scholarly journals Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1685
Author(s):  
Aida Akbarzadeh ◽  
Sokratis Katsikas
Keyword(s):  
Complex System ◽  
Large Scale ◽  
Graphical Model ◽  
Cyber Physical Systems ◽  
Critical Infrastructures ◽  
Valuable Insight ◽  
Physical Systems ◽  
Dependency Structure ◽  
Tightly Coupled ◽  
Insight Into

Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.

scholarly journals The history of settlement and agrarian land use in a boreal forest in Värmland, Sweden, new evidence from pollen analysis

2021 ◽  
Author(s):  
Sigrún Dögg Eddudóttir ◽  
Eva Svensson ◽  
Stefan Nilsson ◽  
Anneli Ekblom ◽  
Karl-Johan Lindholm ◽  
...  
Keyword(s):  
Land Use ◽  
Pollen Analysis ◽  
Large Scale ◽  
Pollen Grains ◽  
Ice Age ◽  
Valuable Insight ◽  
Archaeological Survey ◽  
History Of ◽  
New Evidence ◽  
Insight Into

AbstractShielings are the historically known form of transhumance in Scandinavia, where livestock were moved from the farmstead to sites in the outlands for summer grazing. Pollen analysis has provided a valuable insight into the history of shielings. This paper presents a vegetation reconstruction and archaeological survey from the shieling Kårebolssätern in northern Värmland, western Sweden, a renovated shieling that is still operating today. The first evidence of human activities in the area near Kårebolssätern are Hordeum- and Cannabis-type pollen grains occurring from ca. 100 bc. Further signs of human impact are charcoal and sporadic occurrences of apophyte pollen from ca. ad 250 and pollen indicating opening of the canopy ca. ad 570, probably a result of modification of the forest for grazing. A decrease in land use is seen between ad 1000 and 1250, possibly in response to a shift in emphasis towards large scale commodity production in the outlands. Emphasis on bloomery iron production and pitfall hunting may have caused a shift from agrarian shieling activity. The clearest changes in the pollen assemblage indicating grazing and cultivation occur from the mid-thirteenth century, coinciding with wetter climate at the beginning of the Little Ice Age. The earliest occurrences of anthropochores in the record predate those of other shieling sites in Sweden. The pollen analysis reveals evidence of land use that predates the results of the archaeological survey. The study highlights how pollen analysis can reveal vegetation changes where early archaeological remains are obscure.

scholarly journals Estimating extreme river discharges in Europe through a Bayesian Network

2016 ◽  
Author(s):  
Dominik Paprotny ◽  
Oswaldo Morales Nápoles
Keyword(s):  
Large Scale ◽  
Graphical Model ◽  
Flood Hazard ◽  
Physical Models ◽  
Method Performance ◽  
Discharge Data ◽  
Dependency Structure ◽  
Spatial Coverage ◽  
Geographical Characteristics ◽  
Better Than

Abstract. Large-scale hydrological modelling of flood hazard requires adequate extreme discharge data. Models based on physics are applied alongside those utilizing only statistical analysis. The former requires enormous computation power, while the latter are most limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian Networks (BN), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables describing the geographical characteristics of their catchments. Data on annual maxima of daily discharges from more than 1800 river gauge stations were collected, together with information on terrain, land use and climate of catchments that drain to those locations. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe, and better than a comparable global statistical method. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and is not affected by a split-sample validation. The BN was applied to a large domain covering all sizes of rivers in the continent, both for present and future climate, showing large variation in influence of climate change on river discharges, as well as large differences between emission scenarios. The method could be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

Data quality challenges in large-scale cyber-physical systems: A systematic review

2021 ◽  
pp. 101951
Author(s):  
Ahmed Abdulhasan Alwan ◽  
Mihaela Anca Ciupala ◽  
Allan J. Brimicombe ◽  
Seyed Ali Ghorashi ◽  
Andres Baravalle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Quality ◽  
Large Scale ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Methods for Large-Scale Time-Triggered Network Scheduling

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 738 ◽  
Author(s):  
Francisco Pozo ◽  
Guillermo Rodriguez-Navas ◽  
Hans Hansson
Keyword(s):  
Wireless Networks ◽  
Large Scale ◽  
Synthesis Method ◽  
Cyber Physical Systems ◽  
Network Scheduling ◽  
Synthesis Time ◽  
Physical Systems ◽  
Large Size ◽  
Np Complete ◽  
Communication Schedule

Future cyber–physical systems may extend over broad geographical areas, like cities or regions, thus, requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time by up to two orders of magnitude.

scholarly journals Bloom Filter-Based Secure Data Forwarding in Large-Scale Cyber-Physical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Siyu Lin ◽  
Hao Wu
Keyword(s):  
False Positive ◽  
Large Scale ◽  
False Positive Rate ◽  
Bloom Filter ◽  
Cyber Physical Systems ◽  
Security Requirements ◽  
Data Forwarding ◽  
Physical Systems ◽  
Secure Data ◽  
Positive Rate

Cyber-physical systems (CPSs) connect with the physical world via communication networks, which significantly increases security risks of CPSs. To secure the sensitive data, secure forwarding is an essential component of CPSs. However, CPSs require high dimensional multiattribute and multilevel security requirements due to the significantly increased system scale and diversity, and hence impose high demand on the secure forwarding information query and storage. To tackle these challenges, we propose a practical secure data forwarding scheme for CPSs. Considering the limited storage capability and computational power of entities, we adopt bloom filter to store the secure forwarding information for each entity, which can achieve well balance between the storage consumption and query delay. Furthermore, a novel link-based bloom filter construction method is designed to reduce false positive rate during bloom filter construction. Finally, the effects of false positive rate on the performance of bloom filter-based secure forwarding with different routing policies are discussed.

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