Operational Network Research Articles

Side-Channel Security Analysis of Connected Vehicle Communications Using Hidden Markov Models

This paper investigates side-channel vulnerabilities of a wireless communication application in vehicular environments (DSRC/WAVE) protocol implementation of a traffic intersection application. A prototype roadside unit (RSU) was implemented using real DSRC devices. The functionality of the WAVE short message (Wsm)-channel is extended to include an implementation of WAVE short message protocol (WSMP) for broadcasting GPS data and RSU instructions in vehicular communications. In the example used, DSRC is used to replace an intersection stoplight. Denial of service attacks are executed that leverage DSRC RSU timing and packet size side-channels to selectively disable the stoplight. Simulations are implemented to determine our ability to stealthily drop packets so as to force two vehicles to collide. Hidden Markov models (HMM) and Support Vector Machines (SVM) are constructed from sniffed side-channel information. We use inter-packet delay time and packet size side-channel information to design our attackes. In operational networks, packets should be encrypted in order to hide the contents of the packet payloads, but packet sizes and timing are not affected by encryption. HMMs were inferred using only side-channel information. The inferred HMMs track the protocol status over time. The SVM classifier was inferred using both side-channel data and packet payloads. At run-time, though, the SVM only had access to side-channel information. Simulation experiments were implemented to test HMM and SVM ability to identify packets used to signal vehicles to stop and yield right-of-way. Timing HMM side-channel attack caused collision with 2.5% false positive rate (FPR), while the packet size one resulted 9.5% FPR.

A Primer on Phased Array Radar Technology for the Atmospheric Sciences

Abstract The scientific community has expressed interest in the potential of phased array radars (PARs) to observe the atmosphere with finer spatial and temporal scales. Although convergence has occurred between the meteorological and engineering communities, the need exists to increase access of PAR to meteorologists. Here, we facilitate these interdisciplinary efforts in the field of ground-based PARs for atmospheric studies. We cover high-level technical concepts and terminology for PARs as applied to studies of the atmosphere. A historical perspective is provided as context along with an overview of PAR system architectures, technical challenges, and opportunities. Envisioned scan strategies are summarized because they are distinct from traditional mechanically scanned radars and are the most advantageous for high-resolution studies of the atmosphere. Open access to PAR data is emphasized as a mechanism to educate the future generation of atmospheric scientists. Finally, a vision for the future of operational networks, research facilities, and expansion into complementary radar wavelengths is provided.

Development and Validation of Physics Achievement Test (PAT) to measure Conceptual Understanding of 9th Grade Students

Conceptual understanding is related with connected and operational network of thoughts. The main objective of the study was the development and validation of a Physics Achievement Test (PAT) to measure the conceptual understanding of 9th Graders. PAT was comprised of MCQs based test on first three levels of Bloom’s Taxonomy. First step was content selection & its alignment with Student Learning Outcomes (SLOs). In Second step, Content validity was ensured through Content Validity Ratio (CVI) and Content Validity Index (CVI). Third step was specification of marks. In fourth step, PAT was pilot tested on 376, 9th grade science students. In fifth step, different quality statistics like Difficulty Index, Discrimination Power and Reliability were also computed. Initially, there were 84 items while final test was reduced to 75 items. Consequently, the results affirmed that PAT has adequate verifications of being valid and reliable to measure the conceptual understanding of 9th Graders.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection.

Industry 4.0 affects all components of the modern industry value chain. The accelerating use of the Internet and the convergence of industrial and operational networks constantly increase the need for secure industrial communication solutions. Therefore, “multilevel industrial cyber protection” is critical to Industry 4.0. In general, industrial protection refers to safeguarding information and data and the intellectual property rights of production processes related to the overall industry environment. The availability, integrity, and confidentiality of systems must be maintained. The goal challenge is the best possible protection from attacks and threats which create immediate financial damage and other risks in the industry (reputation, etc.). Based on the Defense-in-Depth strategy, a holistic, multilayered, and in-depth protection of industrial systems is developed in this paper. Specifically, a Semi-Self-Supervised Intrusion Detection System (S3IDS) is proposed, which combines advanced machine learning techniques for industrial data noise reduction to automate the discovery and separation of classes, which are essentially equivalent to cyber-related anomalies. As demonstrated by a mathematical simulation based on computational number theory and specifically on the concept of the single object, the proposed S3IDS learns to accurately reconstruct samples to predict the nature of an anomaly created directly by the industrial ecosystem.

