Article
Computer Science, Information Systems
Xiaofeng Gao, Jiahao Fan, Fan Wu, Guihai Chen
Summary: This paper investigates the cooperative sweep coverage problem with multiple mobile sensors and the multi-sink sweep coverage problem. Two constant-factor approximations, CoCycle and SinkCycle, are proposed to minimize the maximum sweep period for these two problems, with approximation ratios of 4 and 6, respectively. Additionally, optimal algorithms for the CSC problem and useful insights regarding the MSSC problem are provided. Numerical experiments are conducted to validate the effectiveness and efficiency of the designs.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Physics, Multidisciplinary
Thi-Kien Dao, Shu-Chuan Chu, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Nguyen
Summary: This paper proposes a solution to the optimal node coverage of unbalanced wireless sensor network distribution during random deployment based on an enhanced Archimedes optimization algorithm. The algorithm effectively improves the feasible range and convergence speed by combining the best network coverage results from multiple sub-areas.
Article
Engineering, Multidisciplinary
Chuijie Zeng, Tao Qin, Wei Tan, Chuan Lin, Zhaoqiang Zhu, Jing Yang, Shangwei Yuan
Summary: This paper proposes an improved wild horse optimizer algorithm (IWHO) to tackle the problem of network coverage and connectivity in heterogeneous wireless sensor networks (HWSNs). The IWHO combines the WHO algorithm and Golden Sine Algorithm (Golden-SA) to improve accuracy and convergence speed. It also incorporates opposition-based learning and the Cauchy variation strategy to avoid local optima and expand the search space. Experimental results demonstrate that the IWHO outperforms seven other algorithms in optimization capability. Validation experiments in different simulated environments show that the IWHO achieves better sensor connectivity and coverage ratio compared to other algorithms. After optimization, the HWSN achieves coverage and connectivity ratios of 98.51% and 20.04%, respectively, which decrease to 97.79% and 17.44% when obstacles are added.
Article
Computer Science, Information Systems
Yanbi Luo, Yongmao Hu
Summary: This study introduces a Parameters-Optimized HBA (POHBA) method to enhance the optimization performance of wireless sensor networks. By optimizing the parameters, POHBA achieves better network coverage rate without increasing algorithm complexity. The experiments demonstrate that POHBA outperforms other methods in various scenarios.
Article
Green & Sustainable Science & Technology
Qing He, Zhouxin Lan, Damin Zhang, Liu Yang, Shihang Luo
Summary: This paper proposes a wireless sensor network (WSN) coverage optimization model based on an improved marine predator algorithm (IMPA). The improved algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages, and uses a multi-elite random leading strategy to enhance the algorithm's solution accuracy and ability to jump out of local optima. Experimental results demonstrate that the improved algorithm outperforms the standard marine predator algorithm and other algorithms in the literature in terms of optimization performance and solving the WSN coverage optimization problem.
Article
Computer Science, Information Systems
Tuo Shi, Jianzhong Li, Hong Gao, Zhipeng Cai
Summary: This paper introduces the features of battery-free wireless sensor network (BF-WSN), defines a new coverage problem in BF-WSN, and proposes several algorithms to solve it. Extensive simulations demonstrate the effectiveness and efficiency of these algorithms.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Yin-Di Yao, Xiong Li, Yan-Peng Cui, Lang Deng, Chen Wang
Summary: Wireless sensor networks are self-organizing monitoring networks consisting of randomly deployed micro sensor nodes to collect physical information for tasks such as intelligent perception and efficient control. However, the energy limitations of the nodes greatly affect network performance. In this study, a multi-hop routing protocol based on game theory and coverage optimization is proposed to prolong network lifetime.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Li Cao, Yinggao Yue, Yong Cai, Yong Zhang
Summary: An improved Social Spider Optimization (SSO) algorithm is proposed for optimizing the deployment of sensor nodes in heterogeneous wireless sensor networks, aiming to improve network coverage and reduce network costs. By enhancing convergence speed and search ability through improving search and matching radius, the algorithm ultimately achieves an optimized solution.
Article
Computer Science, Hardware & Architecture
Sheng-Chuan Wang, Han C. W. Hsiao, Chun-Cheng Lin, Hui-Hsin Chin
Summary: This paper investigates the multi-objective problem of deploying wireless sensor networks, considering various coverage situations and introducing the concept of cooperative sensing coverage. The deployment of sensor nodes aims to maximize the coverage of target points and minimize the distances between target points and sensor nodes.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Dieyan Liang, Hong Shen, Lin Chen
Summary: This study formulates and analyzes a generic coverage optimization problem in mobile sensor networks, proving its NP-hardness and devising four heuristic or approximate algorithms to solve it. Experimental results validate the effectiveness of these algorithms in various parameter settings.
