Article
Computer Science, Information Systems
Haneen Alhomyani, Mai Fadel, Nikos Dimitriou, Helen Bakhsh, Ghadah Aldabbagh, Samar Alkhuraiji
Summary: This paper discusses the applications of wireless sensor networks with linear topologies and the challenges of designing routing protocols to meet the specific requirements of such systems. It presents a LoRa multi-hop model for monitoring aboveground oil pipelines and evaluates its performance in various scenarios.
Article
Computer Science, Information Systems
Raoudha Saida, Yessine Hadj Kacem, Mohammed S. BenSaleh, Mohamed Abid
Summary: This paper presents a new solution, called EARN-MDE, for the design and development of a reconfigurable WSN system based on the model driven engineering paradigm. The proposed process offers a complete methodology for developing reconfigurable WSN, including automatic generation of high-level models and codes. The EARN-MDE process is validated through the EARNPIPE demonstrator.
Article
Computer Science, Information Systems
Ali Mohammed Kadhim Abdulzahra, Ali Kadhum M. Al-Qurabat, Suha Abdulhussein Abdulzahra
Summary: This article proposes an energy-efficient protocol for IoT that uses WSN, called EFUCSS, which extends the network lifespan and reduces energy consumption through clustering, scheduling, and data transmission. The protocol forms unequal clusters based on Fuzzy C-Means to balance energy usage and uses a fuzzy logic system for cluster head selection. Extensive simulation experiments show that EFUCSS improves remaining energy and network lifespan significantly compared to other protocols.
INTERNET OF THINGS
(2023)
Article
Chemistry, Analytical
Muhammad Fawad, Muhammad Zahid Khan, Khalil Ullah, Hisham Alasmary, Danish Shehzad, Bilal Khan
Summary: An enhanced DV-Hop algorithm is proposed in this paper for accurate localization in wireless sensor networks. The algorithm corrects the single-hop distance, modifies the average hop distance, and uses the least-squares approach to estimate the location of unknown nodes, resulting in significantly improved accuracy and reduced energy consumption.
Article
Chemistry, Analytical
Wooseong Kim, Muhammad Muneer Umar, Shafiullah Khan, Muhammad Altaf Khan
Summary: The proposed novel routing mechanism in this work balances energy consumption among all nodes and extends the lifetime of WSNs by assigning scores to each node based on evaluation metrics. The scoring scheme considers information such as node density and energy levels to represent the importance of nodes in routes.
Article
Chemistry, Analytical
Lial Raja Al-Zabin, Ola A. Al-Wesabi, Hamed Al Hajri, Nibras Abdullah, Baidaa Hamza Khudayer, Hala Al Lawati
Summary: Wireless sensor networks (WSNs) are widely used in event detection and environmental observation applications. Existing event detection methods often rely on static or threshold values, leading to inaccurate sensor readings. This paper proposes a hybrid event detection technique named Probabilistic Collaborative Event Detection (PCED), which utilizes a cluster WSN topology. PCED transforms sensing values into probability formulas using a validated probabilistic technique and introduces a Cluster Head Decision Mechanism for decision-making based on aggregated data. Fuzzy logic is employed at the fusion center level to enhance event detection accuracy. The evaluation using MATLAB shows that PCED significantly reduces false alarms and improves detection accuracy and latency compared to well-established event detection mechanisms such as REDF.
Article
Computer Science, Hardware & Architecture
Vikas Tyagi, Samayveer Singh
Summary: The main goal of designing SDN-enabled WSNs with limited network resource utilization is to maximize the network lifespan. Clustering and routing techniques are widely used to achieve energy-efficient and stable network performance in SDN-enabled WSNs by balancing the network load. However, selecting optimal control nodes (CNs) is a critical challenge in clustering due to the high computational complexity of global optimization. Therefore, optimizing the CNs selection for the routing process with limited network resources is necessary.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Tanvi Sood, Kanika Sharma
Summary: This paper proposes a new clustering protocol, LUET, based on uniform connectivity for energy-efficient coverage in WSN. The protocol considers node's remnant-energy and its proximity from the lines of uniformity to lower down average isolated node count, and introduces a variant to overcome rapid fall after node death. Simulation model demonstrates superiority of LUET and its variants over established clustering protocols in terms of network performance metrics.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Rafael Estepa, Antonio Estepa, German Madinabeitia, Ernesto Garcia
Summary: A novel cross-layer scheme based on RPL is proposed to reduce energy consumption in wireless sensor networks, showing significant energy savings by selecting low-power settings in certain areas. By selecting optimal local transmit power and parent nodes, energy efficiency can be improved without sacrificing transmission efficiency.