A robust control-theory-based exploration strategy in deep reinforcement learning for virtual network embedding

Network slice management and, more generally, resource orchestration should be fully automated in 6G networks, as envisioned by the ETSI ENI. In this context, artificial intelligence (AI) and context-aware policies are certainly major options to move in this direction and to adapt service delivery to changing user needs, environmental conditions and business objectives. In this paper, we step towards this objective by addressing the problem of optimal placement of dynamic virtual networks through a self-adaptive learning-based strategy. These constantly evolving networks present, however, several challenges, mainly due to their stochastic nature, and the high dimensionality of the state and the action spaces. This curse of dimensionality requires, indeed, a broader exploration, which is not always compatible with a real-time execution in an operational network. Thus, we propose DQMC, a new strategy for virtual network embedding in mobile networks combining a Deep Reinforcement Learning (DRL) strategy, namely a Deep Q-Network (DQN), and Monte Carlo (MC). As learning is costly in time and computing resources, and sensitive to changes in the network, we suggest a control-theory-based techniques to dynamically leverage exploration in DQMC. This leads to fast, efficient, and sober learning compared to a Monte Carlo-based strategy. This also ensures a reliable solution even in the case of a change in the requests’ sizes or a node’s failure, showing promising perspectives for solutions combining control-theory and machine learning.

Connected Blockchain Federations for Sharing Electronic Health Records

With the growing utility of blockchain technology, the desire for reciprocal interactions among different blockchains is growing. However, most operational blockchain networks currently operate in a standalone setting. This fragmentation in the form of isolated blockchains creates interoperability difficulties, inhibiting the adoption of blockchains in various ecosystems. Interoperability is a key factor in the healthcare domain for sharing EHRs of patients registered in independent blockchain networks. Each blockchain network could have its own rules and regulations, obstructing the exchange of EHRs for improving diagnosis and treatments. Examples include patients being treated by healthcare providers in different countries or regions, or within one country but with a different set of rules per state or emirate. By contrast, a federation of blockchain networks can provide better communication and service to stakeholders in healthcare. Thus, solutions for facilitating inter-blockchain communication in such a blockchain federation are needed. However, this possibility has not been fully explored, and further investigations are still being conducted. Hence, the present study proposes a transaction-based smart contract triggering system for inter-blockchain communication, enabling EHR sharing among independent blockchains. We use local and global smart contracts that will be executed once a transaction is created in the blockchain. Local smart contracts are used for EHR sharing within the blockchain, whereas global smart contracts are used for EHR sharing among independent blockchains. The experimental setup is conducted using the Hyperledger Fabric blockchain platform. Inter-blockchain communication between two independent fabric networks is conducted through a global smart contract using Hyperledger Cactus for EHR sharing in a health federation setup. To the best of our knowledge, our study is the first to implement an inter-blockchain communication model in the healthcare domain.

Resilient Housing and Care Services for Aging Municipalities

The Social welfare and health care reform in Finland will have an impact on the services provided by the municipalities. The housing services, health promotion and wellbeing of residents will remain in charge of the local authorities. Environmental factors are important for independent coping of older people who live in their own homes assisted by peers or by home care staff. Therefore, architects need to anticipate the demographic change in housing design and urban planning.
 The Government Key project TÄYTYY (2017 – 2018) was a research and development project targeting at efficient and operational service network, combining housing, home care and remote care services. The objective was to provide a resilient service structure for small municipalities through resource efficiency, complementing the urban structure and sharing existing facilities and resources locally.
 The multiple case study method was used to promote shared use of resources and spaces in local context. The project was carried out with cross-sectoral collaboration together with Aalto University, municipalities, health care service providers and local associations. As result, the project provided a model and a process of development for small municipalities. TÄYTYY project was carried out in collaboration with the Ministry of the Social Affairs and Health and the Ministry of the Environment.

