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Using Mobile Sinks in WSN: Computational Complexity and a Theoretical Bound

Chinese Journal of Electronics 2023年第1期20卷 147-150页

作者： Yu Gu Yusheng Ji Hongyang Chen Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China State Key Laboratory of Networking and Switching Technology Beijing China Information Systems Architecture Science Research Division NII Tokyo Japan Institute of Industrial Science University of Tokyo Tokyo Japan

In this paper, we study the lifetime optimization problem in wireless sensor networks using mobile sink nodes. This problem is inherently difficult since we need to consider both sink scheduling and data routing. Through a simple case study we develop a novel notation named the Placement pattern (PP) to bound traffic patterns with candidate locations. This significantly decreases the number of elements needed to be scheduled. Based on the PP, we mathematically formulate this optimization problem as a Mixed-integer non-linear programming (MINLP), which is very tough and time consuming to solve. By proving that the problem is NP-complete, we point out that instead of seeking an optimal algorithm, heuristic algorithms, especially those with performance guarantee, would be much more desirable to develop. Furthermore, in order to help identify performance gains of heuristic algorithms proposed in the future, we develop a Linear programming (LP) formulation which serves as an upper bound by adopting a reformulation and relaxation technique.

关键词： Wireless sensor networks Upper bound Heuristic algorithms Traffic control Programming Performance gain Routing

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Image super resolution using Enhanced Attention Block (EAB) for Grape Leaf Disease Detection

Image super resolution using Enhanced Attention Block (EAB) ...

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International Conference on computer Applications In Electrical Engineering-Recent Advances (CERA)

作者： P.V. Yeswanth Y Thanya S Deivalakshmi K M Lokesh Kumar Johaan George Department of Electronics and communication engineering National Institute of Technology Tiruchirappalli India Department of Computer and communication engineering Amrita Vishwa Vidyapeetham Chennai India Department of Ocean engineering and Naval Architecture Indian Insitute of Technology Kharagpur India

Accurate disease diagnosis in grape leaf analysis is heavily reliant on high-resolution (HR) images. To meet this need, we propose a model that incorporates the Enhanced Attention Block (EAB) to generate HR images for improved disease detection. The EAB employs advanced attention mechanisms and residual learning to effectively capture critical, spatial and channel-wise relationships. Proposed model is assessed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics, yielding PSNR scores of 31. 902dB, 32.785dB, and 34. 853dB, and SSIM score of 0.8709, 0.9058, and 97.46 for factors 2,4 and 6 respectively. This proposed approach holds great potential for widespread implementation in disease detection across various plant species, allowing for timely and accurate interventions for plant health management and increasing industry productivity.

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Tighter Bounds of Speedup Factor of Partitioned EDF for Constrained-Deadline Sporadic Tasks 42

Tighter Bounds of Speedup Factor of Partitioned EDF for Cons...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Liu, Xingwu Chen, Zizhao Han, Xin Sun, Zhenyu Guo, Zhishan School of Mathematical Sciences Dalian University of Technology China SK-Lab of Media Convergence Production Technology and Systems Chinaso Inc School of Software Technology Dalian University of Technology China SK-Lab of Computer Architecture ICT CAS University of CAS Beijing China Department of Electrical and Computer Engineering University of Central Florida United States

ISBN: (纸本)9781665428026

Even though earliest-deadline-first (EDF) is optimal in terms of uniprocessor schedulability, it is co-NP-hard to precisely verify uniprocessor schedulability for constrained-deadline task sets. The most efficient way to solve this problem in polynomial time is via a partially linear approximation of the demand bound function. Such approximation leads to a simple uniprocessor schedulability testing with speedup factor ρ. Such a result further leads to Deadline-Monotonic Partitioned-EDF on multi-processors with speedup factor of 1 + ρ − 1/m (where m is the number of processors). The current state of the art results indicate that ρ is within the range [1.5, 14/9]. Especially, it has been a conjecture that ρ = 1.5. This paper improves the range of ρ to (1.5026, 1.5380). The improved lower bound disproves the conjecture of lower bound 1.5. A novel technique is to construct an auxiliary function that is larger than the approximate demand bound function but keeps the supremum ρ unchanged. It solves the dilemma that beating the lower bound 1.5 requires extremely large task sets, while the large size makes it difficult to check the schedulability. This technique not only enables us to disprove 1.5 by a task set of only eight tasks, but also sheds light on future work in transferring/downsizing task sets and deriving utilization bound based tests for various workload abstraction models, such as DAG tasks. ©2021 IEEE

关键词： Polynomial approximation

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Reducing the Time of Live Container Migration in a Workflow 17th

Reducing the Time of Live Container Migration in a Workflow

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Di, Zhanyuan Shao, En He, Mujun University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Chongqing University Chongqing China

ISBN: (纸本)9783030794774

As a lightweight virtualization solution, container technology can provide resource limiting capabilities and can run multiple isolated process sets under a single kernel instance. Multi-tenant preemption leads to competition in computing, storage, and network resources, resulting in degraded computing service performance. Virtualization service migration can provide a solution to the problem of resource shortage in supercomputing systems. However, the resource overhead and delay in the migration process also reduce the efficiency of high-performance computers. To solve this problem, this dissertation proposes a method of container migration and a tool for supporting container migration. Also, this paper optimizes the startup of containers from checkpoints and proposes a multi-container migration strategy, reducing migration time by 30% compared to sequential migration. The migration method in this paper provides valuable experience for service migration in supercomputers and data centers. © 2021, IFIP International Federation for Information Processing.

