Data Exchange Capabilities Research Articles

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

UOWC Integration with ROVs: A Fiber-Optic Approach for Enhanced Underwater Exploration

Underwater Optical Wireless Communication (UOWC) revolutionizes underwater exploration, leveraging seawater's unique property—reduced absorption of blue-green light. This offers superior advantages over traditional communication methods, providing higher bandwidth and lower latency. This paper explores the integration of UOWC with Remotely Operated Vehicles (ROVs), enhancing data exchange and command capabilities in challenging underwater environments. Seawater's unique properties empower UOWC to outperform acoustic and RF methods, crucial for real-time applications. The study utilizes advanced techniques like Fiber-Optic Communication, High Order Sliding Mode Control, and Hybrid Blockage Detection for ROV integration and control. While the abstract focuses on theory and design, it sets the stage for empirical validations, emphasizing the need for future research to quantify improvements in real-world underwater scenarios

Performance Review of Major Packages in Light of Data Exchange Capabilities in R Ecosystem

Performance Review of Major Packages in Light of Data Exchange Capabilities in R Ecosystem

Impact of Communication System Characteristics on Electric Vehicle Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets.

Improving the Guyana REDD+ Monitoring Reporting and Verification System: stakeholders perception

In 2009 Guyana developed and tested their REDD+ Monitoring Reporting and Verification System (REDD+ MRVS) to satisfy a Memorandum of Understanding between Guyana and the Kingdom of Norway. Despite being a developing country, it has been one of the few countries to progress its MRVS substantially with comparatively similar results to independent auditing standards. While socio-political support has helped efforts to implement a national based monitoring system challenges of improving stakeholder coordination and collaboration can potentially undermine efforts to maintain a robust system. The effectiveness of the REDD+ MRVS was assessed to determine end users demands, and system output applications. We employed a qualitative methodological approach using the Borich needs assessment model. The results revealed that the overall structure of REDD+ MRVS was mostly likely to reduce the efficiency in reporting the outcomes of the MRVS and its value as a tool to aid in natural resources management. Stakeholders believe that Guyana's REDD+ MRVS has the potential to improve natural resource management in Guyana. However, to boost the system's credibility, monitoring, data exchange capabilities, and the timeliness of the assessment should be promptly addressed. Further, the role of Indigenous communities in real time monitoring remains a central idea to be explored.

Freeing the Animal Model: A Modular, Wirelessly Powered, Implantable Electronic Platform.

Fully implantable electronic devices in freely roaming animal models are useful in biomedical research, but their development is prohibitively resource intensive for many laboratories. The advent of miniaturized microcontrollers with onboard wireless data exchange capabilities has enabled cost-efficient development of myriad do-it-yourself electronic devices that are easily customizable with open-source software ( https://www.arduino.cc/ ). Likewise, the global proliferation of mobile devices has led to the development of low-cost miniaturized wireless power technology. The authors present a low-cost, rechargeable, and fully implantable electronic device comprising a commercially available, open-source, wirelessly powered microcontroller that is readily customizable with myriad readily available miniature sensors and actuators. The authors demonstrate the utility of this platform for chronic nerve stimulation in the freely roaming rat with intermittent wireless charging over 4 weeks. Device assembly was achieved within 2 hours and necessitated only basic soldering equipment. Component costs totaled $115 per device. Wireless data transfer and wireless recharging of device batteries was achieved within 30 minutes, and no harmful heat generation occurred during charging or discharging cycles, as measured by external thermography and internal device temperature monitoring. Wireless communication enabled triggered cathodic pulse stimulation of the facial nerve at various user-selected programmed frequencies (1, 5, and 10 Hz) for periods of 4 weeks or longer. This implantable electronic platform could be further miniaturized and expanded to study a vast array of biomedical research questions in live animal models. The clinical relevance of electrical stimulation in neural recovery remains controversial, and long-term neural stimulation in small animal models is challenging. We have developed a low-cost, fully implantable, wirelessly powered nerve stimulation device to facilitate further research in nerve stimulation in animal models.

PREIUD: An Industrial Control Protocols Reverse Engineering Tool Based on Unsupervised Learning and Deep Neural Network Methods

The security of industrial control systems relies on the communication and data exchange capabilities provided by industrial control protocols, which can be complex, and may even use encryption. Reverse engineering these protocols has become an important topic in industrial security research. In this paper, we present PREIUD, a reverse engineering tool for industrial control protocols, based on unsupervised learning and deep neural network methods. The reverse process is divided into stages. First, we use the bootstrap voting expert algorithm to infer the keyword segment boundaries of the protocols, considering the symmetry properties. Then, we employ a bidirectional long short-term memory conditional random field with an attention mechanism to classify the protocols and extract their format and semantic features. We manually constructed data sample sets for six commonly used industrial protocols, and used them to train and test our model, comparing its performance to two advanced protocol reverse tools, MSERA and Discoverer. Our results showed that PREIUD achieved an average accuracy improvement of 7.4% compared to MSERA, and 15.4% compared to Discoverer, while also maintaining a balance between computational conciseness and efficiency. Our approach represents a significant advancement in the field of industrial control protocol reverse engineering, and we believe it has practical implications for securing industrial control systems.

Simulation of the Use of Variance Component Estimation in Relative Weighting of Inter-Satellite Links and GNSS Measurements

Inter-satellite links (ISLs) can improve the performance of the Global Navigation Satellite System (GNSS) in terms of precise orbit determination, communication, and data-exchange capabilities. This research aimed to evaluate a simulation-based processing strategy involving the exploitation of ISLs in orbit determination of Galileo satellites, which are not equipped with operational ISLs. The performance of the estimation process is first tested based on relative weighting coefficients obtained with methods of variance component estimation (VCE) varying in the complexity of the calculations. Inclusion of biases in the ISL measurements allows evaluation of the processing strategy and assessment of the impact of three different sets of ground stations: 44 and 16 stations distributed globally and 16 located in Europe. The results indicate that using different VCE approaches might lower orbit errors by up to 20% with a negligible impact on clock estimation. Depending on the applied ISL connectivity scheme, ISL range bias can be estimated with RMS between 10% to 30% of initial bias values. The accuracy of bias estimation may be associated with weighting approach and the number of ground stations. The results of this study show how introducing VCE with various simulation parameters into the processing chain might increase the accuracy of the orbit estimation.

Power Grid Multi-protocol Network Topology Based on Data Exchange Strategy

With the combination of Internet of Things technology and smart grid, there are more and more application scenarios of Internet of Things in power system, which makes the whole power system more informationized and intelligent. In this paper, an intelligent data switching technology for power communication network based on multi-protocol label is proposed. The link structure model of mobile core network is constructed, and the TDMA protocol of the multi-protocol intelligent data switching node is designed by using route detection method. The dynamic routing decision and node rotation scheduling of the mobile core network are realized, and the intelligent data exchange performance is improved. Finally, the simulation experiment of the intelligent data exchange protocol is carried out, and the comparative experiment between the proposed method and other methods is carried out, which shows the superior performance of the proposed method in improving the intelligent data exchange capability of the mobile core network.

Approaches for overcoming barriers to cross-sector data sharing.

To characterize factors influencing the development and sustainability of data sharing in the Mid-Ohio Farmacy (MOF), a produce referral program implemented in partnership between a community-based organization (the Mid-Ohio Food Collective ["Food Collective"]) and an academic medical center (The Ohio State University Wexner Medical Center [OSUWMC]). We used an in-depth case study approach to identify challenges that arose during implementation of the MOF and related solutions via semistructured interviews with representatives of both organizations (May-September2020). Key informants from OSUWMC (n = 20) and the Food Collective (n = 11) were identified using a combination of purposive and convenience sampling; they included administrators, project champions, clinical providers, and food pantry representatives. Interview transcripts were coded using a deductive dominant approach guided by a logic model aimed at determining the resources and activities relevant to the development of the partnership. Challenges of cross-sector data sharing fit into 3 themes: data sharing regulations, data exchange capabilities, and cross-sector data integration. Overcoming these challenges required creative workarounds-for example, linking patients across organizations was done via establishment of a unique, partnership-specific patient identifier, which was incorporated into the health system's electronic health record for continuity. Our findings suggest that current regulatory frameworks are misspecified to the growing interest in cross-sector partnerships between health care and community-based organizations. Future efforts to support these relationships should consider clarifying rules around data sharing and increasing Medicaid support for nonmedical, health-related social needs.

A multivariate statistical evaluation of actual use of electronic health record systems implementations in Kenya.

