Volume Of Messages Research Articles

Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus Using an LSTM Autoencoder

Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be “air-gapped”, are becoming more susceptible to cyber-attack. Most real-time systems that communicate using the Military Standard 1553B Multiplex data bus (MIL-STD-1553B) protocol do not have the ability to detect cyber-attacks. These systems were originally developed with safety and redundancy in mind, not security. These two factors introduce attack vectors to MIL-STD-1553B communication buses and expose associated avionics systems to exploitation. Recent approaches to anomaly detection for the MIL-STD-1553B data bus have leveraged statistical analysis, Markov Chain modelling, remote terminal fingerprinting and signature-based detection. However, their comparative effectiveness is unknown. Regarding the statistical analysis technique, the lack of accuracy and precision in detecting the start and stop time of anomalous events are not ideal for conducting investigations due to the sheer volume of messages still required to be manually analysed. Deep learning techniques offer an effective means of anomaly detection and applying these techniques to the MIL-STD-1553B data bus could provide more accurate and precise detection times when anomalies or attacks are present, when compared to known statistical analysis, leading to more efficient forensic investigations of anomalous events.

Survey Paper on Implementation of Abusive Language Detection in Chatting Application

Communication has become stronger due to exponential increase in the usage of social media in the last few years. People use them for communicating with friends, finding new friends, updating any important activities of their life, etc. Due to their growing popularity and deep reach, these mediums are infiltrated with huge volume of spam messages. Spam message randomly sent to multiple addressees by all sorts of groups, but mostly lazy advertisers and criminals who wish to lead you to phishing sites. In this we are using various machine learning techniques for detecting spam in the short text messages and also Google vision API for detecting spam images. Key Words: Spam Filtration, Google Vision API, OWASP, Naive baise classifier, Dictionary Based Algorithm

Care Partner Engagement in Secure Messaging Between Patients With Diabetes and Their Clinicians: Cohort Study.

Patient engagement with secure messaging (SM) via digital patient portals has been associated with improved diabetes outcomes, including increased patient satisfaction and better glycemic control. Yet, disparities in SM uptake exist among older patients and racial and ethnic underserved groups. Care partners (family members or friends) may provide a means for mitigating these disparities; however, it remains unclear whether and to what extent care partners might enhance SM use. We aim to examine whether SM use differs among older patients with diabetes based on the involvement of care partner proxies. This is a substudy of the ECLIPPSE (Employing Computational Linguistics to Improve Patient-Provider Secure Emails) project, a cohort study taking place in a large, fully integrated health care delivery system with an established digital patient portal serving over 4 million patients. Participants included patients with type 2 diabetes aged ≥50 years, newly registered on the patient portal, who sent ≥1 English-language message to their clinician between July 1, 2006, and December 31, 2015. Proxy SM was identified by having a registered proxy. To identify nonregistered proxies, a computational linguistics algorithm was applied to detect words and phrases more likely to appear in proxy messages compared to patient-authored messages. The primary outcome was the annual volume of secure messages (sent or received); secondary outcomes were the length of time to the first SM sent by patient or proxy and the number of annual SM exchanges (unique message topics generating ≥1 reply). The mean age of the cohort (N=7659) at this study's start was 61 (SD 7.16) years; 75% (n=5573) were married, 15% (n=1089) identified as Black, 10% (n=747) Chinese, 12% (n=905) Filipino, 13% (n=999) Latino, and 30% (n=2225) White. Further, 49% (n=3782) of patients used a proxy to some extent. Compared to nonproxy users, proxy users were older (P<.001), had lower educational attainment (P<.001), and had more comorbidities (P<.001). Adjusting for patient sociodemographic and clinical characteristics, proxy users had greater annual SM volume (20.7, 95% CI 20.2-21.2 vs 10.9, 95% CI 10.7-11.2; P<.001), shorter time to SM initiation (hazard ratio vs nonusers: 1.30, 95% CI 1.24-1.37; P<.001), and more annual SM exchanges (6.0, 95% CI 5.8-6.1 vs 2.9, 95% CI 2.9-3.0, P<.001). Differences in SM engagement by proxy status were similar across patient levels of education, and racial and ethnic groups. Among a cohort of older patients with diabetes, proxy SM involvement was independently associated with earlier initiation and increased intensity of messaging, although it did not appear to mitigate existing disparities in SM. These findings suggest care partners can enhance patient-clinician telecommunication in diabetes care. Future studies should examine the effect of care partners' SM involvement on diabetes-related quality of care and clinical outcomes.

