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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Chen, Yumeng Liu, Songlin Zhou, Jianhong Ling, Xiang University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications China Xihua University School of Computer and Software Engineering China

ISBN: (纸本)9798350363760

In modern real-time operating systems, complex task loads are often modeled as directed acyclic graphs (DAG) and executed in parallel on multiprocessor systems. The topological constraints present in DAG tasks prevent the DAG task allocation strategy from fully leveraging the parallel advantage of multiprocessor platforms. We analyze the parallelism of DAG tasks and propose a method to limit the number of processors that can be executed by DAG tasks. This method provides more idle processors and processor time for high parallelism DAG tasks by limiting the number of processors that can be executed by low parallelism DAG tasks. In addition, we develope optimistic parallelism scheduling algorithm (OPSA) based on the remaining utilization with low complexity. The experimental results show that our DAG task allocation strategy fully utilizes processor resources, reduces interference between DAG tasks, and achieves better worst case response time (WCRT) analysis results and shorter actual task's WCRT on real-time multiprocessor system. © 2024 IEEE.

关键词： Directed graphs

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Prototype Development of a Multilevel Solar Dryer for Agriculture

Prototype Development of a Multilevel Solar Dryer for Agricu...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Fernandez, Gian Carlo C. Luis, Jed Matthew A. San Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Basic food processing in the Philippine agricultural sector relies on drying, which makes food products less perishable. A common practice is traditional sun-drying, which is simple, but exposes the produce to contaminants. This study developed a multilevel solar dryer for agriculture to provide better drying conditions while keeping costs down. A temperature and humidity monitoring system was built into the dryer to control its cooling fans and the humidity inside. The dryer demonstrated better drying conditions with an average drying temperature of 47.38 °C compared to the traditional sun-drying's 39.38 °C. In addition, the solar dryer's inside humidity was also better with a recorded average of 31.29% RH compared to the traditional setup's 39.66% RH. The dryer successfully dried a hybrid of Capsicum frutescens and Capsicum annuum or siling tingala, as it is locally known, at a drying rate faster than the traditional sun-drying setup. Overall, the multilevel solar dryer was able to provide better drying conditions, a faster drying rate, and a protected enclosed space for drying food products. © 2024 IEEE.

关键词： Solar dryers

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Adaptive PV Systems: Using Genetic Algorithm and Random Forest Classifier to Mitigate the Effects of Partial Shading on Solar Photovoltaic Systems

Adaptive PV Systems: Using Genetic Algorithm and Random Fore...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Cabanayan, Ivan Perry M. Rodriguez, Adrian Enrico D. Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Mismatch losses due to partial shading of solar photovoltaic (PV) arrays can limit their generated power. One method to improve their power output is to electrically reconfigure the array based on the principle of row irradiance equalization. Shaded panels in an array are identified to determine the array's optimal configuration and maximize its power output. In this study, a random forest classification model was developed to identify the partial shading pattern incident on an array. Features of the model included the individual panel currents and the array irradiance of preset 20 shading patterns. The data used to train the model was obtained using an off-grid 3× 3 array composed of nine 10-W PV modules in total-cross-tied (TCT) topology. The model achieved a mean accuracy of 90% using the testing data and 98% using 5-fold cross-validation. This was integrated with a genetic algorithm-based irradiance equalization heuristic, which takes a predicted shading pattern as input and outputs an optimized configuration. For each shading pattern, the suggested reconfiguration was recorded and its corresponding power change was calculated. Power recovery ranged from -15.70% to 40%. The 3× 3 PV array reconfigured successfully to the optimum configuration. © 2024 IEEE.

关键词： Photovoltaics

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Near-Field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronic Engineering Jurong West 639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Atre, Anshul Singh, Kamlesh Kumar Chaurasia, Brijesh Kumar Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering UP Lucknow India Amity University Amity School of Engineering & Technology Department of Electronics Communications and Engineering UP Lucknow India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350385199

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes. © 2024 IEEE.

关键词： Cloud platforms

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Digital Twin-Assisted Space-Air-Ground Integrated Networks for Vehicular Edge Computing

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IEEE Journal on Selected Topics in Signal Processing 2024年第1期18卷 66-82页

作者： Paul, Anal Singh, Keshav Nguyen, Minh-Hien T. Pan, Cunhua Li, Chih-Peng National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT71NN United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211111 China

In this paper, we present a framework that integrates digital twin (DT) technology into space-air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our objective is to efficiently offload tasks in ultra-reliable low-latency communications (URLLC)-enabled vehicular networks, focusing on minimizing overall latency for requested tasks by reducing transmission time for task offloading and edge processing requirements. The proposed framework leverages DT-assisted SAGINs to minimize task offloading latency, expand network coverage, and reduce energy consumption. Key components of our framework include partial task offloading, distributed edge computing, latency modeling, energy consumption analysis, mobility, and channel modeling. We formulate a non-convex optimization problem considering various network constraints to achieve the system objective. To solve this optimization problem, we develop a novel multi-agent deep reinforcement learning (DRL) algorithm, enabling intelligent decision-making by individual agents. Through extensive simulations, we validate the effectiveness of our proposed system in advancing VEC by integrating DT technology into SAGINs. © 2007-2012 IEEE.

关键词： Energy utilization

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Cryptosystem for IoT Devices with Feedback Operation Modes Based on Shared Buffer and Unrolled-Pipeline Techniques

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IEEE Internet of Things Journal 2024年第3期11卷 4389-4397页

作者： Chin, Wen-Long Chen, Pin-Wei Chou, Shih-Hsiang Yang, Yu-Hua Chen, Pei-Yin Jiang, Tao National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China

This article presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network, and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gb/s for encrypting ten IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40 nm. The area efficiency of this work significantly outperforms the state-of-The-Art works. © 2014 IEEE.

