEN routing protocol possesses the issues that are related to unnecessary energy drain, ineffective overall network coverage and premature death of certain nodes. To address these issues, an attempt to optimize the APTEEN routing protocol using Dingo Optimization Algorithm (DOA) and Beluga Whale Optimization Algorithm (BWOA) is made in this proposed clustering protocol. With this motivation, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimized the energy consumption and maximize the lifetime of the network. It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination. It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime. The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation.

The Task Scheduling Problem (TSP) in Multiprocessor Systems (MPS) is an emerging research area in heterogeneous distributed computing environments. Managing complex tasks and achieving optimal efficiency while scheduling tasks in MPS presents challenges. Although adding more processors to a single system greatly increases its processing power, the energy these processors produce is the main drawback. Here, we employed a multi-objective optimization strategy to reduce the makespan and Energy Consumption (EC). To reduce processor EC, we considered an effective Dynamic Voltage and Frequency Scaling (DVFS) technique. Three distinct energy sources are considered, originating from the processors' communication, idle, and active states. The scheduling sequence of tasks and the assignment of tasks to processors are two vital aspects of TSP. Here, the tasks are arranged based on priority using the Upward Rank technique. To minimize the makespan and EC while allocating tasks to processors, we use the population-based metaheuristic method called Enhanced Honey Badger Optimisation (EHBO). We proposed three improvements to the HBO algorithm in EHBO. Initially, we addressed opposite learning-based population initialization to exclude the least suitable candidates and generate a candidate scheduling population that adheres to task precedence. Subsequently, the levy-flight technique is employed to improve the local and global search and preserve their ideal healthy balance. Finally, using dynamic values rather than constant value improves the ability to obtain maximum food. Several experiments are conducted on random task graphs and real-life data sets. Additionally, the results are compared with other upgraded meta-heuristic algorithms, demonstrating the superiority of EHBO.

The users of home energy management systems schedule their real-time energy consumption thanks to advancements in communication technology and smart metering infrastructures. In this paper, a data-driven strategy is proposed, which is an Online Home Energy Management System (ON-HEM) that uses reinforcement learning algorithms (Q-Learning and Deep Q-Learning) to control the optimal energy consumption of a smart home system. The proposed system comprises power resources (grid, photovoltaic), communication networks, and appliances with their agents classified into four groups: deferrable, non-deferrable, power level controllable, and electric vehicle. The system reduces electricity costs and high peak demands while considering the cost of user dissatisfaction with real-life data. Simulations are performed on the proposed ON-HEM considering different pricing approaches (Real Time Pricing and Time of Use Pricing) with Q-Learning and Deep Q-Learning (DQL) algorithms using PyCharm Professional Edition software. The findings demonstrate both the superiority of DQL over Q-Learning and the efficiency of the proposed ON-HEM in decreasing high peak demand, electricity costs, and customer dissatisfaction costs. The efficiency and dependability of the proposed system were verified by utilizing simulation-based findings with real-life data using IBM SPSS Statistics software.

The field of smart cities has seen significant advancements in recent years to improve citizens' quality of life. Technologies such as the Internet of Things (IoT) and Edge Computing (EC), along with Artificial Intelligence (AI), are being utilized to achieve this goal. This study focuses on a specific branch of IoT known as Visual IoT, which uses digital cameras as sensors and relies on visual data. Advances in AI have enabled researchers to integrate AI models into camera-based edge devices, increasing the use of AI-powered Visual IoT systems in smart cities. However, since the energy consumption in battery-powered systems is naturally a concern, being deployed outdoors for visual data gathering with the integration of AI-based processing raises a significant challenge. This paper examines AI-powered Visual IoT systems in smart cities with a special emphasis on energy efficiency. Our goal is not only to evaluate how AI is used in Visual IoT systems in the context of smart cities but also to evaluate the level of consideration given to the energy efficiency aspect in the reviewed studies. Furthermore, we explore all of the methods used to address it. Through our work, readers will gain insights into the current landscape of Visual IoT in smart cities and an understanding of how much importance is placed on energy consumption in AI-integrated solutions.

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92$j$), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.

Technological approaches for effective energy regulation are required due to incorporating contemporary electrical systems with sustainable power resources. Our article suggests a Transactive Energy Managing System (T.E.M.S.) using Blockchain-based technologies and Multiple-Agent Modelling (M.A.M.) to improve the long-term viability and dependability of energy-efficient grids. The framework utilizes a decentralized methodology enabled by self-governing agents. These units include services, sellers, and customers in the electricity system. Such entities engage in vibrant interactions, trading energy according to current economic circumstances, choices, and facts. Blockchain-based technology promotes a more robust and decentralized power industry by eliminating the demand for centralized mediators and improving information security. The suggested T.E.M.S. aims to tackle issues such as demand-response administration, incorporation of sustainable energy resources, and grid reliability. The efficiency of the mechanism in maximizing power use, lowering load spikes, and fostering an improved power environment is proved via modelling and evaluation. The research advances intelligent grid technology by providing an extensive, decentralized approach that aligns with the changing power industry. In this era of connected layouts, integrating Blockchain-based technologies with Multiple-Agent Modelling offers a solid basis for creating flexible and adaptable power control technologies.

One of the essential components of computer systems is memory. A primary hindrance in this regard is the memory speed. Content Addressable Memory (CAM) speeds up transformations and table lookups in network routers and data processing systems for hardware search engines. Parallel seeks using the CAM (Content Addressable Memory) model are often used to enhance memory performance. This paper uses the voltage swing self-adjustable match line (VSSA-ML) technique to describe low-power content addressable memory design and implementation. This project decreases Match Line (ML) power loss by reducing load capacitance and ML voltage swing. A simple ML voltage detector is proposed instead of the complex, fully different detector that allows ML voltage swings near zero. This paper presents 6 T 8×8 CAM arrays using VSSA-ML Technique using Tanner tools 45-nm technology. On the other hand, this design enhances robustness in processing variations by self-adjusting voltage swings. Implementation analysis states that the described mode 6 T 8×8 CAM design utilized fewer MOSFETs than the 8 T 8×8 CAM array.

Integrating EVs (Electric Vehicles) with the electrical system presents essential load distribution difficulties because EV recharging structures are unpredictable and variable. The article presents an innovative technique employing multiple-agent deeper Q-Networking (MADQN) to coordinate electric automobiles and improve the electricity system balance of load. The suggested MADQN simulation rapidly optimizes battery charge plans by utilizing the capabilities of multiple agent networks as well as deeper reinforced learning. The framework adjusts to current network situations utilizing cooperative decision-making between substances, considering variables like a need for power, accessibility to green energy sources, and protection of the arrangement. Beneficial load distribution is made possible when reducing expenses and ecological damage because of the system's capacity to gather data from and modify intricate, changing circumstances. The findings from the modelling indicate how well the suggested MADQN method works to enhance network efficiency, lower peak usage, and use more sustainable power resources. These factors help build a more robust, adaptable, intelligent grid environment.

The Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-to-Device (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state-of-the-art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility.