Search Results (25)

Efficient clustering algorithms are critical for enabling the timely dissemination of emergency messages across maximum coverage areas in vehicular networks. While existing clustering approaches demonstrate stability and scalability, there has been a limited amount of work focused on leveraging roadside units (RSUs) for cluster head selection. This research proposes a novel framework that utilizes RSUs to facilitate cluster head election, mitigating the cluster head selection process, clustering overhead, and broadcast storm problem. The proposed scheme mandates selecting an optimal number of cluster heads to maximize information coverage and prevent traffic congestion, thereby enhancing the quality of service through improved cluster head duration, reduced cluster formation time, expanded coverage area, and decreased overhead. The framework comprises three key components: (I) an acknowledgment-based system for legitimate vehicle entry into the RSU for cluster head selection; (II) an authoritative node behavior mechanism for choosing cluster heads from received notifications; and (III) the role of bridge nodes in maximizing the coverage of the established network. The comparative analysis evaluates the clustering framework’s performance under uniform and non-uniform vehicle speed scenarios for time-barrier-based emergency message dissemination in vehicular ad hoc networks. The results demonstrate that the proposed model’s effectiveness for uniform highway speed scenarios is 100% whereas for non-uniform scenarios 99.55% information coverage is obtained. Furthermore, the clustering process accelerates by over 50%, decreasing overhead and reducing cluster head election time using RSUs. The proposed approach outperforms existing methods for the number of cluster heads, cluster head election time, total cluster formation time, and maximum information coverage across varying vehicle densities. Full article

The Ang Pagtanom og Binhi Project is a University–Community partnership and community-based participatory research project exploring the health benefits of food sovereignty practices in the Philippines. In late 2021, in the midst of data collection, Super Typhoon Odette made landfall in the Philippines causing massive environmental and structural devastation. In the aftermath of the storm, community partners in the Philippines and members of the research team in the United States shared photos, texts, and updates. These messages included descriptions of structural and environmental damage caused by the storm and stories of mutual aid efforts and actions taken by individuals and small organizations, each highlighting connections between food sovereignty efforts in the Philippines and the impacts of climate change. Due to the richness of the stories, the interconnectedness between these conversations and the research topic, and the alignment within the theoretical foundations of the project, the researchers understood that these communications should be included as data. With feedback from the Community Advisory Board, the Research and Design Team amended project protocols, research questions, and consent forms to incorporate this emergent data. This manuscript describes the process that the team undertook and some of the lessons learned by taking this approach. Full article

Background and Objectives: The coronavirus disease of 2019 (COVID-19) pandemic has posed a serious threat to humanity and is considered a global health emergency. Antimalarial drugs (ADs) have been used in the treatment of immuno-inflammatory arthritis (IIA) and coronavirus infection (COVID-19). The aim of this review is to analyze the current knowledge about the immunomodulatory and antiviral mechanisms of action, characteristics of use, and side effects of antimalarial drugs. Material and Methods: A literature search was carried out using PubMed, MEDLINE, SCOPUS, and Google Scholar databases. The inclusion criteria were the results of randomized and cohort studies, meta-analyses, systematic reviews, and original full-text manuscripts in the English language containing statistically confirmed conclusions. The exclusion criteria were summary reports, newspaper articles, and personal messages. Qualitative methods were used for theoretical knowledge on antimalarial drug usage in AIRDs and SARS-CoV-2 such as a summarization of the literature and a comparison of the treatment methods. Results: The ADs were considered a “candidate” for the therapy of a new coronavirus infection due to mechanisms of antiviral activity, such as interactions with endocytic pathways, the prevention of glycosylation of the ACE2 receptors, blocking sialic acid receptors, and reducing the manifestations of cytokine storms. The majority of clinical trials suggest no role of antimalarial drugs in COVID-19 treatment or prevention. These circumstances do not allow for their use in the treatment and prevention of COVID-19. Conclusions: The mechanisms of hydroxychloroquine are related to potential cardiotoxic manifestations and demonstrate potential adverse effects when used for COVID-19. Furthermore, the need for high doses in the treatment of viral infections increases the likelihood of gastrointestinal side effects, the prolongation of QT, and retinopathy. Large randomized clinical trials (RCTs) have refuted the fact that there is a positive effect on the course and results of COVID-19. Full article

