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Enabling Industrial Internet of Things by Leveraging Distributed Edge-to-cloud computing: Challenges and Opportunities

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IEEE ACCESS 2024年 12卷 127294-127308页

作者： Jamil, Mohammad Newaj Schelen, Olov Monrat, Ahmed Afif Andersson, Karl Lulea Univ Technol Dept Comp Sci Elect & Space Engn S-93187 Lulea Sweden

The Industrial Internet of Things (IIoT) promises automation, efficiency, and data-driven decision-making by real-time data collection and analysis. However, traditional IIoT architectures are cloud-centric and, therefore, struggle to handle large volumes of data, edge bandwidth constraints, and data confidentiality. Distributed edge-to-cloud computing emerges as a potential solution, also paving the ground for edge-to-cloud data analytics and distributed Artificial Intelligence (AI) to obtain insights for decision-making and predictive maintenance. Despite the potential, however, there is a lack of comprehensive studies identifying key requirements for distributed edge-to-cloud IIoT and analyzing to what extent emerging IoT platforms meet those requirements. The scope of this article is to survey existing literature to identify key requirements in IIoT from the perspective of distributed edge-to-cloud computing. We provide a comparative analysis of three prominent IoT platforms, namely ThingsBoard, Eclipse Ditto, and Microsoft Azure IoT, and assess how these platforms meet the key IIoT requirements. Finally, we identify open challenges and potential research opportunities based on the insights gained from the analysis of the three IoT platforms, thereby setting the stage for future work.

关键词： Industrial Internet of Things cloud computing Scalability Data analysis Real-time systems Distributed databases Big Data Industrial Internet of Things (IIoT) edge-to-cloud computing data analytics IoT platforms

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MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in cloud computing

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Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第3期31卷 1037-1060页

作者： Aghimien, Douglas Aigbavboa, Clinton Ohis Chan, Daniel W. M. Aghimien, Emmanuel Imuetinyan Univ Johannesburg Fac Engn & Built Environm Dept Civil Engn Technol Johannesburg South Africa Univ Johannesburg Fac Engn & Built Environm Cidb Ctr Excellence Johannesburg South Africa Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Tong Hong Kong Peoples R China Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Purpose This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC. Design/methodology/approach This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC. Findings The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations. Originality/value The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

关键词： Artificial neural network cloud computing Machine learning Multiple linear regression Structural equation modelling Technology-organisation-environment

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Hierarchal Bilateral Access Control With Constant Size Ciphertexts for Mobile cloud computing

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IEEE TRANSACTIONS ON cloud computing 2024年第2期12卷 659-670页

作者： Wu, Axin Zhang, Yinghui Zhu, Jianhao Zhao, Qiuxia Zhang, Yu State Key Lab Cryptol Beijing 100878 Peoples R China Xian Univ Posts & Telecommun Natl Engn Res Ctr Secured Wireless NERCSW Sch Cyberspace Secur Xian 710121 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Guangdong Peoples R China Qinghai Normal Univ Sch Comp Sci Xining 810001 Peoples R China Yuncheng Univ Maths & Informat Technol Sch Yuncheng 044000 Peoples R China

Mobile cloud computing (MCC) integrates the advantages of mobile networks and cloud computing, enabling users to enjoy personalized services without constraints and restrictions of time and place. While this brings convenience, it also comes with risks such as privacy breaches and unauthorized access to outsourced data. Bilateral access control is a promising technique for addressing these issues. However, the current bilateral access control schemes cannot solve problems such as single point failure. To further enhance and enrich the existing schemes, we propose hierarchical bilateral access control. In the proposed scheme, the permission of generating encryption keys and decryption keys can be delegated to its child nodes, which alleviates the computation and communication overheads of the parent nodes and weaken the potential risks of single-point failure. Additionally, the ciphertext size remains constant, reducing the costs of transmitting and storing ciphertext and relieving resource limitations on devices. We then prove the privacy and authenticity of the scheme in the random oracle model. Finally, the comprehensive performance comparison and analysis demonstrate the efficiency of the proposed scheme.

关键词： Access control cloud computing Privacy Encryption Computational modeling Receivers Costs Bilateral access control constant size ciphertexts hierarchal identity structure mobile cloud computing

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Binarized Spiking Neural Network with blockchain based intrusion detection framework for enhancing privacy and security in cloud computing environment

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APPLIED SOFT computing 2024年 154卷

