Reliable Message Research Articles

Performance simulation analysis of new train control system safety communication protocol based on Matlab/SimEvents

The vehicle-ground safety communication of the New Train Control System adopts a brand-new underlying network and a communication mechanism of safe and reliable transmission. Based on the GPRS packet switching network, the data link layer adopts the PPP protocol, and the TCP protocol is adopted in the transmission layer to provide reliable transmission services. According to the GPRS network service quality index and the retransmission mechanism of the TCP protocol in the transport layer, this paper adopts the Matlab/SimEvents discrete event simulation method and uses the entity as the communication carrier to conduct a hierarchical and modular system-level modeling of New Train Control System vehicle-ground communication protocol. It simulates the communication process of the message from the application layer, the security layer to the transport layer, and finally transmitted on the GPRS network channel with random delay and packet loss probability. From the quantitative point of view, this paper analyzes the safety connection establishment time and success rate of the new train control system vehicle-ground wireless communication under different channel conditions, the timeliness of the non-periodic message receiving a reply to complete the specific scene function, and the reliability of the periodic message receiving delay. The analysis results show that the new train control system vehicle-ground wireless communication adopts GPRS network and TCP protocol retransmission mechanism to improve the real-time and reliability of communication.

Optimizing Bulk Transfer Size and Scheduling for Efficient Buffer Management in Mobile Opportunistic Networks

Mobile Opportunistic Networks (MONs) are characterized by intermittent connectivity with long isolation period, and nodes following redundant transmissions for reliable message delivery. This often leads to unnecessary buffer occupancy, preventing new messages from getting replicated due to small contact duration and low bandwidth, or leading to packet drop under constrained buffer. Although attempts have been made to mitigate buffer congestion, the existing schemes are localized, are slow to react, or are specific to a routing scheme. Moreover, they rely on message exchanges to obtain buffer state/occupancy, thereby incurring additional overhead. In this paper, we first develop a generalized probabilistic forwarding model where the forwarding probability denotes the likelihood of a message to get forwarded to the encountered node. Based on the forwarding probability, we develop a congestion indicator and predict the point of congestion using the Kalman filter. Using this, a node can decide the optimal number and the exact set of messages to replicate, which leads to an optimal performance with minimal packet drop and overhead. Simulation results using a synthetic mobility model and a real-life mobility trace show that the proposed scheme outperforms the existing schemes.

"Maternal Vaccination Greatly Depends on Your Trust in the Healthcare System": A Qualitative Study on the Acceptability of Maternal Vaccines among Pregnant Women and Healthcare Workers in Barcelona, Spain.

The World Health Organization (WHO) identified vaccine hesitancy as one of the top 10 threats to global health in 2019. Health promotion and education have been seen to improve knowledge and uptake of vaccinations in pregnancy. This qualitative study was conducted based on phenomenology, a methodological approach to understand first-hand experiences, and grounded theory, an inductive approach to analyse data, where theoretical generalisations emerge. Data were collected through semi-structured interviews with pregnant women attending antenatal care services and healthcare workers (HCWs) in Barcelona, Spain. Interviews were audio-recorded, transcribed, and coded, and notes were taken. Inductive thematic analysis was performed, and data were manually coded. Pertussis was reported as the most trusted vaccine among pregnant women due to its long-standing background as a recommended vaccine in pregnancy. The influenza vaccine was regarded as less important since it was perceived to cause mild disease. The COVID-19 vaccine was the least trustworthy for pregnant women due to uncertainties about effectiveness, health effects in the mid- and long-term, the fast development of the vaccine mRNA technology, and the perceptions of limited data on vaccine safety. However, the necessity to be vaccinated was justified by pregnant women due to the exceptional circumstances of the COVID-19 pandemic. The recommendations provided by HCW and the established relationship between the HCW, particularly midwives, and pregnant women were the main factors affecting decision-making. The role of mass media was perceived as key to helping provide reliable messages about the need for vaccines during pregnancy. Overall, vaccines administered during pregnancy were perceived as great tools associated with better health and improved quality of life. Pregnancy was envisioned as a vulnerable period in women's lives that required risk-benefits assessments for decision-making about maternal vaccinations. A holistic approach involving the community and society was considered crucial for health education regarding maternal vaccines in support of the work conducted by HCWs.