Health facilities in developing countries are increasingly adopting Electronic Health Records systems (EHRs) to support healthcare processes. However, only limited studies are available that assess the actual use of the EHRs once adopted in these settings. We assessed the state of the 376 KenyaEMR system (national EHRs) implementations in healthcare facilities offering HIV services in Kenya. The study focused on seven EHRs use indicators. Six of the seven indicators were programmed and packaged into a query script for execution within each KenyaEMR system (KeEMRs) implementation to collect monthly server-log data for each indicator for the period 2012-2019. The indicators included: Staff system use, observations (clinical data volume), data exchange, standardized terminologies, patient identification, and automatic reports. The seventh indicator (EHR variable Completeness) was derived from routine data quality report within the EHRs. Data were analysed using descriptive statistics, and multiple linear regression analysis was used to examine how individual facility characteristics affected the use of the system. 213 facilities spanning 19 counties participated in the study. The mean number of authorized users who actively used the KeEMRs was 18.1% (SD = 13.1%, p<0.001) across the facilities. On average, the volume of clinical data (observations) captured in the EHRs was 3363 (SD = 4259). Only a few facilities(14.1%) had health data exchange capability. 97.6% of EHRs concept dictionary terms mapped to standardized terminologies such as CIEL. Within the facility EHRs, only 50.5% (SD = 35.4%, p< 0.001) of patients had the nationally-endorsed patient identifier number recorded. Multiple regression analysis indicated the need for improvement on the mode of EHRs use of implementation. The standard EHRs use indicators can effectively measure EHRs use and consequently determine success of the EHRs implementations. The results suggest that most of the EHRs use areas assessed need improvement, especially in relation to active usage of the system and data exchange readiness.

Research on Key Technologies of VR based on MEC

Multi access edge computing (MEC) is an extension of 5G network on the edge side. Its network characteristics of high bandwidth and low delay provide necessary guarantee for the extreme user experience of VR (virtual reality). At the same time, the distributed architecture of 5g MEC network and the fast data exchange capability between nodes provide the hardware foundation for VR application marginalization deployment and equipment mobility access. Considering the cost and experience of VR, this paper first analyzes the advantages and disadvantages of VR based on traditional hardware architecture, and then describes the benefits of MEC on VR cost and experience. Finally, taking VR live video scene as an example, the key technologies of VR based on MEC are described.

Progress and Challenges with Connecting Hospitals with the Public Health System

Improving the health information technology (HIT) infrastructure of the public health system is critical for public health activities and population health management. To strengthen the public health HIT infrastructure, CMS included specific public health reporting capabilities in the Meaningful Use (MU) criteria that comprised the Electronic Health Record (EHR) incentive program for hospitals, now called the Promoting Interoperability (PI) program.Evidence suggests that these incentives have improved hospitals’ public health reporting capabilities, but recent data show that only 60% of hospitals have achieved MU standards, suggesting that barriers remain for electronic integration of hospitals with public health agencies. While progress is promising and may be indicative of improvements in electronic data exchange capabilities, the specific issues that hospitals continue to face are unclear and there remains limited understanding of approaches that may improve hospital public health reporting capabilities.This study uses cross‐sectional data from the 2017 American Hospital Association (AHA) Annual Survey and Information Technology (AHAIT) supplement, which reports the challenges hospitals face in submitting data to public health agencies. Four nonmutually exclusive options included data standards issues, technical capacity issues, uncertainty of who to send data to, and an open‐ended option. Open‐ended responses were qualitatively coded by type of challenge (ie, technology, organization, or environment); responsible/affected party (ie, hospital, public health agency, vendor, or multiple); and the specific issue described. Multivariate logistic regression was used to identify characteristics of hospitals associated with experiencing any challenge.Acute‐care, nonfederal hospitals responding to both the 2017 AHA and AHAIT supplemental surveys.Among 2,794 hospitals included, 1,713 (61%) reported experiencing at least one challenge in reporting health data to a public health agency. Of hospitals experiencing challenges, the vast majority reported organizational issues (85% or 1,458) at public health agencies (84% or 1,226), specifically the technical capacity of public health agencies. Hospital system members (adjusted odds ratio (AOR) 0.80; 95% Confidence Interval (CI) 0.65‐0.98) were less likely to experience challenges, whereas for‐profit hospitals (AOR 1.47; 95% CI 1.07‐2.03), being in the West (AOR 1.72; 95% CI 1.28‐2.30), possessing basic (AOR 1.56; 95% CI 1.06‐2.31) or comprehensive (AOR 1.57; 95% CI 1.06‐2.31) EHRs, and participating in an HIE (AOR 1.87; 95% CI 1.58‐2.22) were associated with increased likelihood of experiencing challenges.Challenges remain in facilitating data exchange between hospitals and public health agencies. Hospitals with more advanced EHRs and HIE were more likely to experience challenges. These hospitals may have technological capabilities that exceed those of public health agencies. Hospitals and public health agencies may also be in competition for a technically trained workforce, and data suggest that both may be experiencing shortages of staff with essential expertise.Efforts to alleviate barriers to hospital reporting of health data to public health agencies should target technological, organizational, and environmental issues. Responses include improvements in the data standards that facilitate exchange; funding for public health agencies to improve their technological capabilities, workforce training programs, and clarifications for PI policy specifications and reporting.