作者： Sarveshwaran, Velliangiri Pandiaraj, Shanthini Bindu, Garikapati Ganesan, Vithya Swamidason, Iwin Thanakumar Joseph SRM Inst Sci & Technol Dept Comp Intelligence Kattankulathur Campus Chennai Tamil Nadu India Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India

cloud computing (CC) is prone to attacks, which upsurges complication and erudition. By this, its origins provocative implications can enterprise data veracity, concealment, and accessibility. To overwhelm these issues, a Binarized Spiking Neural Network with block chain based deputized proof of stake consensus (DPoS) algorithm fostered intrusion detection scheme is proposed in this manuscript to enhance the privacy and the security on the cloud computing Environment (EP-DPoSBC-ES-BS4NN-IDS-CC). The data is amassed from NSL-KDD benchmark dataset. The first -level privacy process is carried with block chain based deputized proof of stake consensus (DPoS) algorithm. The secondary level privacy process is carried out by utilizing the pre-processing and the feature selection process. For, pre-processing, proposed Basic interlude gradient filtering (BIGF) are utilized to eradicate the unsolicited content and filtering pertinent data. The pre-processing outcome is supplied to the feature selection phase. Here, the ideal features are taken with the help of Weightiness espoused feature assortment approaches (WEFA). The data is classified as normal or abnormal based on Binarized Spiking Neural Network. Subsequently, the proposed EP-DPoSBC-ES-BS4NN-IDS-CC is examined under some performance metrics. The proposed technique attains 12.94 %, 17.68 %, 17.99 % and 13.96 % improved accuracy;59.9 %, 50 %, 31.45 % and 48.17 % lower Computation Time and 3.19 %, 0.83 %, 2.1 % and 5.43 % higher AUC than the existing methods. Customers and cloud service providers may find this framework useful as a decision -support tool in helping them move their data in a safe, timely, and reliable manner. In future work, a prototype of the approach will develop in real -world scenario, capably inside a tight network of connected computers. It allows evaluate effectively in real -world utility.

关键词： Deputized proof of stake consensus (DPoS) algorithm cloud computing Basic interlude gradient filtering (BIGF) Weightiness espoused feature assortment approaches (WEFA) Binarized Spiking Neural Network

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A novel strategy for deterministic workflow scheduling with load balancing using modified min-min heuristic in cloud computing environment

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 6985-7006页

作者： Choudhary, Anjali Rajak, Ranjit Dr Harisingh Gour Cent Univ Dept Comp Sci & Applicat Sagar India

cloud computing Environment (CCE) has gained considerable attention in recent years because of scalability, flexibility, and cost-effectiveness. Workflow scheduling, a critical aspect of CCE, involves assigning tasks of a workflow to suitable resources to optimize various performance metrics. Load balancing plays an important role in achieving efficient resource utilization and reducing execution time in workflow scheduling. There are many scheduling algorithms are developed and Min-Min is out of them that uses independent tasks. However, the original Min-Min heuristic does not consider the load distribution among resources, which can lead to imbalanced resource utilization and increased execution *** address this limitation, we introduce a modified Min-Min heuristic that incorporates load-balancing principles. Taking into consideration both task completion time and resource load, the method aims to achieve optimal load distribution and minimize the overall execution time of the *** evaluate the effectiveness of the proposed load-balancing method, extensive simulations are performed using benchmark workflow datasets such as randomly generated workflows and Montage workflows. The results show that the modified Min-Min heuristic outperforms as compared to heuristics HEFT and PETS in terms of load balancing, makespan, speedup, efficiency,and resource utilization. The proposed method achieves more balanced resource allocation, reduces the completion time of the workflow, and improves overall system performance. The present study contributes to the area of workflow scheduling in CCE by presenting a load-balancing method that enhances the efficiency of resource allocation. The findings emphasize the importance of considering load-balancing principles in task scheduling to optimize performance in cloud computing environments. The proposed method can serve as a valuable tool for practitioners and researchers involved in workflow scheduling in CCE, offering impro

关键词： Workflow graph DAG Makespan Load balancing Virtual machine cloud computing

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Exponential Squirrel Search Algorithm-Based Deep Classifier for Intrusion Detection in cloud computing with Big Data Assisted Spark Framework

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CYBERNETICS AND SYSTEMS 2024年第2期55卷 331-350页

作者： Polepally, Vijayakumar Jagannadha Rao, D. B. Kalpana, Parsi Nagendra Prabhu, S. Kakatiya Inst Technol & Sci Dept Comp Sci & Engn Warangal Telangana India Shri Jagdishprasad Jhabarmal Tibrewala Univ Dept Comp Sci & Engn Jhunjhunu Rajasthan India St Francis Coll Women Dept Comp Sci Hyderabad Telangana India Malla Reddy Coll Engn & Technol Dept Comp Sci & Engn Dhulapally Telangana India Kakatiya Inst Technol & Sci Dept Comp Sci & Engn Warangal 506015 Telangana India Malla Reddy Univ Hyderabad Dept Comp Sci & Engn Data Sci Hyderabad 500043 Telangana India Vasavi Coll Engn Dept Comp Sci & Engn Hyderabad 500031 Telangana India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Chennai 603203 Tamilanadu India

Intrusion detection systems (IDS) are extensively employed for detecting suspicious behaviors in hosts. The ability of distributed IDS solutions makes it viable to combine and handle various kinds of sensors and generate alerts to different hosts positioned in distributed platforms. However, to offer secure and feasible services in a cloud platform is an imperative issue due to the impacts of attacks. This paper devises a novel IDS framework using cloud data to counter the influence of attacks. Here, the spark architecture is employed for discovering the intrusions. The pre-processing is applied to the input data for removing artifacts and noise considering input data. Thereafter, the feature extraction and feature fusion are performed in slave nodes. The feature fusion is carried out with the proposed Exponential Squirrel Search Algorithm (ExpSSA) algorithm. The fused features are considered in a deep-stacked autoencoder (Deep SAE) for performing effective intrusion detection. The proposed ExpSSA is adapted to train Deep SAE for tuning optimum weights. The exponential weighted moving average (EWMA) and squirrel search algorithm (SSA) are combined to create the proposed ExpSSA. The proposed ExpSSA-based Deep SAE offered improved performance compared to other techniques with the highest accuracy, detection rate of 0.846, and minimal FPR.