CPDD: Clustering and Probabilistic based Data Dissemination in Vehicular Adhoc Networks

Objectives: To maintain Quality of Service (QOS) for data dissemination, prevent packet loss and high reachability. Methods: To achieve this, CPDD: Clustering and Probabilistic based Data Dissemination in Vehicular Adhoc Networks is proposed. The messages are disseminated between the clusters. The Performance metrics like throughput, jitter, Normalized Routing Load, Routing Overhead and packet loss ratio are analysed using NS2 and the results are compared for the varying number of nodes. Findings: The proposed approach has a low packet drop ratio and end-to-end delay compared to SECADD, SEAD and S1PD. The efficiency of the traffic system and the quality of driving are both improved by data dissemination through VANETs by using CPDD. The goal of an effective clustering method is to reduce the number of clusters while maintaining high communication over the vehicular network. Novelty: Establishing effective communication between two fastmoving vehicles is a major challenge in VANET. The challenge in designing a robust and reliable message dissemination technique is required for VANET safety applications. The results of the proposed model CPDD is compared for different numbers of nodes. The proposed work improves the QOS metrics with the Normalized Routing Load of 1.009%, Routing Overhead of 1.03 bits/sec, throughput of 205.5 bits/sec, end-to-end delay of 0.435 seconds and packet loss ratio of 20.02% for 50 nodes. Keywords: Broadcasting; CPDD; Routing Overhead; Dissemination; Clustering; VANET

ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes' energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.

Blossom: Cluster-Based Routing for Preserving Privacy in Opportunistic Networks

Opportunistic networks are an enabler technology for typologies without centralized infrastructure. Portable devices, such as wearable and embedded mobile systems, send relay messages to the communication range devices. One of the most critical challenges is to find the optimal route in these networks while at the same time preserving privacy for the participants of the network. Addressing this challenge, we presented a novel routing algorithm based on device clusters, reducing the overall message load and increasing network performance. At the same time, possibly identifying information of network nodes is eliminated by cloaking to meet privacy requirements. We evaluated our routing algorithm in terms of efficiency and privacy in opportunistic networks of traditional and structured cities, i.e., Venice and San Francisco by comparing our approach against the PRoPHET, First Contact, and Epidemic routing algorithms. In the San Francisco and Venice scenarios, Blossom improves messages delivery probability and outperforms PRoPHET, First Contact, and Epidemic by 46%, 100%, and 160% and by 67%, 78%, and 204%, respectively. In addition, the dropped messages probability in Blossom decreased 83% compared to PRoPHET and Epidemic in San Francisco and 91% compared to PRoPHET and Epidemic in Venice. Due to the small number of messages generated, the network overhead in this algorithm is close to zero. The network overhead can be significantly reduced by clustering while maintaining a reliable message delivery.

Adaptive scheduled partitioning technique for reliable emergency message broadcasting in VANET for intelligent transportation systems

This paper aims to enable accurate and reliable emergency message broadcast in Vehicular Ad hoc Network (VANET). The VANET is the most common topology used in Intelligent Transportation Systems (ITS), where changes in standard topology due to the mobility of nodes create challenges in broadcasting the emergency message and efficient data delivery in both highway and urban scenarios. The main problems in urban scenarios are channel contention, message redundancy and road structure. To obtain information, broadcast protocols for VANET typically use beacon messages, which are distributed among the vehicles. When multiple vehicles transmit messages at the same time, a broadcast storm occurs and vehicles experience message delivery failure. To address this problem, Adaptive Scheduled Partitioning and Broadcasting Technique (ASPBT) for emergency message broadcast and beacon retransmissions for message reliability were proposed. This protocol dynamically modifies several partitions and beacon periodicity to reduce the number of retransmissions. Later, the partition size is determined by estimating the network transmission density of each partition schedule via the Black Widow Optimization (BWO) algorithm is proposed. The simulation is carried out with different network densities at the vehicle speed of 110 km/h, a direct path length of 12 km under a four-way direction path and performance analysis was performed.

Adaptive Propagation Graph Convolutional Networks Based on Attention Mechanism

The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central node to obtain messages regarding its neighbors and update itself, so that it can express deep-layer features. Because the graph structure data have no local translation invariance, the number of neighbors of each central node is different, and there is no order, there are two difficulties: (1) how to design a reliable message propagation method to better express all network topologies; (2) how to design a feature aggregation function so that it can weigh local features and global features. In this paper, a new adaptive propagation graph convolutional network model based on the attention mechanism (APAT-GCN) is proposed, which enables GNNs to adaptively complete the process of message propagation and feature aggregation, according to the neighbors of the central node, and set the influence degree of local and global messages on the aggregation of the central node. Compared with other classical models, this method is superior to the baseline model and can improve the accuracy of node- and graph-level classification tasks in downstream tasks.

Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

Assessing the numbers and distribution of at-risk megafauna such as the black rhino (Diceros bicornis) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals (e.g., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework has a lower hardware cost and can function with a reduced internet bandwidth requirement for local network communication. It proposes the use of a Jetson Xavier NX, onboard a Parrot Anafi drone, connected to the internet throughout the flight to deliver a lightweight web-based notification system upon detection of the target species. The GPS location with the detected target species images is sent using MQ Telemetry Transport (MQTT), a lightweight messaging protocol using a publisher/subscriber architecture for IoT devices. It provides reliable message delivery when internet connection is sporadic. We used a YOLOv5l6 object detection architecture trained to identify a bounding box for one of five objects of interest in a frame of video. At an intersection over union (IoU) threshold of 0.5, our model achieved an average precision (AP) of 0.81 for black rhino (our primary target) and 0.83 for giraffe (Giraffa giraffa). The model was less successful at identifying the other smaller objects which were not our primary targets: 0.34, 0.25, and 0.42 for ostrich (Struthio camelus australis), springbok (Antidorcas marsupialis) and human respectively. We used several techniques to optimize performance and overcome the inherent challenge of small objects (animals) in the data. Although our primary focus for the development of the model was rhino, we included other species classes to emulate field conditions where many animal species are encountered, and thus reduce the false positive occurrence rate for rhino detections. To constrain model overfitting, we trained the model on a dataset with varied terrain, angle and lighting conditions and used data augmentation techniques (i.e., GANs). We used image tiling and a relatively larger (i.e., higher resolution) image input size to compensate for the difficulty faced in detecting small objects when using YOLO. In this study, we demonstrated the potential of a drone-based AI pipeline model to automate the detection of free-ranging megafauna detection in a remote setting and create alerts to a wildlife manager in a relatively poorly connected field environment.

The local wisdom-based disaster mitigation literacy of the indigenous Pangandaran community

Disaster mitigation is a series of efforts to reduce disaster risks through physical development, awareness, and capacity building to face disaster threats. Appropriate communication strategies are needed to increase awareness and the ability to cope with disasters. Delivering appropriate, accurate, and reliable messages can build a reliable disaster literacy community. This study aimed to examine disaster mitigation literacy based on the local wisdom of indigenous people in Pangandaran Regency, West Java. The descriptive qualitative method was employed to explain the phenomena and events occuring in the field comprehensively. Data were collected through interviews, observation, and literature study. Meanwhile, the data were analysed systematically through reduction, presentation, and conclusion stages. The key informants were traditional figures in Badud and Pananjung Villages. The results showed that some traditions contain moral, social, and spiritual messages about disaster mitigation. This study concludes that the local wisdom of the Pangandaran community is an appropriate communication strategy to build disaster mitigation literacy.

Peculiarities of using open data in preparing marketing and commercial management decisions

The article examines the features of the use of data from open sources of information, special approaches and techniques in the system of marketing research of enterprises. Improving approaches to integration into modern marketing information systems of modern content analysis tools have been developed and tested, which organically provides constructive synergy of applied methods and means of neuro-fuzzy modeling and clustering of information arrays, statistical analysis of information units including potential and actual consumers. The modern methodological tools of machine learning were tested on the basis of the author’s approaches to detecting "fakes" (unreliable information) in the formation of situational awareness of company management, identifying trends in target markets, analytical processing of changes associated with high risks and uncertainties for business. It is shown that the great variability of the modern information environment (data, content) creates significant prerequisites and significant combinatorial opportunities for generating distorted information in different ways, as well as the dissemination of the latter. Possibilities for detecting inaccurate information (fakes) in the information field of a particular product market have been worked out. Comparison of the results of different models based on the confusion matrix showed that two of the four learning models, namely the neural network and the "random forest" model, did well enough to assess the reliability ("fake") messages. The recommendations on the organization of the formation of source data from open sources of information, improving the quality and reliability of their processing and successful integration of marketing and commercial analytics systems have been constructively developed.