Implementation of an electronic health record-integrated instant messaging system in an academic health system.

Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment of one such system (Secure Chat). Data were collected on Secure Chat message volume from June 2017 to April 2023. Significant perideployment events were reviewed chronologically. After the first coronavirus disease 2019 lockdown in March 2020, messaging use increased by over 25 000 messages per month, with 1.2 million messages sent monthly by April 2023. Comparative features of current communication modalities in healthcare were summarized, highlighting the many advantages of Secure Chat. While EHR-embedded secure instantaneous messaging systems represent a novel and potentially valuable communication medium in healthcare, generally agreed-upon best practices for their implementation are, as of yet, undetermined.

Demographics and clinical features associated with rates of electronic message utilization in the primary care setting

IntroductionElectronic messages are growing as an important form of patient-provider communication, particularly in the primary care setting. However, adoption of healthcare technology has been under-utilized by underserved patient populations. The purpose of this study was to describe how adoption and utilization of electronic messaging occurred within a large primary care urban-based patient population. MethodsIn this retrospective study, the frequency of electronic messages initiated by adult outpatient primary care patients was observed. Patients were classified as either non-portal adopters, non-message utilizers, low message utilizers, and high message utilizers. Logistic regression modeling was used to compare factors associated with message utilization rates to determine disparities in access. ResultsAmong a sample of 27,453 ethnically diverse adult patients from the Houston, Texas Metropolitan area, 33,497 unique messages were sent (1.22 messages/patient). Message burden was predominantly derived by a small number of high utilizers (individuals who sent 3 or more messages), who comprised 15.7 % of the study population (n = 4302) but accounted for 77 % of the message volume (n = 25,776). These high utilizers were typically older, White, English speaking, from middle to upper income zip codes, had higher number of comorbidities, and a higher number of clinical visits. ConclusionsMost inbox messages were generated by a small number of patients. While it was reassuring to see older and sicker individuals utilizing electronic messaging, patients from minority and/or lower income background utilized electronic messaging much less. This may propagate systematic bias and decrease the level of care for traditionally underserved patients.

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023.

The purpose of this study is to evaluate recent trends in primary care physician (PCP) electronic health record (EHR) workload. This longitudinal study observed the EHR use of 141 academic PCPs over 4 years (May 2019 to March 2023). Ambulatory full-time equivalency (aFTE), visit volume, and panel size were evaluated. Electronic health record time and inbox message volume were measured per 8 hours of scheduled clinic appointments. From the pre-COVID-19 pandemic year (May 2019 to February 2020) to the most recent study year (April 2022 to March 2023), the average time PCPs spent in the EHR per 8 hours of scheduled clinic appointments increased (+28.4 minutes, 7.8%), as did time in orders (+23.1 minutes, 58.9%), inbox (+14.0 minutes, 24.4%), chart review (+7.2 minutes, 13.0%), notes (+2.9 minutes, 2.3%), outside scheduled hours on days with scheduled appointments (+6.4 minutes, 8.2%), and on unscheduled days (+13.6 minutes, 19.9%). Primary care physicians received more patient medical advice requests (+5.4 messages, 55.5%) and prescription messages (+2.3, 19.5%) per 8 hours of scheduled clinic appointments, but fewer patient calls (-2.8, -10.5%) and results messages (-0.3, -2.7%). While total time in the EHR continued to increase in the final study year (+7.7 minutes, 2.0%), inbox time decreased slightly from the year prior (-2.2 minutes, -3.0%). Primary care physicians' average aFTE decreased 5.2% from 0.66 to 0.63 over 4 years. Primary care physicians' time in the EHR continues to grow. While PCPs' inbox time may be stabilizing, it is still substantially higher than pre-pandemic levels. It is imperative health systems develop strategies to change the EHR workload trajectory to minimize PCPs' occupational stress and mitigate unnecessary reductions in effective physician workforce resulting from the increased EHR burden.

StockTwits classified sentiment and stock returns

We classify the sentiment of a large sample of StockTwits messages as bullish, bearish or neutral, and create a stock-aggregate daily sentiment polarity measure. Polarity is positively associated with contemporaneous stock returns. On average, polarity is not able to predict next-day stock returns. But when we condition on specific events, defined as sudden peaks of message volume, polarity has predictive power on abnormal returns. Polarity-sorted portfolios illustrate the economic relevance of our sentiment measure.