Monitoring the final orbital decay and the re-entry of Tiangong-1 with the Italian SST ground sensor network

The uncontrolled re-entry of spacecraft and upper stages is quite common, occurring nearly every week. Among them, intact objects having a mass greater than five metric tons re-enter, on average, 1–2 times per year. Therefore, the re-entry of the first Chinese Space Station, Tiangong-1, was far from unusual, but attracted anyway a great worldwide attention and some concerns. For these reasons, the Italian component of the European SST (Space Surveillance and Tracking) consortium took this opportunity for carrying out a national exercise. According to Chinese official sources, the ground control of Tiangong-1 was lost in March 2016, precluding the planned de-orbiting in the South Pacific Ocean Unpopulated Area (SPOUA). Tiangong-1 consisted of a cylindrical section, 10:5 m in length and 3:4 m in (maximum) diameter, with two rectangular solar panels of 3 m × 7 m. The mass was estimated to be around 7500 kg.The Italian network of sensors activated for the campaign included mono-static and bi-static radars, optical telescopes, a laser ranging station and a network of all-sky cameras, originally deployed for the observation of fireballs and bolides. In addition to providing complementary information, concerning the orbit, the attitude and the photometry of Tiangong-1, this quite heterogeneous collection of national assets provided also the occasion for testing, in an operational environment, the Italian sensor tasking preparedness and the data acquisition, exchange and processing capabilities within the European SST consortium.In this respect, it is important to remember that in 2014 the European Commission, well aware of the topic criticality, took the commitment to implement a European network of sensors for surveillance and tracking of objects in Earth’s orbit by starting a dedicated SST support framework program. Italy, France, Germany, Spain and UK joined it and constituted, together with SatCent, the front desk for SST services, the EUSST Consortium. In this paper, a description of the Tiangong-1 monitoring activities and of the main observations results obtained by the Italian sensor network are reported. Attention is also devoted to the coordination aspects of several Italian entities (military, civil and research organizations) that worked together. Finally, a description of the re-entry prediction and alert procedure for the national civil protection authorities is presented.

PaFiR: Particle Filter Routing – A predictive relaying scheme for UAV-assisted IoT communications in future innovated networks

Increasing urbanization, smart cities and other cutting-edge technologies offer the prospect of providing more functions to benefit citizens by relying on the substantial data processing and exchange capabilities now possible. This can generate significant unpredictable and unbalanced data loads for the bearing IoT network to support its application and service demands. We thus propose a wireless routing scheme designed to use the Particle Filter algorithm to empower portable smart devices with intelligent capacities for the radio communication system. This facilitates the offloading of traffic from traditional wireless networks and enables the IoT system to adopt unmanned aerial vehicles, thus also offering further innovation to flying network platforms. The proposed PaFiR routing protocol offers the network more scalability, tolerance and resilience, to achieve the goal of smart relaying. Simulation results that demonstrate the routing algorithm designed offers excellent performance when compared with existing wireless relaying schemes. It provides delivery ratios that are improved by up to 40% without unmanageable increases in latency or overheads.