H^2-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis

Current representation learning methods for whole slide image (WSI) with pyramidal resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale and heterogeneous diagnostic information of different structures for comprehensive analysis. This paper presents a novel graph neural network-based multiple instance learning framework (i.e., H^2-MIL) to learn hierarchical representation from a heterogeneous graph with different resolutions for WSI analysis. A heterogeneous graph with the “resolution” attribute is constructed to explicitly model the feature and spatial-scaling relationship of multi-resolution patches. We then design a novel resolution-aware attention convolution (RAConv) block to learn compact yet discriminative representation from the graph, which tackles the heterogeneity of node neighbors with different resolutions and yields more reliable message passing. More importantly, to explore the task-related structured information of WSI pyramid, we elaborately design a novel iterative hierarchical pooling (IHPool) module to progressively aggregate the heterogeneous graph based on scaling relationships of different nodes. We evaluated our method on two public WSI datasets from the TCGA project, i.e., esophageal cancer and kidney cancer. Experimental results show that our method clearly outperforms the state-of-the-art methods on both tumor typing and staging tasks.

Scaling analysis of the Telegram Open Network blockchain project

Blockchain systems are always distributed but differ greatly in resolutions, sizes, roles, transparency, types of participants, and the way transactions are handled. The decentralized structure offers inalienable security benefits, but with a large number of participants faces the problem of limited scaling. Sharding is suggested to solve this problem. Sharding is a way to divide consensual workload and transactions into several nodes. Effective application of sharding requires a lot of detail and work in terms of architecture and system implementation. As was found in the process of the work, in order to achieve significant results in scaling the blockchain system through sharding, it is necessary that the blockchain project is based on several blockchains simultaneously (i.e. had a multi-blockchain architecture) and provides infrastructure for fast and reliable messaging between them (i.e. strongly interconnected). The Telegram Open Network (TON) and its component TON Blockchain are considered an example of such a project.
 This work provides a comparative analysis of the most popular blockchain platforms (Bitcoin and Ethereum) with the implementations of TON Blockchain ideas (The Open Network and Everscale) on three parameters: system bandwidth (measured at transactions per second, abbreviated TPS), the average time of a new block’s appearance in the network and projected bandwidth in TPS.

A position-based reliable emergency message routing scheme for road safety in VANETs

Reliable emergency message (EM) transmission in vehicular adhoc networks (VANETs) necessitates an effective routing scheme. Position-based routing is considered more suitable for VANETs for not having to maintain any routing table or sharing connection states with neighbors. However, position-based routing is challenging in VANETs because vehicles change their positions instantly, and the next-hop can often go out of the communication range in greedy forwarding mode. This unstable behavior of the next-hop triggers route redundancy and leads to a high end-to-end delay (ED) and lower packet delivery ratio (PDR). Moreover, routing decisions based on a next-hop (relay) vehicle may be less optimal if we do not consider the stability and predict the position of a next-hop vehicle in such dynamic environments. To that end, we propose a position-based reliable emergency message routing (REMR) scheme based on our mobility metrics, which exploits the vehicle moving behaviors to enhance EM delivery. We describe how the choice of next-hop in greedy forwarding can be enhanced by leveraging neighbor’s future location information. By taking into account the Euclidean distance and position information, REMR predicts the relative positions of neighbor vehicles to exclude unstable neighbors from the list of candidate next-hops. In addition, REMR employs the vehicles’ movement information (e.g., position, speed variation, and moving angle) to minimize a possible link disruption and to choose an optimal next-hop for robust routing of EMs. REMR also offers a beaconing control strategy to enhance message reliability and to deal with the problem of beacons congestion. To minimize beacons congestion, REMR adjusts the beacon interval based on the neighborhood density. By consolidating mobility metrics and beacon control strategy, REMR can respond adequately to variation in the network traffic and frequent topology changes as validated by our simulation results.

Survey Paper on Communication System Using Blockchain and Cryptography

Abstract: Decentralized application make use of peer-to-peer networks, this ensures that no network failure can occur due to central node failure. Blockchain serves as a ledger which allows messaging to take place in a decentralized manner. Decentralized application for the communication and resource sharing is need in today’s world, where keeping data on a centralized server can be risky and a very expensive experience. With the help of some consensus, we can implement different ways to share resources and communicate. Together with Blockchain and Decentralized Applications, we can create a secure and very reliable messaging application that overcomes the drawbacks of traditional messaging applications. Keywords: Blockchain, Cryptography, Communication System, Secure Hash Algorithm, Encryption and Decryption

Enhanced Reliable Communication Using Direct-Trust-Based GPSR Protocol in VANETs

Vehicular Ad-hoc Network (VANET) is becoming a potential solution to improve traffic safety and efficiency by facilitating communication among vehicles via exchanging reliable messages. Therefore, reliable and authentic message delivery from legitimate vehicles is a prerequisite. But internal malicious vehicles may disrupt proper functioning by discarding or fabricating packets. For this objective, a computationally efficient direct trust-based model is proposed which evaluate vehicle behavior by verifying the correctness of communicated message even in sparse network situation. We ensure the reliability of each communicated message by secure path selection in Direct-trust-based GPSR routing protocol (DT-GPSR) incorporating trustworthy vehicle. Simulation and analyses show our model help legitimate vehicles to mitigate effect of malicious vehicle communicating false or bogus data to disrupt network.