Analysis of methods of classification of electronic messages based on neural network models

In the article, the creation of a mechanism for detecting and classifying messages is considered, with an assessment of how effectively different neural networks work and can recognize and classify different types of electronic messages, including phishing attacks, spam, and legitimate messages. A preliminary analysis of incoming messages has been performed, encompassing their headers, text, and other relevant attributes. For instance, in the case of emails, these attributes could be the 'subject' and 'sender' of the message. Methods for data preparation and processing have been reviewed, including text vectorization, noise removal, and normalization, to be utilized in training neural networks. Message tokenization has been performed by transforming them into a numerical format while considering the selection of features. For text messages, it is crucial to execute both tokenization and text vectorization. The model training was performed on the test data with prior splitting into two parts: 80% for training and 20% for testing. The training set is utilized for training the model, while the test set is used to evaluate its effectiveness. The peculiarity of the class structure of the data, namely the uniformity of the distribution of classes, is considered. In this case, spam occurs less frequently than legitimate messages, so class balancing techniques such as random deletion of redundant examples, upsampling, and subsampling were applied to ensure adequate model training. Optimization of network parameters was performed, by researching the optimal parameters of neural networks, such as the number and size of layers, activation functions, and optimization of hyperparameters to achieve the best performance. Hyperparameter optimization includes determining optimal settings for neural networks, such as layer size, activation functions, learning rate, and other parameters. The effectiveness was assessed by comparing the results and performance of various classification methods based on neural networks using metrics such as precision and F1-score. It was determined how well the methods can avoid misclassifications where legitimate messages are mistakenly identified as spam, and vice versa. A comparison of the methods' effectiveness in processing a large volume of messages in real time was conducted. An analysis of different architectures of neural network models was performed. Based on the analysis, it was revealed how effectively different neural network models can recognize and classify messages as spam.

Online hate in the Philippines: the role of narratives and networks

ABSTRACT Online hate is a global problem, affecting diverse social conflicts around the world. Common techniques to understand online hate focus primarily on measuring the volume of hateful messages and identifying individual perpetrators. However, these approaches fail to capture how online hate draws on wider social narratives to attack its targets, and how online hate is spread and organized in networked groups. Our work posits that assessing online hate through the lens of narratives and networks facilitates a deeper understanding of its dynamics in specific contexts and points to more holistic directions for mitigating its potential manipulation. We demonstrate the utility of our framework across two case studies in the Philippines: (a) the 2020 outbreak of the COVID-19 pandemic, and (b) the 2022 presidential elections. Through a computational pipeline of social cyber-security tools, we characterize the online hate narratives used to attack gendered, political, and racial identities in these events, and the network structures of the online hate groups involved. We additionally quantify manipulation of online hate narratives and networks by bots and trolls. These findings offer insights into the design of effective counter-narratives and building more resilient communities against online hate.

Identification and Characterization of Illegal Sales of Cannabis and Nicotine Delivery Products on Telegram Messaging Platform.

Unregulated and potentially illegal sales of tobacco, nicotine, and cannabis products have been detected on various social media platforms, e-commerce sites, online retailers, and the dark web. New end-to-end encrypted messaging services are popular among online users and present opportunities for marketing, trading, and selling of these products. The purpose of this study was to identify and characterize tobacco, nicotine, and cannabis selling activity on the messaging platform Telegram. The study was conducted in three phases: (1) identifying keywords related to tobacco, nicotine, and cannabis products for purposes of detecting Telegram groups and channel messages; (2) automated data collection from public Telegram groups; and (3) manual annotation and classification of messages engaged in marketing and selling products to consumers. Four keywords were identified ("Nicotine," "Vape," "Cannabis," and "Smoke") that yielded 20 Telegram groups with 262 506 active subscribers. Total volume of channel messages was 43 963 unique messages that included 3094 (7.04%) marketing/selling messages. The most commonly sold products in these groups were cannabis-derived products (83.25%, n = 2576), followed by tobacco/nicotine-derived products (6.46%, n = 200), and other illicit drugs (0.77%, n = 24). A variety of marketing tactics and a mix of seller accounts were observed, though most appeared to be individual suppliers. Telegram is an online messaging application that allows for custom group creation and global connectivity, but also includes unregulated activities associated with the sale of cannabis and nicotine delivery products. Greater attention is needed to conduct monitoring and enforcement on these emerging platforms for unregulated and potentially illegal cannabis and nicotine product sales direct-to-consumer. Based on study results, Telegram represents an emerging platform that enables a robust cannabis and nicotine-selling marketplace. As local, state, and national tobacco control regulations continue to advance sales restrictions and bans at the retail level, easily accessible and unregulated Internet-based channels must be further assessed to ensure that they do not act as conduits for exposure and access to unregulated or illegal cannabis and nicotine products.

Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms

BackgroundDigital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety. MethodsWe conducted an 8-week longitudinal usability test of the Small Steps SMS program, recruiting 20 participants who met criteria for major depressive disorder and/or generalized anxiety disorder. Participants used the automated intervention for 8 weeks and completed symptom severity and usability self-report surveys after 4 and 8 weeks of intervention use. Participants also completed individual interviews to provide feedback on the intervention. ResultsParticipants responded to automated messages on 70 % of study days and with 85 % of participants sending responses to messages in the 8th week of use. Usability surpassed established cutoffs for software that is considered acceptable. Depression symptom severity decreased significantly over the usability test, but reductions in anxiety symptoms were not significant. Participants noted key areas for improvement including addressing message volume, aligning message scheduling to individuals' availability, and increasing the customizability of content. LimitationsThis study does not contain a control group. ConclusionsAn 8-week automated interactive text messaging intervention, Small Steps SMS, demonstrates promise with regard to being a feasible, usable, and engaging method to deliver daily mental health support to individuals with symptoms of anxiety and depression.

Characterizing the Patterns of Electronic Health Record-Integrated Secure Messaging Use: Cross-Sectional Study.

Communication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden. This study aims to characterize the use of an electronic health record-integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system. We collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient). A total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P=.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages). Secure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors.

Identifying Common Topics in Patient Portal Messages with Unsupervised Natural Language Processing.

Patient portal messaging is an increasingly important form of communication between patients and medical providers. This has become particularly relevant in oncology, where patients undergo intense longitudinal treatments that require frequent communication regarding symptoms, appointments, and diagnostic results. The rise in the volume of these messages has significantly increased the workload of medical providers and consequent physician burn-out. Natural language processing (NLP), particularly transformer-based models, may offer an automated approach to characterize the content of patient messages and improve message triage and routing. In this study, we employed a state-of-the-art language model (Bidirectional Encoder Representations from Transformers; BERT) to identify data-derived categories of representative topics from real-world data thereby providing basic information to build an appropriate routing system. Patient-generated portal messages sent to a messaging pool for a single institution radiation oncology department from 2014 to 2023 were extracted. BERTopic, an NLP-based topic modeling technique based on BERT was optimized for topic modeling of patient messages. Uniform Manifold Approximation and Projection (UMAP) was used to reduce dimensionality and visualize topic relationships across messages. The BERTopic-identified topic categories were subsequently labeled manually by one of the physician investigators. Differences of number of messages over time were assessed using t-tests. A total of 47,492 messages were retrieved. The average number of messages per month from a single patient ranged from 1 to 18 (median 1.67, interquartile range 1.0-2.4). The total volume of patient messages showed a ten-fold increase over the study period, with 101 messages per month sent in 2014 and 999 messages per month in 2022 (p<0.001). BERTopic initially identified 35 topics whose relationships and degrees of overlap were visualized by UMAP. Due to physician-identified similarities, these topics were reduced into 13 categories. The most frequent topic category was messages about laboratory tests or imaging studies: 24.3%, followed by messages expressing appreciation: 18.9%, scheduling discussions: 15.6%, symptom-related messages: 11%, and treatment-related messages: 10.7%. Patient portal messages sent to a single institution radiation oncology department have increased dramatically in volume since implementation, corresponding to a broader national trend. NLP successfully identified common subject themes across patient messages, many of which are related to scheduling. This presents potential opportunities to apply NLP to automate message routing in the future.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype

BackgroundYoung adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). MethodsThe aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18–25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. ResultsForty-eight individuals ages 18–25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. ConclusionsYoung adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

Ctrl-C: a cross-sectional study of the electronic health record usage patterns of US oncology clinicians.

Despite some positive impact, the use of electronic health records (EHRs) has been associated with negative effects, such as emotional exhaustion. We sought to compare EHR use patterns for oncology vs nononcology medical specialists. In this cross-sectional study, we employed EHR usage data for 349 ambulatory health-care systems nationwide collected from the vendor Epic from January to August 2019. We compared note composition, message volume, and time in the EHR system for oncology vs nononcology clinicians. Compared with nononcology medical specialists, oncologists had a statistically significantly greater percentage of notes derived from Copy and Paste functions but less SmartPhrase use. They received more total EHR messages per day than other medical specialists, with a higher proportion of results and system-generated messages. Our results point to priorities for enhancing EHR systems to meet the needs of oncology clinicians, particularly as related to facilitating the complex documentation, results, and therapy involved in oncology care.