A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources. Full article

In the context of utilizing BeiDou short-message communication (SMC) for transmitting maritime safety information, challenges arise regarding information redundancy and limited message length. To address these issues, compressing the data content of SMC becomes essential. This paper proposes a dual-stage compression model based on Beidou SMC for compressing maritime safety information, aiming to achieve efficient compression and reduce information redundancy. In the first stage, a binary encoding method (MBE) specifically designed for maritime safety information is proposed to optimize the byte space of the short messages, ensuring the accuracy, integrity, and reliability of the information. In the second stage, we propose a data compression algorithm called XH based on a hash dictionary, which efficiently compresses maritime safety information and reduces information redundancy. Different maritime data have corresponding structures and compositions, which can have a significant impact on the evaluation of compression algorithms. We create a database considering six categories of objects: waves, sea ice, tsunamis, storms, weather, and navigation warnings. Experimental results demonstrate that the proposed model achieves significant compression efficiency and performance on the maritime safety data set, outperforming other benchmark algorithms. Full article

This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. In particular, the framework is considered in the context of a study case regarding Internet of Things (IoT) devices to be attached to rolling stock in a railway. A lack of computation and storage resources available in edge devices and infrequent network connectivity are not often seen in the existing literature, but were considered in this paper. Two distinct implementations of IRONEDGE were considered and tested. One, identified as Apache Kafka with Kafka Connect (K0-WC), uses Kafka Connect to pass messages from MQ Telemetry Transport (MQTT) to Apache Kafka. The second scenario, identified as Apache Kafka with No Kafka Connect (K1-NC), allows Apache Storm to consume messages directly. When the data rate increased, K0-WC showed low throughput resulting from high losses, whereas K1-NC displayed an increase in throughput, but did not match the input rate for the Data Reports. The results showed that the framework can be used for defining new solutions for edge Stream Processing scenarios and identified a reference implementation for the considered study case. In future work, the authors propose to extend the evaluation of the architectural variation of K1-NC. Full article

A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number of automobile connections in the car communications system. To ensure a high level of service and avoid unsafe situations brought on by congestion or a broadcast storm, data dissemination in a VANET network requires an effective approach. Effective multi-objective optimization methods are required to tackle this because of the implied competing nature of multi-metric approaches. A meta-heuristic technique with a high level of solution interactions can handle efficient optimization. To accomplish this, a meta-heuristic search algorithm particle optimization was chosen. In this paper, we have created a network consisting of vehicles as nodes. The aim is to send emergency messages immediately to the stationary nodes. The normal messages will be sent to the FIFO queue. To send these messages to a destination node, multiple routes were found using Time delay-based Multipath Routing (TMR) method, and to find the optimal and secure path Particle Swarm Optimization (PSO) is used. Our method is compared with different optimization methods such as Ant Colony Optimization (ACO), Firefly Optimization (FFO), and Enhanced Flying Ant Colony Optimization (EFACO). Significant improvements in terms of throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and the energy consumption are revealed by the experimental results. Full article

Economically disadvantaged coastal communities face severe damage and casualties, which can be attributed to storm surges. Excessive amounts of inundation should be considered to a similar level to wind speeds and heavy rains that communities commonly prepare for amidst a hurricane event. Marginalized residents, such as residents of color, disabled residents, elderly residents, and residents occupying low-income housing, suffer from storm surge events. Coastal resiliency plans are bottlenecked by factors, such as residential stability, ability to relocate, and insurance coverage, all of which are inequitably constrained for marginalized communities. This exploratory study reviews the previous literature on wireless emergency alert (WEA) equity critiques and spatial analysis of the WEAs sent to coastal Virginia communities. Two research questions are explored in this paper: (1) How does the previous literature critique equity in wireless emergency alerts? (2) How many households are below the poverty line in areas where storm surge warnings have been sent? To improve the utilization of WEAs for the protection of low-income community members, there is evidence to support the increase in the frequency of message delivery and improving the call-to-action text. This paper sets the stage for future policy analyses and message design experimentation on emergency communication in coastal regions. Full article