Article
Energy & Fuels
Ankur Choudhary, Santosh Kumar, Sharad Gupta, Mingwei Gong, Aniket Mahanti
Summary: Technological advancements have increased confidence in designing large-scale wireless networks consisting of small energy-constrained devices. The proposed Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm addresses limitations of existing algorithms and shows encouraging simulation results.
Article
Remote Sensing
Salil Bharany, Sandeep Sharma, Jaroslav Frnda, Mohammed Shuaib, Muhammad Irfan Khalid, Saddam Hussain, Jawaid Iqbal, Syed Sajid Ullah
Summary: This paper presents a unique clustering approach for forest fire detection, which transfers data to a base station via wireless communication to extend the lifetime of unmanned aerial vehicles. The proposed EE-SS algorithm, which regulates the energy usage of nodes, outperforms other state-of-art algorithms in terms of overall energy usage, network lifetime, and cluster building time.
Article
Computer Science, Theory & Methods
Xu Zhu
Summary: This paper discusses the fusion approach of Radio Frequency Identification (RFID) technology and wireless sensor networks, addressing energy imbalance in infusion networks. By designing an improved network structure model, developing RFID systems, and researching environmental collection terminals, the monitoring system design and development are achieved. The efficiency and accuracy of the results are improved compared to other studies, making the model more practical.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Instruments & Instrumentation
Farzad Kiani, Amir Seyyedabbasi, Sajjad Nematzadeh
Summary: This paper introduces three new methods based on metaheuristic algorithms for optimal cluster head selection, aiming to extend the network lifetime, conserve energy, enhance overhead, and improve packet delivery ratio.
Article
Computer Science, Information Systems
Deena Sivakumar, S. Suganthi Devi, T. Nalini
Summary: Energy efficiency is a challenging problem in Wireless Sensor Networks (WSNs) due to limited energy resources. This study proposes an energy-aware clustering protocol using a chaotic gorilla troops optimization algorithm (EACP-CGTOA) to improve the network lifetime and efficiency. The EACP-CGTOA model employs a circle chaotic mapping to explore solutions with high convergence rate and sensitivity, and utilizes a fitness function involving distance to neighbors (DTN), distance to base station (DBS), and energy ratio (ER).
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Panayiotis Kolios, Loizos Papachristoforou, Christos Panayiotou, Georgios Ellinas
Summary: Event-triggering is a promising technique for efficient operation of IoT devices by applying communication and control after event interrupt. This work applies event-triggering technique on public transportation services to track buses accurately while limiting communication. A multi-model event triggering technique is proposed, where multiple models are derived to represent system state, allowing hosts to adapt to system changes for better performance in terms of tracking accuracy and reduced communication energy consumption compared to single-model and periodic triggering approaches.
Article
Computer Science, Theory & Methods
Jiang Xiao, Huichuwu Li, Minrui Wu, Hai Jin, M. Jamal Deen, Jiannong Cao
Summary: This article introduces the latest research progress in wireless device-free human sensing (WDHS) technology, classifying the systems into different categories and discussing various sensing task types and motion granularity. The article also proposes a new research framework to summarize WDHS systems, and presents future research directions in terms of data collection, sensing methodology, performance evaluation, and application scenarios.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Lei Yang, Jiaming Huang, Wanyu Lin, Jiannong Cao
Summary: Personalized federated learning (PFL) provides personalized models that fit the local data distribution of each client. We propose a Group-based Federated Meta-Learning framework (G-FML) that adaptively divides clients into groups based on data distribution similarity to achieve personalized models. Experimental results show that our framework improves model accuracy by up to 13.15% compared to state-of-the-art federated meta-learning.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Theory & Methods
Juncen Zhu, Jiannong Cao, Divya Saxena, Shan Jiang, Houda Ferradi
Summary: Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices without exchanging local data samples. Existing centralized solutions have disadvantages, and blockchain has been identified as a potential solution. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Electrical & Electronic
Rongling Yu, Ping He, Heng Li, Jiannong Cao, Feiqi Deng
Summary: This article investigates the consensus problem of linear multi-agent systems (MASs) with unknown external disturbances under intermittent communication. Firstly, the distributed extended observer is utilized to observe the relative output information and unknown disturbance. Then, a distributed active disturbance rejection intermittent consensus protocol is proposed using the observer information. Finally, a simulation example is provided to demonstrate the effectiveness of the consensus protocol.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Hardware & Architecture
Zhenyu Ning, Chenxu Wang, Yinhua Chen, Fengwei Zhang, Jiannong Cao
