Solution For Data Dissemination Research Articles

Vehicular social aspects in data dissemination of urban critical events with QoS and QoE for supporting emergency response

AbstractCritical urban events impact the city agenda, like sustainable urban mobility, safe transportation, and efficient governance, thus leading many times to social disturbance, environmental degradation, and economic and human losses. Such events demand efficient dissemination of their data to the authorities to take rapid and effective actions to minimize their effects. The Social Internet of Vehicles (SIoV) paradigm can aid in monitoring such events in time and space. Vehicles are suitable for monitoring random events in space and time due to their ubiquity. Simultaneously, social relationships between vehicle users establish local communities where social intelligence can help disseminate data collected by VANET to support public management in event recognition. Thus, this article proposes a distributed and robust QoS/QoE aware, social‐based data dissemination solution for critical urban events and investigates how social parameters influence the quality of data dissemination. The impact of social influence, social activity, and friendship strategies was analyzed by simulation. The results show the gains in QoS when using social activity and in QoE subjective metrics when employing social influence.

Large-volume data dissemination for cellular-assisted automated driving with edge intelligence

Automated driving emerges as a potential technology for safe, convenient, and efficient transportation systems. In order to assist autonomous vehicles with large-volume data dissemination such as high definition (HD) map, cellular network becomes more inevitable to provide vehicles with the ability of accessing Internet. However, due to the bottleneck of wireless bandwidth and the high mobility of vehicles, efficient data dissemination assisted by cellular network is a challenging task. In this work, we investigate the large-volume data dissemination problem for cellular-assisted automated driving within given delay limit. Firstly, a data dissemination scheme is designed that transmits data from the base stations to the target vehicle via a collection of relay vehicles. Secondly, a novel user satisfaction model is derived by considering the vehicular mobility patterns and erasure coding, and the data dissemination is formulated into an optimization problem. Then, to solve this NP-hard problem, a low-complexity dynamic programming based data dissemination solution with edge intelligence is proposed. Finally, by carrying out extensive simulation, the effectiveness of proposed data dissemination scheme for cellular-assisted automated driving is demonstrated, in comparison with the optimal solution based on CPLEX as well as other conventional schemes.

CO2 Data Share: A Platform for Sharing CO2 Storage Reference Datasets from Demonstration Projects

Extensive research is ongoing worldwide to build competence and to develop cost-efficient technologies for safe injection and subsurface storage of CO2. Currently, the full potential of academic research is not realized due to limited access to reference datasets from field-scale and industry-scale CO2 storage projects. While synthetic data and simplified geometries are useful for proof-of-concept studies and simple estimates, data from actual CO2 storage operations remain invaluable to guide practical research, validate assumptions and provide new insights. Major investments in pilot, demonstration and industry-scale projects over the years have resulted in a range of reference datasets, which should play an important role for building confidence in CO2 storage as a greenhouse gas control strategy. However, significant barriers exist for dissemination and use of such datasets among a larger scientific audience, including lack of awareness, technical, licensing and quality issues, cost and documentation requirements. The CO2 DataShare project is dedicated to address these challenges by establishing an international database and online distribution system for curated, quality reference datasets collected from past and on-going CO2 storage projects. Through the financial support from US Department of Energy and the Norwegian CLIMIT programme, a pilot for sharing reference datasets is planned to be available for the CCS community during 2019. In the present paper, we describe the approach undertaken to establish a robust data dissemination solution based on identified needs and requirements of data users and data providers. A key component is a data documentation system that ensure that assumptions and inherent limitations of a given dataset are made clear to the users, while limiting the burden on data providers. It is important to note that work on the CO2 DataShare solution is still ongoing, and information given in the article should be considered preliminary.