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor performance and a limited network lifetime. To overcome these issues, we propose a depth controlled with energy-balanced routing protocol, which will be able to adjust the depth of lower energy nodes and be able to swap the lower energy nodes with higher energy nodes to ensure consistent energy utilization. The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. In the proposed energy-efficient routing protocol, an enhanced cluster head node is used to select a strategy that can analyze the remaining energy and directions of each participating node. In the simulation, the proposed model achieves 86.7% packet delivery ratio, 12.6% energy consumption, and 10.5% packet drop ratio over existing depth-based routing and energy-efficient depth-based routing methods for underwater wireless sensor network.

A New Method to Distinguish between Nearby and Direct Lightning Strikes for Long Operational Medium Voltage Lines

Lightning strikes near Medium Voltage (MV) electricity distribution lines cause voltage transients to be induced onto the conductors of the power lines. The resultant Lightning Induced Overvoltage (LIOV) on the phase conductors may be estimated using computer models or measurements. Models and measurements are mostly limited to short floating lines to simplify calculations and to ensure accurate results that can be easily compared between different models. Existing measurement methods cannot be used to distinguish between nearby lightning and direct lightning strikes to an operational MV network by using only one instrument on a line that is more than 100 km long. The instrument needs to be less than a hundred meters from where the LIOV amplitude on the line is the highest. The new method described in this paper allows one to distinguish between nearby and direct lightning strikes to the line; by comparing the polarity of the current transient recorded at the substation to the lightning return-stroke polarity obtained from a lightning detection network. The new method also allows one to determine whether the LIOV of a particular lightning transient exceeded the Basic Insulation Level (BIL) of the line, by observing the power frequency fault current. The new method was applied to an operational 714 km network operating at 22 kV, and the results were compared with predictions in IEEE Std 1410-2010. Significantly fewer line faults due to nearby lightning were observed than predicted; the reason for this is explained.

Exploration of Pressure Driven Method in Hydraulic Modelling of Water Distribution Network

Abstract: A water supply network is assessed on its efficiency and capability to cater to the demand of consumers. There are two approaches for analysing a network viz. Demand Driven Approach (DDA) & Pressure Driven Approach (PDA). It assumes that the demands at all nodes are always satisfied, irrespective of the available nodal pressure. But many a times, in an operational network, certain situations such as pipe burst, pump failure, fire demand, supply shortage etc. arise. These situations cause pressure deficiency or even negative pressures. Such lower pressures cause water shortage at delivery points or sometimes even supply disruption. This is where DDA fails. PDA is comparatively a new approach. But it is capable of simulating pressure deficient conditions and analysing their effects on the network. Though, not for designing, but it can be efficiently used to simulate different conditions, analyse their effects and determine solutions to mitigate hazards. This paper compares DDA & PDA by analysing a looped network.

Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.

Application of a distributed verification in Islamic microfinance institutions: a sustainable model

The literature gap in microfinance paradox of double bottom line (financial performance vs. outreach) has always been an interesting area of research. This paper proposes a theoretical model most suitable for Islamic Microfinance Institutions (MFIs) which enables Islamic MFIs’ to operate together with the existing financial models compliant with Islamic Shariah Law. This model is based on a distributed verification/decision-making process that might be realized (but not necessary) through blockchain. Among the available distributed verification techniques, blockchain technology is an attractive emerging computing paradigm due to its decentralized, immutable, shared, and secure data structure characteristics. This model proposes three significant propositions. First, sharing information through blockchain will allow a transparent network in MFI operations, which will raise confidence for donors resulting in a causal effect of a relatively lower profit rate to be charged by the MFIs. Second, the consensus mechanism will enable risk-sharing, a character of Islamic finance; thus, the MFIs will operate without any collateral for low-risk firms. Third, the double bottom line of MFIs' long-lasting paradox would be solved. As for practical implication of this proposed model, the causal impact of lower cost investment by the lenders would increase social welfare because of no collateral and no initial wealth requirement. The proposed model proposes a credit rationing approach where profit can be negative. No collateral will be used when calculating the creditworthiness of a borrower.