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM${}^3$S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM${}^3$S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM${}^3$S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM${}^3$S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM${}^3$S version. The generality of this new RAM${}^3$S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM${}^3$S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams. Full article

In this paper, we target dissemination issues of emergency messages in a highly dynamic Internet of Vehicles (IoV) network. IoV is emerging as a new class of vehicular networks to optimize road safety as well as users’ comfort. In such a context, forwarding emergency messages through vehicle-to-vehicle communications (V2V) plays a vital role in enabling road safety-related applications. For instance, when an accident occurs, forwarding such information in real time will help to avoid other accidents in addition to avoiding congestion of network traffic. Thus, dissemination of emergency information is a major concern. However, on the one hand, vehicle density has increased in the last decade which may lead to several issues including message collisions, broadcast storm, and the problem of hidden nodes. On the other hand, high mobility of vehicles and hence dynamic changes of network topology result in failure of dissemination of emergency packets. To overcome these problems, we propose a new dissemination scheme of emergency packets by vehicles equipped with both DSRC and cellular LTE wireless communication capabilities. Our scheme is based on a dynamic clustering strategy, which includes a new cluster head selection algorithm to deal with the broadcast storm problem. Furthermore, our selection algorithm enables not only the election of the most stable vehicles as cluster heads, and hence their exploitation in forwarding the emergency information, but also the avoidance of packet collisions. We simulated our scheme in an urban environment and compared it with other data dissemination schemes. Obtained results show the efficiency of our scheme in minimizing collision and broadcast storm problems, while improving latency, packet delivery ratio and data throughput, as compared to other schemes. Full article

Vehicle platooning reduces the safety distance between vehicles and the travel time of vehicles so that it leads to an increase in road capacity and to saving fuel consumption. In Europe, many projects for vehicle platooning are being actively developed, but mostly focus on truck platooning on the highway with a simpler topology than that of the urban road. When an existing vehicle platoon is applied to urban roads, many challenges are more complicated to address than highways. They include complex topology, various routes, traffic signals, intersections, frequent lane change, and communication interference depending on a higher vehicle density. To address these challenges, we propose a distributed urban platooning protocol (DUPP) that enables high mobility and maximizes flexibility for driving vehicles to conduct urban platooning in a decentralized manner. DUPP has simple procedures to perform platooning maneuvers and does not require explicit conforming for the completion of platooning maneuvers. Since DUPP mainly operates on a service channel, it does not cause negative side effects on the exchange of basic safety messages on a control channel. Moreover, DUPP does not generate any data propagation delay due to contention-based channel access since it guarantees sequential data transmission opportunities for urban platooning vehicles. Finally, to address a problem of the broadcast storm while vehicles notify detected road events, DUPP performs forwarder selection using an analytic hierarchy process. The performance of the proposed DUPP is compared with that of ENSEMBLE which is the latest European platooning project in terms of the travel time of vehicles, the lifetime of an urban platoon, the success ratio of a designed maneuver, the external cost and the periodicity of the urban platooning-related transmissions, the adaptability of an urban platoon, and the forwarder selection ratio for each vehicle. The results of the performance evaluation demonstrate that the proposed DUPP is well suited to dynamic urban environments by maintaining a vehicle platoon as stable as possible after DUPP flexibly and quickly forms a vehicle platoon without the support of a centralized node. Full article

In Vehicular Adhoc Networks (VANETs), disseminating Emergency Messages (EMs) to a maximum number of vehicles with low latency and low packet loss is critical for road safety. However, avoiding the broadcast storm and dealing with large-scale dissemination of EMs in urban VANETs, particularly at intersections, are the challenging tasks. The problems become even more challenging in a dense network. We propose an Effective Emergency Message Dissemination Scheme (EEMDS) for urban VANETs. The scheme is based on our mobility metrics to avoid communication overhead and to maintain a stable cluster structure. Every vehicle takes into account its direction angle and path loss factor for selecting a suitable cluster head. Moreover, we introduce estimated link stability to choose a suitable relay vehicle that reduces the number of rebroadcasts and communication congestion in the network. Simulation results show that EEMDS provides an acceptable end-to-end delay, information coverage, and packet delivery ratio compared to the eminent EM dissemination schemes. Full article