Industrial Internet of Things Based Efficient and Reliable Data Dissemination Solution for Vehicular Ad Hoc Networks

Industrial Internet of Things (IIoT) is the other name of industrial Internet. It integrates a variety of existing industrial automation technologies with computing, machine learning, and communication technologies. Vehicular ad hoc network, an application of IIoT, is a self‐organized network of vehicles which tends to provide improved road safety, diminished traffic congestion, and ultimate comfort to the travellers. In VANETs, vehicles exchange data with each other directly or through roadside units (RSUs). Data dissemination in VANETs experiences numerous challenging issues including broadcast storm, network partitions, intermittent connectivity between vehicles, and limited bandwidth. In literature, various data dissemination schemes are proposed. However, most of these schemes are designed for either urban or highway VANET scenarios and evaluated under sparse or dense traffic conditions. Moreover, these schemes do not effectively overcome the aforementioned issues simultaneously. In this paper, we present a new data dissemination protocol for VANETs, which disseminates the emergency messages in different scenarios under varying traffic conditions. During dense traffic conditions, DDP4V employs the segmentation of transmission region of a vehicle in order to select the most appropriate next forwarding vehicle (NFV). Accordingly, it divides the transmission region of a vehicle in three distinct segments and selects vehicle(s) inside the highest priority segment to forward the message to all neighbour vehicles, whereas it also uses implicit acknowledgements for guaranteed message delivery during sparse traffic Conditions. Simulation results show that DDP4V protocol outperforms the other existing related protocols in terms of coverage, network overhead, collision, and end‐to‐end delay.

Vehicular delay-tolerant networks for smart grid data management using mobile edge computing

With the widespread popularity and usage of ICT around the world, there is increasing interest in replacing the traditional electric grid by the smart grid in the near future. Many smart devices exist in the smart grid environment. These devices may share their data with one another using the ICT-based infrastructure. The analysis of the data generated from various smart devices in the smart grid environment is one of the most challenging tasks to be performed as it varies with respect to parameters such as size, volume, velocity, and variety. The output of the data analysis needs to be transferred to the end users using various networks and smart appliances. But sometimes networks may become overloaded during such data transmissions to various smart devices. Consequently, significant delays may be incurred, which affect the overall performance of any implemented solution in this environment. We investigate the use of VDTNs as one of the solutions for data dissemination to various devices in the smart grid environment using mobile edge computing. VDTNs use the store-and-carry forward mechanism for message dissemination to various smart devices so that delays can be reduced during overloading and congestion situations in the core networks. As vehicles have high mobility, we propose mobile edge network support assisted by the cloud environment to manage the handoff and the processing of large data sets generated by various smart devices in the smart grid environment. In the proposed architecture, most of the computation for making decisions about charging and discharging is done by mobile devices such as vehicles located at the edge of the network (also called mobile edge computing). The computing and communication aspects are explored to analyze the impact of mobile edge computing on performance metrics such as message transmission delay, response time, and throughput to the end users using vehicles as the mobile nodes. Our empirical results demonstrate an improved performance 10-15 percent increase in throughput, 20 percent decrease in response time, and 10 percent decrease in the delay incurred with our proposed solution compared to existing state-ofthe- art solutions in the literature.

Efficient Broadcasting in VANETs Using Dynamic Backbone and Network Coding

Multihop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and many other interesting applications. However, designing an efficient data dissemination protocol for VANETs has been a challenging issue due to vehicle movements, limited wireless resources, and the lossy characteristics of wireless communication. In this paper, we propose a protocol that can provide a lightweight and reliable solution for data dissemination in VANETs. The protocol employs dynamically generated backbone vehicles to disseminate broadcast packets to reduce the MAC-layer contention time at each node while maintaining a high packet dissemination ratio by taking into account vehicle movement dynamics and the link quality between vehicles for the backbone selection. The protocol also uses network coding to reduce the protocol overhead and to improve the packet reception probability as compared with conventional approaches. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

Scalable data dissemination in opportunistic networks through cognitive methods

The Future Internet scenario will be characterised by a very large amount of information circulating in large scale content-centric networks. One primary concern is clearly to replicate and disseminate content efficiently, such that–ideally–it is replicated and spread only in those portions of the network where there are interested users. As centralised data dissemination solutions are unlikely to be feasible due to the sheer amount of content expected to circulate, nodes themselves must locally take data dissemination decisions, taking into account contextual information about users interests. In this paper, we consider a mobile opportunistic networking environment where mobile nodes exploit contacts among each other to replicate and disseminate content without central control. In this environment, we see nodes as proxies of their human users in the cyber world made up by mobile devices. Accordingly, we want nodes to act as much as possible as their users would do if they had to disseminate information among each other. We thus propose a new solution based on cognitive heuristics. Cognitive heuristics are functional models of the human mental processes, studied in the cognitive psychology field. They describe the judgement process the brain performs when subject to temporal constraints or partial information. We illustrate how these cognitive processes can be fruitfully implemented into a feasible and working ICT solution, in which decisions about the dissemination process are based on aggregated information built up from observations of the encountered nodes and successively exploited through a stochastic mechanism to decide what content to replicate. These two features allow the proposed solution to drastically limit the state kept by each node, and to dynamically adapt to the dynamics of content diffusion, the dynamically changing node interests and the presence of churning of nodes participation to the data dissemination process. The performance of our solution is evaluated through simulations and compared with reference solutions in the literature.