Article
Computer Science, Information Systems
Kale Navnath Dattatraya, K. Raghava Rao
Summary: This paper discusses the importance of optimal cluster head selection in wireless sensor networks, proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), and compares it with other traditional methods, demonstrating the superiority of the algorithm.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Telecommunications
Mahnaz Toloueiashtian, Mehdi Golsorkhtabaramiri, Seyed Yaser Bozorgi Rad
Summary: This paper proposes an improved meta-heuristic algorithm based on whale optimization algorithm (WOA) to solve the network coverage problem in wireless sensor networks. The algorithm outperforms other compared algorithms in increasing the lifespan of the coverage area, as demonstrated by experimental results.
TELECOMMUNICATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hakim Q. A. Abdulrab, Fawnizu Azmadi Hussin, Azrina Abd Aziz, Azlan Awang, Idris Ismail, Mohd Shakir M. D. Saat, Hussein Shutari
Summary: The article discusses the optimization of router placement in wireless mesh networks and introduces a method using Harris Hawk's Optimization algorithm to improve connectivity and coverage. Through comparison with other algorithms and performance validation, the results show that the proposed method has advantages in terms of network coverage and connectivity.
Article
Engineering, Multidisciplinary
Xinmiao Lu, Yanwen Su, Qiong Wu, Yuhan Wei, Jiaxu Wang
Summary: This paper explores gap fixing in heterogenous WSNs and proposes an improved method based on fixing priority, using Voronoi polygons to determine and prioritize fixing points. The method effectively enhances the coverage of the entire network by removing nodes with overlapping sensing ranges.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Information Systems
Xiaojian Zhu, Mengchu Zhou, Abdullah Abusorrah
Summary: This article investigates the design and deployment of a rechargeable CSN to achieve full-view coverage for monitoring and recognizing objects at target points. The problem is formulated as an integer linear program and solved using a greedy heuristic and a differential evolution algorithm. Extensive simulation results show that the latter achieves higher success rate and solution quality but requires more time compared to the former.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Vrajesh Kumar Chawra, Govind P. Gupta
Summary: This paper proposes an optimized coverage-aware target node selection and trajectory planning scheme for efficient data collection in AUV-based UASN. The scheme applies BSO technique for node selection and trajectory planning, and uses load balanced cluster-region partitioning. Performance evaluation and comparison show that the proposed scheme performs well and has great potential.
EVOLUTIONARY INTELLIGENCE
(2023)
Article
Telecommunications
Amanpreet Kaur, Govind P. Gupta, Sangeeta Mittal
Summary: This paper investigates different versions of DV-Hop-based algorithms and conducts the latest performance comparison in terms of accuracy, localization error, optimal number of anchors used, and communication overhead. It introduces the working processes of various versions of DV-Hop and compares them, while also discussing open research issues for future work.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Telecommunications
Vrajesh Kumar ChawranAff, Govind P. Gupta
Summary: This paper proposes an improved Memetic Algorithm based energy efficient wakeup scheduling scheme, considering energy consumption, coverage, connectivity, and optimal length of wakeup schedule list as constraints. Extensive simulation experiments show that the proposed scheme outperforms two latest existing schemes in terms of coverage ratio, optimal number of active sensor nodes, and network lifetime.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Vrajesh Kumar Chawra, Govind P. Gupta
Summary: This study investigates the issues of sensing data acquisition from large-scale hazardous environments using UAVs-assisted WSNs. The proposed scheme overcomes the problems of low scalability, high latency, low throughput, and low service time of the deployed network by utilizing a clustered WSN architecture and a hybrid meta-heuristic based optimal path planning algorithm. The results show significant performance improvement compared to existing state-of-the-art schemes.
INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH
(2022)
Article
Automation & Control Systems
Randhir Kumar, Prabhat Kumar, Rakesh Tripathi, Govind P. Gupta, A. K. M. Najmul Islam, Mohammad Shorfuzzaman
Summary: The industrial healthcare system allows advanced real-time patient monitoring through data sharing among intelligent wearable devices and sensors. However, this connectivity poses security and privacy vulnerabilities. To address this, PBDL integrates permissioned blockchain, smart contracts, and deep learning techniques to create a secure and efficient data sharing framework.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Civil
Randhir Kumar, Prabhat Kumar, Rakesh Tripathi, Govind P. Gupta, Neeraj Kumar, Mohammad Mehedi Hassan
Summary: This paper presents a secure framework based on blockchain and deep learning modules to provide privacy and security in C-ITS infrastructure. The framework provides two levels of security and privacy, with a blockchain module for secure data transmission and a deep-learning module for encoding data to prevent inference attacks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Randhir Kumar, Prabhat Kumar, Rakesh Tripathi, Govind P. Gupta, Sahil Garg, Mohammad Mehedi Hassan
Summary: This article proposes a blockchain and deep-learning-based integrated framework, BDTwin, to enhance security and privacy in CT-driven V2X applications. It designs a blockchain scheme using smart contracts and zero knowledge proofs for secure communication among vehicles, and utilizes a deep-learning scheme for automatic feature extraction and attack detection.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Hardware & Architecture
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar
Summary: This research proposes a novel three-phase Deep Malware Detection (DMD) framework based on the fusion of Discrete Wavelet Transform (DWT) and Generative Adversarial Network (GAN) for IoT-based Smart Agriculture (IoT-SA). The framework achieves multiresolution analysis through DWT and employs a lightweight Convolutional Neural Network (CNN) for in-depth analysis of malware. Experimental results demonstrate that the proposed framework achieves 99.99% accuracy on both benchmark datasets and outperforms existing models.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Engineering, Civil
Randhir Kumar, Prabhat Kumar, Rakesh Tripathi, Govind P. Gupta, Neeraj Kumar
Summary: With the development of Internet of Vehicles (IoV), integrating IoT and manual vehicles in ITS brings challenges such as security vulnerabilities and data privacy. To address these challenges, a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV) is proposed, which integrates blockchain and deep learning techniques to securely transmit data and detect intrusion. Experimental results show that P2SF-IoV outperforms other privacy-preserving intrusion detection strategies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Prabhat Kumar, Randhir Kumar, Govind P. Gupta, Rakesh Tripathi, Alireza Jolfaei, A. K. M. Najmul Islam
Summary: This article presents a method for secure data transmission in IoT-enabled healthcare system using blockchain and deep learning. It ensures data integrity and secure transmission through a novel scalable blockchain architecture with Zero Knowledge Proof mechanism, and addresses storage cost and security issues by integrating off-chain storage and smart contracts. Experimental results demonstrate that the proposed method outperforms existing techniques in both non-blockchain and blockchain settings, achieving accuracy close to 99%.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Green & Sustainable Science & Technology
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar, Prabhat Kumar
Summary: In this paper, an optimized and efficient ensemble learning-based Android malware detection framework is proposed to address the challenges of high false-positive rate and low detection rate of new malware variants. The framework utilizes statistical feature engineering and meta-heuristic feature selection techniques to improve the accuracy and performance of malware detection. Experimental results demonstrate the promising performance of the proposed framework, achieving high classification accuracy and statistical significance when compared to existing methods.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Gourab Dhabal, Govind Gupta
Summary: In this paper, a novel android malware detection framework using hybrid deep learning techniques is proposed. The framework includes feature selection and optimization, and the performance is compared with other state-of-the-art techniques, showing better results.
SOFT COMPUTING FOR SECURITY APPLICATIONS, ICSCS 2022
(2023)
Article
Engineering, Civil
Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi, Sahil Garg, Mohammad Mehedi Hassan
Summary: The recent growth of IoT technologies in the maritime industry has digitalized Maritime Transportation Systems (MTS), but also introduced cybersecurity threats. Cyber Threat Intelligence (CTI) is an effective security strategy, but existing solutions have low detection rates and high false alarm rates. To address these challenges, an automated framework called DLTIF has been proposed, which can accurately identify threat types.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Anil Kumar Mandle, Satya Prakash Sahu, Govind P. Gupta
Summary: This paper proposes a deep learning model based on VGG-19 convolutional neural networks for the classification of brain tumors. The model achieves high accuracy and recall rates according to the experiments conducted on a publicly available dataset.
INTERNATIONAL JOURNAL OF SOFTWARE SCIENCE AND COMPUTATIONAL INTELLIGENCE-IJSSCI
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Sakshi Bhagwat, Govind P. Gupta
Summary: This paper proposes a novel malware framework for detecting Android malware using dynamic features. The framework utilizes genetic algorithm, gravitational search algorithm, and correlation to select optimized features, and uses adaptive boosting and extreme gradient boosting classifiers for detection. The proposed framework achieves an accuracy of 95.3% according to the performance analysis using the CICMalDroid-2020 dataset.
ADVANCES IN COMPUTING AND DATA SCIENCES (ICACDS 2022), PT I
(2022)