Distributed stream processing engines (DSPEs) deploy multiple tasks on distributed servers to process data streams in real time. Many DSPEs have provided locality-aware stream partitioning (LSP) methods to reduce network communication costs. However, an even job scheduler provided by DSPEs deploys tasks far away from each other on the distributed servers, which cannot use the LSP properly. In this paper, we propose a Locality/Fairness-aware job scheduler (L/F job scheduler) that considers locality together to solve problems of the even job scheduler that only considers fairness. First, the L/F job scheduler increases cohesion of contiguous tasks that require message transmissions for the locality. At the same time, it reduces coupling of parallel tasks that do not require message transmissions for the fairness. Next, we connect the contiguous tasks into a stream pipeline and evenly deploy stream pipelines to the distributed servers so that the L/F job scheduler achieves high cohesion and low coupling. Finally, we implement the proposed L/F job scheduler in Apache Storm, a representative DSPE, and evaluate it in both synthetic and real-world workloads. Experimental results show that the L/F job scheduler is similar in throughput compared to the even job scheduler, but latency is significantly improved by up to 139.2% for the LSP applications and by up to 140.7% even for the non-LSP applications. The L/F job scheduler also improves latency by 19.58% and 12.13%, respectively, in two real-world workloads. These results indicate that our L/F job scheduler provides superior processing performance for the DSPE applications. Full article

Climate change makes coastal communities more vulnerable to floods associated with storm surges and sea level rise, requiring both adaptation and mitigation measures. Moreover, proper understanding of flood risks and their potential impacts on climate change appears to be a communication challenge. In climate change communication, the effect of framing congruency on perception of risk, efficacy and behavioural intentions towards climate change adaptation and mitigation has received limited attention. Messages have not been congruent in framing risks associated with climate change. We define congruency as the coherent alignment of several aspects of message content. Messages are considered congruent when they provide recipients with consistent contents such as giving concrete and actionable advice, or by providing more abstract and general background information. This research focuses on climate change communication in fostering mitigation behaviours among adolescents in vulnerable locations in the global South. Based on Construal Level Theory, this paper investigates how message congruency affects the link between perceptions of climate change risk and efficacy and two predictors of behavioural change: perceived responsibility and mitigation intentions. We conducted an experiment to test the effect of congruent vs. incongruent risk communication among adolescents in highly vulnerable coastal communities in the Mekong Delta in Vietnam (N = 348). Multiple regression analysis found strong effects of congruency in message framing; when messages were congruent in the content, communicative interventions changed adolescents’ perceptions and attitudes toward climate change mitigation more consistently. This research contributes both theoretically and practically to risk communication among adolescents and toward climate change mitigation behaviour. Full article

In this paper, we describe a LoRa (from “Long Range”)-based, linear sensor network we have developed for transmitting location information of personnel and equipment in an underground mine. The system is intended to be used during emergencies when existing communications infrastructure has failed. Linear networks comprise a sequence of relays that forward data to a common destination, the headend. Relays forward location information transmitted from tags carried by personnel or equipment. Relays will usually be put in place as investigators or rescuers enter the mine. LoRa is used both by the tags to communicate to the relays and by the relays to forward messages to the headend. We have implemented and tested this system, and have carried out simulations and analyses to determine its scalability, reliability and fairness. The need for robustness and reliability has led us to use flooding rather than unicast communication. We also use message sequence numbers and time-to-live fields to prevent broadcast storms. Contention is managed using a simplified Carrier Sense Multiple Access (CSMA) scheme. We also address fairness. When the network is under load messages may be dropped by relays making messages generated more hops from the headend more likely to be dropped than messages nearer the headend. We explore the relationship between unfairness, traffic load and number of relays. We also observe that a network of larger numbers of lightly loaded relays performs more effectively than smaller numbers of heavily loaded relays. Full article