Summary: Processors nowadays come with debugging features for program analysis, but the security of these features has been under-examined, especially with the introduction of a new debugging model by ARM. This article provides a comprehensive security analysis of ARM debugging features and discovers a new attacking surface that exists universally in platforms with ARM-A architecture. It also presents the Nailgun attack as an example, which exploits the debugging features to obtain sensitive information and execute arbitrary payloads. The article suggests potential mitigations and proposes a practical defense mechanism based on ARM virtualization technology.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Hongbo Jiang, Jigang Wen
Summary: This paper proposes a novel Graph-based Tensor Recovery model (Graph-TR) for accurate Internet anomaly detection. By incorporating non-linear proximity information using nearest neighbor graphs and graph Laplacian, the proposed approach outperforms state-of-the-art algorithms in terms of detection accuracy.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Jigang Wen
Summary: This paper studies a novel sparse measurement scheduling problem in network management. By taking advantage of low-rank features and using tensor completion, the authors propose a scheme that can accurately obtain complete network-wide monitoring data with a low sampling ratio. Experimental results show that their approach outperforms other tensor completion algorithms in terms of sample efficiency.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Zengcheng Sun, Ping He, Heng Li, Jiannong Cao, Feiqi Deng
Summary: The group consensus of second-order sample multi-agent systems is investigated using a distributed event-triggered mechanism. A consensus protocol is proposed, which is updated only when the event-triggered condition is met and the update only depends on the data collected at the moment of triggering. The sufficient condition for group consensus is obtained, and a differential equation is constructed to avoid the Zeno phenomenon and obtain a minimum positive and lower bound for any two trigger time intervals. The effectiveness is verified through a simulation example.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Wei Li, Jinlin Chen, Jiannong Cao, Chao Ma, Jia Wang, Xiaohui Cui, Ping Chen
Summary: In this article, a new augmentation model called EID-GANs is proposed to address the extremely imbalanced data augmentation problem. The model utilizes a new penalty function to guide the generator in learning the features of outliers and incorporates outlier detectors for evaluating the availability of generated instances. Experimental results demonstrate that EID-GAN outperforms existing augmentation models on different imbalanced datasets.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Hanqing Wu, Shan Jiang, Jiannong Cao
Summary: Supply chain traceability requires transparency, authenticity, and high efficiency in product tracking. Blockchain has been widely adopted for transparency and authenticity, but the efficiency issue has been overlooked.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Wei Liang, Yang Yang, Ce Yang, Yonghua Hu, Songyou Xie, Kuan-Ching Li, Jiannong Cao
Summary: The article introduces the personal data privacy protection scheme PDPChain based on consortium blockchain, which uses an improved Paillier homomorphic encryption mechanism and CP-ABE to achieve fine-grained access control, ensuring user privacy and security. Experimental results validate the effectiveness of the scheme in reducing encryption and decryption time.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen
Summary: Dynamic graphs are graphs whose structure changes over time. Existing approaches only consider dynamic graphs as a sequence of changes in vertex connections, ignoring the asynchronous nature of the dynamics where the evolution of each local structure starts at different times and lasts for various durations. To address this, we propose a novel representation of dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). We also introduce a time-aware Transformer to embed the dynamic connections and ToEs into learned vertex representations, along with encoding time-sensitive information. Our approach outperforms the state-of-the-art in various graph mining tasks and is efficient for embedding large-scale dynamic graphs.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Lei Yang, Junzhong Jia, Hongcai Lin, Jiannong Cao
Summary: This paper focuses on the problem of Service Function Chain (SFC) scheduling in the dynamic 5G network environment. It formulates the problem as a mixed integer non-linear programming and proposes a reinforcement learning method to increase the success rate of SFC requests.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Anais Durand, Michel Raynal, Gadi Taubenfeld
Summary: This article presents a new restriction of the classical basic computational model for solving consensus in asynchronous distributed systems. It introduces the concept of lambda-constrained crashes and shows that consensus can be achieved by enriching the system with objects of consensus number x >= 1. The article also presents algorithms for solving consensus under different conditions and discusses the impossibility results for the number of lambda-constrained failures that can be tolerated.
THEORETICAL COMPUTER SCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Kai Liu, Chunhui Liu, Guozhi Yan, Victor C. S. Lee, Jiannong Cao
Summary: This paper explores the acceleration of Deep Neural Network (DNN) inference in Vehicular Edge Computing (VEC) while ensuring reliability. The authors analyze the need for balancing DNN inference acceleration and reliability in VEC and propose a cooperative partitioning and offloading (CPO) problem. They also introduce two approximation algorithms, SA(3) and FMtR, for maximizing inference reliability. Simulation results demonstrate the effectiveness of the proposed solutions.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
