2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)最新文献

{"title":"Welcome Message from the Chairs","authors":"","doi":"10.1109/coins54846.2022.9854977","DOIUrl":"https://doi.org/10.1109/coins54846.2022.9854977","url":null,"abstract":": The talk will be why, what and how of 6G and present some potential solutions that address these challenges. These challenges relate to future wireless, ubiquitous coverage and Core network that underpin concept of Future Network of Networks. Prof. Ari Pouttu has scientific and engineering experience as a researcher, project manager and research manager in various domains of ICT development. The projects under his command have resulted in waveforms and system designs for military radio communication, radar systems, embedded device networks, future wireless radio communications including cellular systems, cognitive networks and navigation applications. He has published more than 70 conference or journal papers in the field of wireless communications and he holds two patents. He is the principal investigator of 5G test network (5GTN) experimental research, and vice-director of the national 6G Flagship Programme as well as 6GESS programme targeting 6G solutions including wireless solutions for business verticals such as energy, industry, health and automotive. Abstract: The architectures of mobile networks have seen an unprecedented techno-economic transformation, fusing the telecommunications world within the cloud world, adding the spices of Software Engineering to the overall system design, and ultimately yielding the concept of Telco Cloud. This has brought significant benefits in terms of reducing expenditure and operational costs, flexibility in deployment, and faster time to market. The key enablers are network function virtualization, software-defined networking, and edge/cloud computing. Artificial intelligence is also kicking in this arena. When all these technologies are well integrated, the creation and life-cycle management of fully programmable, flexible, service-tailored, and automated end-to-end network slices/services become possible. This will support diverse 5G and beyond 5G services, spanning from tactile IoT to pervasive robotics and immersive services. In this talk 6G Flagship introduces an unprecedented and disruptive vision for 6G that shifts the perception of future mobile networks from the old-fashioned concept of “network of networks” towards a new vision of “service of services.” The talk then introduces the functional model of the envisioned system architecture, along with its components. It then provides a high-level description of the logical architecture.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133653741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfigurability For Industry 4.0 Middleware Software Architectures","authors":"Sune Chung Jepsen, T. Worm, Eunsuk Kang","doi":"10.1109/COINS54846.2022.9854981","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854981","url":null,"abstract":"Industry 4.0 (I4.0) is about creating an intelligent network of machines to handle readjustment in the production system and increase productivity effectiveness. Understanding reconfigurability of the Cyber-Physical System (CPS) becomes important towards successful architecting advanced production systems. This paper proposes a definition of reconfigurability for I4.0 middleware software architecture. The contribution of the paper is a definition of reconfigurability that enables reasoning about reconfigurable I4.0 middleware software architectures. We demonstrate how the reconfigurability definition supports architectural reasoning about reconfigurable middleware through a case study on actual running middleware in the university I4.0 laboratory.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132945651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Automation Python-based Application: The performance of a Multi-Layer Cloud Based Solution","authors":"Monerah Al-Mekhlal, A. Alyahya, A. Aldhamin, Azmath Khan","doi":"10.1109/COINS54846.2022.9854953","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854953","url":null,"abstract":"In recent years, we have witnessed a growing interest in adopting network automation solutions to maximize network availability in increasingly hybrid data centers networks. Amongst all the solution design characteristics, reliability, performance, scalability, and minimal resource overhead, are essential features that influence the adoption choice. This interest, and the associated challenges, have led to a rapid development of several network automation solutions that are available in the market, mostly offered by the original equipment manufacturers (OEMs). In this paper, we explore the ability to develop lightweight network automation Python programs to automate data center network tasks that can run on private cloud environments. We compare the programs performance running on the cloud implementation with a conventional deployment on a standalone physical server. Further, we evaluate the execution time performance and the recovery of the Python programs running different network tasks. Our measurements show that the reading performance for the programs running on the cloud achieves a steady performance across multiple runs with relative improvements compared to the standalone servers. Further, the average performance for the writing tasks produced comparable results for both scenarios. Our analysis shows that the worst writing performance when running on the cloud can still achieve a 22% better performance. Finally, our experimental results show effective utilization of the cloud built-in high availability features to provide necessary levels of recovery without losing the running state, and with acceptable resource overhead.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"50 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116252684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric Vehicle Battery Management using Digital Twin","authors":"Naga Durga Krishna Mohan Eaty, P. Bagade","doi":"10.1109/COINS54846.2022.9854955","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854955","url":null,"abstract":"In the transportation business, battery-powered electric vehicles (EVs) are regarded the immediate solution to internal combustion engines in light of the growth in environmental pollution. While expanding the use of electric vehicles, battery-related difficulties such as range anxiety, safety concerns, cost, and the availability of charging stations are important concerns. A precise online estimate of the battery’s State of Health (SoH) has the ability to resolve some of these issues. However, computing the SoH on EVs is computationally intensive, necessitating expensive onboard integrated electronics and rapidly draining the EV battery. In addition, the SoH estimating algorithms currently available do not utilise incremental battery usage data. This research presents a digital twin of the EV battery as a solution to the difficulty of onboard computation for incremental SoH prediction. It enables intensive computing and analytics to be performed in the cloud instead of a vehicle’s battery management system (BMS). It calculates the SoH of the battery using an incremental learning method with a mean square error of 0.023.