关键词： Containers

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Swarm Intelligence in Internet of Medical Things 10

Swarm Intelligence in Internet of Medical Things

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Roshanzamir, Mohamad Darbandy, Mohammad Tayarani Roshanzamir, Mahdi Alizadehsani, Roohallah Shoeibi, Afshin Ahmadian, Davood Fasa University Faculty of Engineering Department of Computer Engineering Fasa74617-81189 Iran Islamic Azad University Taft School of Architecture Taft*** Iran University of Tabriz Department of Electrical and Computer Engineering Tabriz Iran VIC Australia K. N. Toosi University of Technology FPGA Laboratory Faculty of Electrical Engineering Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Sciences Tabriz Iran

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Advancement in information and communication technology leads to different communication channels. One of them is the Internet of Things (IoT) which plays an important role in the new world. The Internet of Medical Things (IoMT) is the sub-branch of IoT which focuses on the communication between medical devices to share their data and information. It helps the healthcare industry to present better services for physicians and patients. It improves the quality of life by increasing the quality and decreasing the time and cost of services. An important challenge in IoMT is optimization in its different sub-problems and Swarm Intelligence (SI) algorithms are the best solution for them. In this paper, we tried to investigate the status of using SI in IoMT. Its applications and their corresponding performance were analyzed and investigated. The reported results show that using SI can efficiently solve the optimization problems in IoMT. © 2022 IEEE.

关键词： Internet of things

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NOMA: A Novel Reliability Improvement Methodology for 3-D IC-based Neuromorphic Systems

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IEEE Transactions on Components, Packaging and Manufacturing technology 2024年

作者： Nguyen, Ngo-Doanh Dang, Khanh N. Ahmed, Akram Ben Abdallah, Abderazek Ben Tran, Xuan-Tu The University of Aizu The Graduate School of Computer Science and Engineering Fukushima Aizu-Wakamatsu965-8580 Japan Vietnam National University The Information Technology Institute Hanoi10000 Viet Nam National Institute of Advanced Industrial Sciences and Technology The Digital Architecture Research Center 2-3-26 Aomi Koto-ku Tokyo Japan

Hardware is the foundation of trust for all real-time applications and is especially crucial in Artificial Intelligence (AI). The growing reliance on AI for various tasks has raised concerns about the reliability of the hardware. Extensive research has shown that hardware faults arising from the manufacturing process or device variation can significantly impact the precision and accuracy of AI applications. This also applies to 3-D IC (Three-Dimensional Integrated Circuit)-based Spiking Neural Networks (SNNs), despite their noise resilience, low-power, and low memory footprint advantages. This is because, in addition to the mentioned hardware faults, thermal dissipation on 3-D ICs can negatively affect the memory stacked on upper dies. Hence, this article proposes a methodology called NOMA (Network-of-Memory architecture) to enhance the reliability of 3-D IC-based SNNs by replacing the defective critical synaptic weights in high-priority layers with lower-priority ones. The method is assessed on multiple memory-on-logic architectures with various memory technologies such as SRAM, eDRAM, STT-RAM, and RRAM. As a result, for the 45nm SRAM library, the accuracy degradation improves by roughly 56% at a BER of 0.05 with a timing overhead of 3.538μs and a power overhead of 390.796nJ. © 2011-2012 IEEE.

关键词： Memory architecture

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AgileWatts: An Energy-Efficient CPU Core Idle-State architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

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Suitability of ISAs for Data Paths Based on Redundant Number Systems: Is RISC-V the best?

Suitability of ISAs for Data Paths Based on Redundant Number...

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Euromicro Symposium on Digital System Design

作者： Johannes Knödtel Sebastian Rachuj Marc Reichenbach Chair of Computer Engineering Brandenburg University of Technology (B-TU) Cottbus Germany Chair for Computer Architecture Friedrich-Alexander-Universitat Erlangen-NUrnberg (FAU) Erlangen Germany

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

It has been known for a long time that in processor design, delay in arithmetic circuits can be reduced by using redundant number representations (RNS). This advantage is currently only exploited to a limited extent since various aspects complicate its use. For this reason the redundant representation is abandoned at the boundaries of the AL U and the values are reconverted back to the traditional binary representation. In particular, some operations that are traditionally considered fast are now subject to a higher delay. Among other concerns, this complicates comparison operations (e.g. equal to, greater than) and thus affects the timing behavior of conditional jumps. There is some initial research promising speedups using RNS in register files and the data path, but there are still some open questions. In particular it is important to evaluate how the instruction set is designed. Such a study is necessary to estimate whether it is worthwhile to develop the entire data path beyond the AL U in redundant representation. If no reconversion from redundant to traditional binary number system takes place, then e.g. the evaluation of condition codes or flags is problematic, since all speed advantages are lost again. In this work a qualitative and quantitative analysis of common ISAs in processors with redundant data paths is presented. All relevant properties of an ISA are identified and an evaluation of several common ISAs according to these criteria. A performance comparison of three common RISC ISAs (MIPS, A64 (ARM), RISC- V) is given based on a simulation of the Embench bench-mark suite using an adapted version of QEMU. This comparison estimates the speedup of processors with redundant versus binary data paths. It was found, that RISC- V was overall outperforming the other ISA with a maximum speedup of 1.41.

关键词： Adaptation models Reduced instruction set computing Codes Statistical analysis Digital systems Delays Registers

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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