Integrating AI Microservices into Hard-Real-Time SoS to Ensure Trustworthiness of Digital Enterprise Using Mission Engineering

Due to the increased complexities and operating speeds of today’s and tomorrow’s system-of-systems (SoS) architectural configurations for digital enterprises, the design of new domain architecture management systems is required. A key element of these new designs will be the incorporation of Artificial Intelligence (AI) microservices to provide dynamically containerized and orchestrated service capabilities within a lightweight interoperability fabric with the ability to operate in hard-real-time environments. Each containerized AI microservice exposes an independent, programmable function, which enables it to be easily reused, evolved, or replaced without compromising interoperability across critical mission essential functions to execute mission threads. In addition, embedded in this design needs to be a trust management layer to enforce reliable messaging and trust amongst the actors. This paper provides a framework for planned research and demonstrates the feasibility of microservices using a representative simple problem to demonstrate the application of the framework. Early positive analysis results using AI microservices within an SoS environment shows that the 500 milli-second (ms) threshold for latency can be met.

MQTT based Secure Transport Layer Communication for Mutual Authentication in IoT Network

Recent advancements in the communication protocols and the networking technologies have enabled connectivity of a wide range of objects, resulting in the Internet of Things (IoT) network. The protocols like MQ Telemetry Transport (MQTT), as well as Constrained Application Protocol (CoAP) are moderately capable of providing the management of heterogeneous wireless sensor networks even in an environment with very limited bandwidth. In this paper, we develop a lightweight encryption algorithm to obtain reliable secure data transmission between IoT devices. We propose a Secure Reliable Message Communication (SEC-RMC) protocol using Mosquitto MQTT message broker with cryptographic enhancements to offer security services and also provide the mutual authentication in the IoT environment at the transport layer. The proposed scheme decreases the number of messages transmitted between the devices. Also, the authentication scheme provides resistance to DNS hacking, routing table poisoning and packet mistreatment. On comparison with the existing methods, the transmission time has been reduced by 80% in this work.

Joint Communication and Binary State Detection

We study a basic joint communication and sensing setup from an information theoretic perspective. A transmitter sends an encoded message through a pair of noisy channels connected to a receiver and a sensor. The receiver is interested in decoding the message from its noisy observation. The sensor has access to the message as side information, and instead is interested in estimating an unknown yet fixed binary state of its channel. This basic setup models scenarios where a wireless transceiver broadcasts an information bearing waveform, to be decoded by possibly more than one receiver, and then observes a modulated echo of the waveform from which it wishes to detect an element or object in the environment. We consider binary symmetric and Gaussian settings with multiplicative binary states, and establish the fundamental trade-off between reliable message communication and efficient state detection in these settings. This trade-off is captured by the message communication rate against the state detection error exponent. The results give insights into the benefits of carrying out communication and sensing jointly.

Radial Access Simulation Using Mentice

Text message–based interventions have been demonstrated to be a valuable monitoring tool across various conditions. Here, we aimed to describe our early experience using a newly developed text message–based platform designed to track symptoms in spine surgery patients.We used the Informed Mindset Medical (IMM) platform to automatically send text messages with secure and encrypted hyperlinks to enrolled patients. Patient symptoms were monitored using well-standardized functional assessments. Limited patient data and responses were stored on a Health Insurance Portability and Accountability Act–compliant SQL cloud-based server database.In 3 months, 101 patients scheduled for elective spine surgery accepted participation in our pilot study. Overall, 71.2% of the enrolled patients responded to at least 1 preoperative baseline questionnaire. The response rates were similar across attendings, questionnaire bundles (cervical vs. thoracolumbar), genders, and age groups. The overall preoperative IMM pain scores were found to correlate positively with the preoperative electronic medical record pain rates. Similarly, the overall preoperative IMM and electronic medical record pain scores correlated positively with the IMM-collected Neck Disability Index/Oswestry Disability Index scores. From an initial 71.2%, the response rate decreased to 54.9% for the 6-week follow-up questionnaires.Our preliminary findings support the reliability of this text message–based strategy to monitor symptoms in spine surgery patients. Further studies are warranted to explore strategies to increase the response rate and expand this platform's clinical and research applicability.