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125776488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overview of Closed-Loop Control Systems and Artificial Intelligence Utilization in Greenhouse Farming","authors":"Dominik Walczuch, Tim Nitzsche, Tim Seidel, Julius Schöning","doi":"10.1109/COINS54846.2022.9854938","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854938","url":null,"abstract":"The increased demand for agricultural land and the more frequent occurrence of extreme weather conditions lead to increased greenhouses usage in agriculture. Greenhouses offer an efficient alternative to traditional agriculture as all environmental parameters are controlled, leading to a higher yield per land use. In older greenhouses, the different environmental factors are set manually, resulting in unfavorable climate conditions due to over- and undershoots of the factors. Optimizing environmental conditions, closed-loop control systems (CLCS) are used to reduce the susceptibility to over- and undershoots as well as to disturbance variables. The greenhouse actuators respond to various input values bringing changes to the environmental parameters. For controlling the actuators, artificial intelligence (AI) offers the potential to control the environmental parameters more accurately than a simple CLCS. With the help of predicting the influence of actuators regarding the greenhouse climate, AI-based climate systems might outperform CLCS systems and human experts. This paper provides an overview of the different architectures in which AI is used for controlling complex systems and discusses its potential for greenhouses. The results will show that AI offers the possibility for yield increase and resources saving.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126119309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Flexible Distributed Building Simulator for Federated Reinforcement Learning","authors":"Shugo Fujimura, Koki Fujita, Yuwei Sun, H. Esaki, H. Ochiai","doi":"10.1109/COINS54846.2022.9855000","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9855000","url":null,"abstract":"Recently, researches on building control using reinforcement learning are underway to realize a sustainable society. Such building control methods typically use data acquired from sensors and other sources in the building, but such data may contain sensitive information such as human flow, and collecting these in the Cloud for model training can be problematic in terms of privacy and security. With federated learning, it is possible to share and train models for large numbers of buildings while protecting such data. However, it is almost impossible to conduct a model training experiment with federated learning while actually running real buildings. There are several studies of building simulators, but none of them are designed for simultaneous operation of large numbers of buildings, as is the case with federated learning. Therefore, we propose a distributed building simulator for federated reinforcement learning. This simulator is both scalable and flexible, allowing users to conduct experiments scaled to multiple machines without writing complex code for distributed processing. In this paper, we describe the design and implementation of this simulator and demonstrate that it can perform experiments with a large number of buildings, at least 1024 buildings, in a scalable manner.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129028218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extortion of a Staking Pool in a Proof-of-Stake Consensus Mechanism","authors":"Alpesh Bhudia, A. Cartwright, E. Cartwright, J. Hernandez-Castro, D. Hurley-Smith","doi":"10.1109/COINS54846.2022.9854946","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854946","url":null,"abstract":"Cryptocurrencies to date, most notably Bitcoin, have primarily relied on a proof-of-work system to validate and process transactions on the blockchain. Proof-of-work systems have, however, several limitations, such as enormous energy demands, and so are likely to be replaced by proof-of-stake systems. These systems use a mechanism that does not rely on mining power but the amount of stake owned by a node, allowing randomly selected validators to create blocks and verify blocks created by other validators. Proof-of-stake systems naturally result in staking pools where in a third party organisation operates validators on behalf of investors who have staked in the currency. Given they have oversight for a large amount of staked currency, staking pools are a prime target for malicious actors. In this paper we explore the economic implications of an attack on a staking pool. We pay particular attention to how the staking pool and clients could resolve an extortion attack by a malicious actor who has accessed relevant signing keys.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129483886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disruption of Connectivity Graphs in Uncertain Multi-Agent Systems","authors":"Brian Reily, Caden Coniff, J. Rogers, Christopher M. Reardon","doi":"10.1109/COINS54846.2022.9854963","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854963","url":null,"abstract":"Multi-agent systems have become ever-present in modern society, whether as multi-robot teams, sensor networks, or social networks. While ensuring the connectivity and robustness of multi-agent systems has seen extensive research, the problem of disrupting the connectivity of a multi-agent system has remained largely unaddressed. Yet, this capability can be essential in certain applications, such as responding to a hostile multi-robot system or controlling the flow of disinformation in a social network. In this paper we propose a novel method to disrupt the connectivity of a multi-agent system with uncertain relationships. We represent a multi-agent system as a graph, with edges denoting the probability of communication between agents. We introduce the problem of identifying a subgraph which minimizes the overall connectivity of the multi-agent system. We formulate a novel approach to identify optimal sets of vertices to remove by approximating a minimization of the algebraic connectivity, given constraints on the number of vertices to disconnect. We show through evaluation on simulated multi-agent systems that our approach is able to effectively disrupt the connectivity of a multi-agent system, and discuss its comparative complexity to existing approaches while attaining these superior results.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130698213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Timeseries biomarkers clustering for Alzheimer’s Disease progression","authors":"Laura Hernández-Lorenzo, Inigo Sanz Ilundain, J. L. Ayala","doi":"10.1109/COINS54846.2022.9855010","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9855010","url":null,"abstract":"Neurodegenerative diseases are complex and highly time-dependent diseases. Among them, the most common is Alzheimer’s Disease (AD), in which the patient goes through a series of symptomatic stages before receiving the diagnosis of dementia caused by AD. Due to its temporal characteristics, it is necessary to study the biomarkers associated with the AD from a time series point of view. In this work, we have applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort the Dynamic Time Warping (DTW) technique combined with hierarchical clustering. We extensively applied this technique to several datasets: unidimensional (only one biomarker) and multidimensional (two or more biomarkers) datasets. The results obtained with both dataset types corresponded very clearly with the expected clinical outcomes. The work presented here raises the enormous potential of time series clustering to discover new knowledge in time-dependent diseases such as AD.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130776502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Valuing the Impact of Machine Learning Faults to Cyber-Physical Production Systems","authors":"Tyler Cody, Stephen Adams, P. Beling, Laura Freeman","doi":"10.1109/COINS54846.2022.9854969","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854969","url":null,"abstract":"Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130823215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}