I-COPTIC: Implementation of community pharmacy-based testing for hepatitis C: Delphi consensus protocol.

The World Health Organisation aims to eliminate Hepatitis C (HCV) by 2030. To achieve this, targeted testing needs to be widely available. Studies have demonstrated that community pharmacies can deliver effective targeted testing for HCV and the National Health Service in England has commissioned a national service. However, a recent survey of HCV operational delivery networks has shown limited uptake of this service. The objective of this protocol is to guide the formation of a consensus statement to facilitate the widespread implementation of community pharmacy-based targeted testing for HCV. We will use a modified Delphi method. A purposive selection of panel participants will be identified and recruited from a national survey and via chain-referral sampling. The main inclusion criteria for selection is direct involvement in the implementation of an HCV testing service in pharmacies. We aim for a heterogenous group, encompassing all aspects of the testing service. We will conduct a three round Delphi. The first round will consist of open questions which will be qualitatively analysed using thematic analysis with a framework method based on the WHO Health Systems Framework. This analysis will generate statements, that will be sent to the participants in the second round. A third round will be used where consensus is not reached. The findings from this Delphi consensus study will facilitate the widespread implementation of targeted testing for HCV in community pharmacies.

Empirical study of an artificial neural network for a manufacturing production operation

Empirical study of an artificial neural network for a manufacturing production operation

Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.

Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany

Abstract. From June to August 2020, an observational network of 103 meteorological ground-based stations covered the greater area (50 km × 35 km) of Hamburg (Germany) as part of the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hanseatic city of Hamburg (FESST@HH). The purpose of the experiment was to shed light on the sub-mesoscale (𝒪(100) m–𝒪(10) km) structure of convective cold pools that typically remain under-resolved in operational networks. During the experiment, 82 custom-built, low-cost APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure with fast-response sensors at 1 s resolution to adequately capture the strong and rapid perturbations associated with propagating cold pool fronts. A secondary network of 21 weather stations with commercial sensors provided additional information on relative humidity, wind speed, and precipitation at 10 s resolution. The realization of the experiment during the COVID-19 pandemic was facilitated by a large number of volunteers who provided measurement sites on their premises and supported station maintenance. This article introduces the novel type of autonomously operating instruments, their measurement characteristics, and the FESST@HH data set (https://doi.org/10.25592/UHHFDM.10172; Kirsch et al., 2021b). A case study demonstrates that the network is capable of mapping the horizontal structure of the temperature signal inside a cold pool, and quantifying a cold pool's size and propagation velocity throughout its life cycle. Beyond its primary purpose, the data set offers new insights into the spatial and temporal characteristics of the nocturnal urban heat island and variations of turbulent temperature fluctuations associated with different urban and natural environments.

Perinatal excellence to reduce injury in preterm birth (PERIPrem) through quality improvement

Perinatal Excellence to Reduce Injury in Premature Birth (PERIPrem) is an 11-element perinatal care bundle designed to improve outcomes for preterm babies, in line with the National Health Service (NHS)...

Traffic Anomaly Detection Using Deep Semi-Supervised Learning at the Mobile Edge

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause.

RANCANG BANGUN SISTEM PENERIMAAN DOKUMEN PADA PT. REASURANSI INDONESIA UTAMA

In the Administration at PT. Reinsurance Indonesia Utama letter is an important means of important and confidential information related to the agency contained in it. Timeliness in receiving letters must be carried out quickly and precisely. Because of that, a better and structured mail receipt information system process is needed to be faster in searching for existing data and in the process of making reports. This letter receipt system has the following capabilities: This reception system is run on a computer operational network in the administration section of PT. Reinsurance Indonesia Utama, administrative officers can access the system by entering the correct admin login, can change, search, and save the letter. The research methods used are observation, interviews, literature studies, systems analysis, proposed system design, system testing, and system implementation. The result of this design is to produce an application that can manage letters according to the specified flow, and solve existing problems.