关键词： Cryptography

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HachiFI: A Lightweight SoC Architecture-Independent Fault-Injection Framework for SEU Impact Evaluation

HachiFI: A Lightweight SoC Architecture-Independent Fault-In...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Cheng, Quan Liao, Wang Zhang, Ruilin Yu, Hao Lin, Longyang Hashimoto, Masanori Kyoto University Department of Communications and Computer Engineering Kyoto Japan School of Microelectronics Southern University of Science and Technology Shenzhen China School of Systems Engineering Kochi University of Technology Kochi Japan

ISBN: (纸本)9783982674100

Single-Event Upsets (SEUs), triggered by energetic particles, manifest as unexpected bit-flips in memory cells or registers, potentially causing significant anomalies in electronic devices. Driven by the needs of safety-critical applications, it is crucial to evaluate the reliability of these electronic devices before they are deployed. However, traditional reliability analysis techniques, such as irradiation experiments, are costly, while fault injection (FI) simulations often fail to provide full coverage and have limited effectiveness and accuracy. To address these issues, we introduce HachiFI, a lightweight, architecture-independent framework that automates fault injection with 100% coverage via memory and scan-chain accesses and simulates the behavior of SEUs based on specific cross-sections. HachiFI supports configurable fault injection patterns for both system-level and module-level reliability analysis. Using HachiFI, we demonstrate a low hardware overhead (2=0.984) between FI and irradiation experiments, verified on a 22nm edge-AI chip. © 2025 EDAA.

关键词： Failure rate

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Multi-hop Link Quality Prediction for DRL based Task Offloading in RSU-assisted IoV 10

Multi-hop Link Quality Prediction for DRL based Task Offload...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhao, Wei Zuo, Qixue Weng, Tangjie Xu, Xinwei Shao, Xun Liu, Zhi Anhui University of Technology School of Computer Science and Technology China Anhui University of Technology School of Management Science and Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan The University of Electro-Communications Department of Computer and Network Engineering Japan

ISBN: (纸本)9798331520861

Roadside units (RSUs) with strong sensing abilities enhance the feasibility of the RSU-to-Everything (R2X) paradigm, providing crucial infrastructure support for mobile edge computing and reducing data processing latency. Due to the limited computing capacity of RSUs, it is beneficial to offload tasks to other RSUs, vehicles, and cloud servers for processing, allowing for the efficient utilization of computational resources. To leverage the abundant idle vehicle resources on the road, tasks can be offloaded to vehicles outside the coverage area of the RSUs through multi-hop transmission. Due to the high mobility of vehicles, there is a risk of communication link disconnection during task transmission, which poses a challenge to the reliable transmission of tasks. In this paper, we ensure the communication link quality among nodes based on the established vehicle mobility model, and design a mechanism for selecting forwarding vehicles. The goal is to find the path with the highest reliability between vehicles and the source RSU. To encourage nodes to participate in multi-hop transmission, a certain relay cost and computational cost are paid to the computing nodes. We establish a joint optimization model focusing on task delay and task cost. To achieve this, we develop a 0-1 mathematical model and then transform it into a Markov Decision Process (MDP). Building on this foundation, we propose a solution based on Deep Reinforcement Learning (DRL). Experimental results demonstrate that this method significantly reduces task delay and task cost. © 2024 IEEE.

关键词： Markov processes

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MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV 10

MAPPO based Single-hop Task Offloading with Dynamic Vehicle ...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhao, Wei Yang, Dongling Weng, Tangjie Xu, Xinwei Shao, Xun Liu, Zhi Anhui University of Technology School of Computer Science and Technology China Anhui University of Technology School of Management Science and Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan The University of Electro-Communications Department of Computer and Network Engineering Japan

ISBN: (纸本)9798331520861

In the realm of Internet of Vehicles (IoV), Roadside Units (RSUs) play a vital role due to their advanced sensing and computing capabilities. They support Mobile Edge Computing (MEC) by enabling real-time data processing and reducing latency and energy consumption. Given the large volume of data and the limited computing capacity of RSUs, tasks are often offloaded to other nodes, such as the cloud, other RSUs, or vehicles. However, due to the environment dynamic caused by vehicle mobility, the set of neighboring nodes for each RSU frequently changes, and variations in neighboring nodes are not perceived timely. In such cases, RSUs need to predict changes in neighboring nodes to ensure effective task offloading. Moreover, since each RSU can only perceive the surrounding environment, it is difficult for a single node to make optimal offloading decisions for each task independently. To address these issues, this paper proposes a distributed task offloading scheme that incorporates vehicle trajectory prediction. First, each RSU uses the vehicle trajectories predicted by the Gated Recurrent Unit (GRU) model to determine neighboring vehicles and update the set of neighboring nodes. Then, to enable each RSU to select a suitable computing node and minimize computation delay and energy consumption for the task, we adopt a distributed approach where each RSU independently makes offloading decisions based on local observations. To achieve this goal, we establish a 01 mathematical model and transform it into a Markov Decision Process (MDP). Subsequently, we propose a solution based on Multi-Agent Proximal Policy Optimization (MAPPO). Through extensive simulation experiments, we demonstrate that the scheme effectively reduces both long-term average processing delay and energy consumption. © 2024 IEEE.

关键词： MAPPO neighboring vehicle prediction RSU task offloading

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