关键词： Big data cloud computing intrusion detection spark architecture deep stacked autoencoder

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Exploring Social Credit for Fairness in cloud computing Networks

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IETE TECHNICAL REVIEW 2024年第5期41卷 578-591页

作者： Saxena, Navrati Huang, Kaiyi Roy, Abhishek San Jose State Univ Dept Comp Sci San Jose CA 95112 USA MediaTek Adv Commun Technol San Jose CA 95134 USA

cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to reap the benefits of cloud computing's advantages at an even lower cost. However, this new paradigm comes with a slew of challenges: (i) security, when operating with the devices of strangers and (ii) fairness when not operating in a market-based system. This paper aims to address these two issues by proposing an algorithm based on social credits for a P2P cloud system that uses a social network to establish its security measures. We implement our new Social Credit algorithm along with two other task-migration-based load-balancing algorithms adapted for a P2P social cloud in "cloudsim Plus", and the relative gains are shown in terms of fairness and other related metrics.

关键词： cloud computing cloud sim Fairness P2P systems Random graphs Social credit

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Energy Efficient Resource Allocation and Latency Reduction in Mobile cloud computing Environments

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期136卷 657-687页

作者： Rathika, J. Soranamageswari, M. Coimbatore Inst Technol Dept Comp Data Sci Coimbatore 641014 Tamil Nadu India Govt Arts Coll Dept Informat Technol Coimbatore 641018 Tamil Nadu India

Each mobile user in a typical multi-user mobile cloud computing (MCC) system has a number of independent tasks to do. In the modern world of finite resources and growing demands, it is critical to make the best use of multiple available resources in order to optimize their edge server placements. In this article, we explore the best ways to deploy edge servers in a cost-effective and efficient manner. The issue of reducing the quantity of edge servers while maintaining the need for access delay in MCC setting has been addressed. The selection of the fewer computational access points co-located with an edge server to ensure optimal service for all users is one of the primary issues. The other is determining how to appropriately allocate offloading tasks to edge servers. We partition the mobile networks into clusters in response to these difficulties, and the cluster heads are co-located with edge servers. We redefine the term "dominating set" and convert the problem under consideration into the dual-modeled game theory (DGT) equivalent of the minimal dominating set problem. We provide new optimizer-based techniques to find the best solutions depending on various scenarios. Resource sharing and clustering can be done using an adapted Gaussian distribution function with the whale optimization algorithm (AGDF-WOA). The offloading choices can be made by each user using AGDF-WOA-DGT progress. Its effective reduction of edge servers and load balance that leads to lower energy and cost make it a desirable option for MCC through simulations.

关键词： cloud computing Enhanced optimizer Dual game theory Load balancer Latency

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Enhancing the Swimmer Movement Techniques Using cloud computing and Artificial Intelligence

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MOBILE NETWORKS & APPLICATIONS 2024年第2期29卷 433-447页

作者： Liu, Xurui Zhang, Guobao Beijing Sport Univ China Swimming Coll Beijing Peoples R China

Nowadays, miniature sensors can communicate with intelligent tools and pervasive computing devices to analyze and assess sports and physical activities. These sensors allow us to collect data from various physical activities using the underlying framework from anywhere, at any time, and in any location. However, these devices generate a large volume of data. Thus, having as much data as possible to assess a sports person's physical activity is critical. Wearable devices with tiny sensors, edge and cloud computing, and artificial intelligence are the pillars that are capable to change the current level of analysing physical activities in sports. Based on these features, this paper aims to combine these three pillars of physical activities to enhance the athlete's profile by predicting their physique, and recommend special training. For this purpose, a novel framework is proposed that allows us to generate a dataset from the realistic ecological conditions. To ensure the efficiency of this work, we have conducted a comprehensive literature on sports and physical activities. We underlined the limitations of the data collection, sensors, and processing techniques from literature. We hypothesize that the acquisition of data, continuous measurement, and analysis of different processes will end up in a more reliable model with the help of edge and cloud computing devices that allow the data to stream without restriction. Personalized training and profile-type approaches are used for swimmer athletes in this paper. The experimental results show that the suggested integrated method provides significant data to coaches and players, enabling focused training, performance optimisation, and improved athlete healthcare.

关键词： Edge computation cloud computing Artificial Intelligence K-Mean Algorithm Convolutional Neural Network (CNN)

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