&lt;italic&gt;Neda&lt;/italic&gt;: Supporting Direct Inter-Core Neighbor Data Exchange in GPUs

Image processing applications employ various filters for several purposes, such as enhancing the images and extracting the features. Recent studies show that filters in image processing applications take a substantial amount of the execution time, and it is crucial to boost their performance to improve the overall performance of the image processing applications. Image processing filters require a significant amount of data sharing among threads which are in charge of filtering neighbor pixels. Graphics Processing Units (GPUs) attempt to satisfy the demand of data sharing by providing the scratch-pad memory, shuffle instructions, and on-chip caches. However, we observe that these mechanisms are insufficient to provide a fast and energy-efficient neighbor data sharing for the image processing filters. In this paper, we propose a new hardware/software co-design mechanism for GPUs, to effectively provide a fast and energy-efficient register-level neighbor data sharing for the image filters. We propose a neighbor data exchange mechanism, called Neda , that adds a register to each streaming processor (SP) which can be accessed by its neighboring SPs. Our experimental results show that Neda improves the performance and energy consumption by 12.4 and 13.5 percent, on average, respectively, compared to the NVIDIA SDK implementation of image processing filters. Moreover, Neda 's performance is within 9.3 percent of the ideal GPU with zero latency neighbor data exchange capability.

VAnt-ITS: Ant-based mobility aware routing for intelligent transport systems

VANETs offer data exchange capabilities at very high speed. Due to variations in the vehicle’s velocity, it is quite challenging to ensure the reliable communication. In a mobile environment, the network topology is frequently updated, thus causes packet loss at intermediate nodes, generate extra control overhead for routing as well as it may also lead to congestion/contention. It is necessary to manage the mobile environment, in order to ensure reliability and scalability. Researchers have developed various solutions for V2I and V2V but still, thee are some open issues related to communication over high-speed networks. In this paper, a Swarm Intelligence (SI) based routing for VANETs is proposed, called vAnt-ITS. Simulation results show its performance in terms of improved network efficiency. Keywords-VANET, Wireless, Mobility, ANTnet, AI, Swarm Intelligence, ITS

Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments

Lane changing is a complex decision-making process that is affected by factors such as vehicle features, driver characteristics, network attributes, and traffic conditions. Understanding the changes in driver behavior and vehicle trajectory before the lane change initiation process is essential to the design of a safe and reliable crash avoidance system. The recently introduced connected vehicle (CV) technology provides opportunities for real-time, high-resolution data exchange capability between vehicles. This study explored the high-resolution vehicle trajectory data attainable in CV environments for detecting the onset of lane change maneuvers. The observed change in behavior before the initiation of such a maneuver was examined to identify the associated driving pattern. This pattern was used to develop two lane change detection models: an artificial neural network (ANN) model and a multiple logistic regression (MLR) model. The two models were trained and tested with Next Generation Simulation data collected from a weaving freeway segment in Arlington, Virginia. The results show 80% detection accuracy for the ANN model, compared with 72% for the MLR model. The developed models identified the vehicle speed, acceleration, and speed relative to the lead vehicle as the most significant attributes for lane change detection. Drivers’ intentions could be detected early and potential crashes could be prevented by training these models to capture similar driving behavior patterns.

Design of a high throughput electronics module for high energy physics experiments**Supported by National Natural Science Foundation of China (11435013, 11461141011)

High-energy physics experiments enable us to explore and understand particle properties and interactions. An increase in luminosity in the accelerator, which allows us to study particles in higher energy ranges, demands faster data transmission and processing. Aimed at this, a high throughput uTCA-compliant electronics module, based on the latest FPGAs, has been designed. It contains 48 10.0 Gb/s optical fiber input channels and 24 10.0 Gb/s optical fiber output channels, supporting up to 480 Gb/s input bandwidth and 240 Gb/s output bandwidth. It complies with the uTCA standards, providing high speed data exchange capability and functioning as a compact and key module in a trigger and DAQ system for a large experiment. A reliable 10.0 Gb/s data transmission among two boards has been verified and one functionality that merges 6 1.6 Gb/s data channels into one single 10.0 Gb/s channel has been achieved. The hardware, firmware and software together with a performance evaluation are given in this paper.

Toward enhanced data exchange capabilities for the oneM2M service platform

The machine-to-machine (M2M) service platform is being standardized to enable the intercommunication of devices, which is the basis for smart environments and intelligent transport systems (ITS) applications. In such environments, adapting the data exchange between devices and applications to the requirements of the application is a critical step in ensuring the functionality and reliability of the service. This article employs test-cases to analyze the data exchange of the oneM2M standard using an M2M-based automotive service delivery platform. Following the analysis, it proposes enhancements such as application-data-dependent criteria for data notification in combination with aggregation of different subscriptions to the same resource. Finally, the article discusses the proposed enhancements against the background of M2M design considerations and improved privacy.