A BROADCAST BASED ROUTING PROTOCOL FOR INTER-CLUSTER DATA DISSEMINATION IN VEHICULAR COMMUNICATION

Vehicular communication is one of the fast growing and promising technologies for a safe and comfortable driving environment. These technologies do not end up with economic and safety ramifications, but also extended with other informational and entertainment oriented services. Data are being propagated multi-hop between source and destination vehicles for many real-life applications. Clustering is one of the effective and scalable solutions for data dissemination in wireless ad hoc networks. Though many cluster-based methods have been proposed for multi-hop data delivery in vehicular ad hoc networks, most of them do not consider the real-time changes in the network topology or imposes large penalties in routing such as unstable clusters, broken links, updating route tables. In order to address these issues, we propose a broadcast based routing protocol for inter-cluster data dissemination in this study that works on real-time vehicle information. Unlike, most existing routing algorithms, it only uses hello messages to obtain routing information without many other control messages. In addition, it alleviates the storage of routing information in every node, which largely reduces the overheads in routing. We performed extensive simulation to demonstrate the effectiveness and efficiency of the proposed routing protocol. Results show that the proposed protocol outperforms other approaches in terms of average delay, average delivery ratio and average number of hops.

Minimum Broadcasting Structure for Optimal Data Dissemination in Vehicular Networks

In this paper, we model a vehicular multihop network as an evolving graph and formulate the problem of optimal data dissemination over the network in terms of minimum number of transmissions. We show that the problem belongs to the NP complexity class and provide an easy to implement polynomial-time 2 τH(Δ)-approximation algorithm, where τ is the number of different subgraphs that comprise the evolving graph, and H(Δ) is the harmonic number of the degree of the evolving graph. By means of applying our heuristic over a vehicular scenario generated by a microscopic road-traffic simulator, we provide some insight into the data dissemination issue. In addition, the proposed algorithm is employed to benchmark a state-of-the-art communication protocol. We hope this paper will inspire more efficient heuristics and data dissemination solutions.

Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

In the convergence of theCyber-Physical World, user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, therecognition heuristic, for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.

Towards Adaptive and Scalable Context Aware Middleware

The diffusion of portable client devices is promoting the spreading of novel mobile services, both traditional such as email and printing, and new such as social computing applications, capable of opportunistically exploiting any computing resource and any wireless connectivity encountered by roaming users. The new requirements call for novel context-aware middlewares to support and simplify the retrieval and the usage of context data. However, existing context data dissemination infrastructures still present several limitations: they are unable to adaptively exploit impromptu any wireless communication opportunity; they are unable to scale, especially in wide/densely populated environments; and they are prone to connection/device flaws. The article proposes a novel context-aware middleware that achieves adaptability, scalability, and dependability in context data dissemination through three main core guidelines: by using a distributed hierarchical architecture, by employing lightweight and adaptive context data dissemination solutions, and by adopting statistical context data/query replication techniques. The performance results, obtained by extensively testing the proposed solution in our wireless university campus testbed, have validated our design choices.

Magnetic diffusion: Scalability, reliability, and QoS of data dissemination mechanisms for wireless sensor networks

Envisioning a new generation of sensor network applications in healthcare and workplace safety, we seek mechanisms that provide timely and reliable transmissions of mission-critical data. Inspired by the physics in magnetism, we propose a simple diffusion-based data dissemination mechanism, referred to as the magnetic diffusion (MD). In that, the data sink, functioning like the magnet, propagates the magnetic charge to set up the magnetic field. Under the influence of the magnetic field, the sensor data, functioning like the metallic nails, are attracted towards the sink. We compare MD to the state-of-the-art mechanisms and find that MD: (1) performs the best in timely delivery of data, (2) achieves high data reliability in the presence of network dynamics, and yet (3) works as energy efficiently as the state of the art. These suggest that MD is an effective data dissemination solution to the mission-critical applications.