COVID exacerbated the gender disparity in physician electronic health record inbox burden.

The COVID-19 pandemic was associated with significant changes to the delivery of ambulatory care, including a dramatic increase in patient messages to physicians. While asynchronous messaging is a valuable communication modality for patients, a greater volume of patient messages is associated with burnout and decreased well-being for physicians. Given that women physicians experienced greater electronic health record (EHR) burden and received more patient messages pre-pandemic, there is concern that COVID may have exacerbated this disparity. Using EHR audit log data of ambulatory physicians at an academic medical center, we used a difference-in-differences framework to evaluate the impact of the pandemic on patient message volume and compare differences between men and women physicians. We found patient message volume increased post-COVID for all physicians, and women physicians saw an additional increase compared to men. Our results contribute to the growing evidence of different communication expectations for women physicians that contribute to the gender disparity in EHR burden.

Trends, Distribution, and Impact of Pediatric Messages in a Large Health System From 2019 to 2021

This cohort study assesses changes in the volume of medical advice messages between 2019 and 2021, variation among pediatricians, and the association of volume with time spent working on the electronic health record outside clinical hours.

A Grey Literature Review on Data Stream Processing applications testing

Context:The Data Stream Processing (DSP) approach focuses on real-time data processing by applying specific techniques for capturing and processing relevant data for on-the-fly results, i.e. without necessarily requiring prior storage. Like in any other software, testing plays a vital role in the quality assurance of DSP applications. However, testing such kind of software is not a simple task. In this context, some factors that make challenging testing are message temporality, parallelism, data volume, complex infrastructure, variability, and speed of messages. Objective:This work aims to map and synthesize industry knowledge and experience regarding DSP application testing. Specifically, we want to know about challenges, test purposes, test approaches, test data sources, and adopted tools. Method:To achieve the objective, we performed a Grey Literature Review (e.g., blog posts, white papers, discussion lists, lecture themes at technical events, professional social networks, software repositories, and other web-published) on testing DSP applications. We searched the grey literature using Google’s regular search engine in addition to specific searches on technical software development content websites. The selected studies were analyzed using qualitative and quantitative techniques. Results:Results are based on evidence from 154 selected sources. The challenges for testing DSP applications are the complexity of DSP applications, test infrastructure complexity, timing, and data acquisition issues. The main test objectives identified are functional suitability, performance efficiency, reliability, and maintainability. The main test approaches reported: Performance Testing, Regression Testing, Property-Based Testing, Chaos Testing, and Contract/Schema Testing. The strategies adopted by practitioners to obtain test data: Historical Data, Production Data Mirroring, Semi-Synthetic Data, and Synthetic Data. We also report 50 tools used in various testing activities, which are used for: automating infrastructure, generating test data, test utilities, dealing with timing issues, load generation, simulation, and others. Furthermore, we identified gaps and opportunities for future scientific work. Conclusion:This work selected and summarized content produced by practitioners regarding DSP application testing. We identified that knowledge, techniques, and tools intrinsic to the practice were not present in the formal literature, so this study helps reduce the gap between industry and academia on this topic. The document has delivered benefits to industry practitioners and academic researchers.

Efficient Non-DHT-Based RC-Based Architecture for Fog Computing in Healthcare 4.0

Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems.

PATRIC: A high performance parallel urban transport simulation framework based on traffic clustering

Parallel traffic simulation requires partitioning the road network into several components that can be assigned to different computing nodes (CPNs). Existing studies focus more on reducing edge-cuts (message-passing pipes between CPNs) to decrease synchronization message amongst CPNs for efficiency improvement. However, even reducing edge-cuts drastically, the volume of messages transmitted might still be high, which does not significantly improve performance. Based on observation that some traffic clusters (TCs) exist during simulation, i.e., areas with high internal and low external traffic density. For high-performance urban transport simulation, we propose a data-driven parallel approach named PATRIC, which can generate parallel partitions automatically based on traffic clustering. Specifically, the TC-based automatic partitioner (TAP) is designed to automatically identify TCs and then construct partitions in parallel. We present a partition-growing algorithm that prevents traffic-intensive TCs being split across multiple CPNs when distributing computing workloads, resulting in more balanced load and fewer synchronization operations. Unlike prior work using fixed thresholds for load balancing, we develop the adaptive partition updater (APU) to fit the dynamic traffic in the road network, which achieves a better trade-off between balancing workload and lowering communication for higher efficiency. Experiments on real-world datasets demonstrate that our approach outperforms the state-of-the-art methods.