Replica voting based architectures for reliable data dissemination in vehicular networks

Vehicular Ad Hoc Networks (VANETs) have emerged as an exciting research and application area. The envisioned applications, as well as some inherent VANET characteristics such as highly dynamic topology, frequently disconnected network, and different and dynamic network density, make data dissemination a challenging task in these networks. Several approaches for data dissemination in VANETs have been recently proposed in the literature. However, more work needs to be done since most of the proposed solutions do not effectively address some or all of the main challenges in these scenarios such as the broadcast storm, network partition and temporal network fragmentation. In this paper, we provide a comprehensive and critical review of proposed techniques for data dissemination in VANETs and identify the main challenges in this area. For this, we first define suitable criteria to classify and organize some of the existing solutions. Next we identify and discuss some open issues and conclude with possible future research directions.

We consider the problem of data dissemination in vehicular networks. Our main goal is to compare the application-level performance of fully distributed and centralized data dissemination approaches in the context of traffic advisory systems. Vehicular networks are emerging as a new distributed system environment with myriad promising applications. Wirelessly-connected, GPS-equipped vehicles can be used, for instance, as probes for traffic advisory or pavement condition information services with significant improvements in cost, coverage and accuracy. There is an ongoing discussion on the pros and cons of alternative approaches to data distribution for these applications. Proposed centralized, or infrastructure-based, models rely on road-side equipment to upload information to a central location for later use. Distributed approaches take advantage of the direct exchanges between participating vehicles to achieve higher scalability at the potential cost of data consistency. While distributed solutions can significantly reduce infrastructures' deployment and maintenance costs, it is unclear what the impact of "imprecise" information is to an application or what level of adoption is needed for this model to be effective. This paper investigates the inherent trade-offs in the adoption of distributed or centralized approaches to a traffic advisory service, a commonly proposed application. We based our analysis on a measurements study of signal propagation in urban settings and an extensive simulation-based experimentation in the Chicago road network.

Surveillance applications consist of computational devices that collect data representing the external environment (e.g., terrain conditions and vehicle tracking). The data collected is prone to errors because the processing algorithms in devices often have only limited capabilities that result in a fuzzy and imprecise representation of the external event. Here, replicating the devices and voting on the environment data collected by them enhances the trust-worthiness of data reported to the application. The paper describes a cooperation mechanism that embodies two complementary functions: "replication" of devices and "voting" among devices. Aided by domain-specific semantic information about the object space being sensed (e.g., a hostile terrain), we infuse methods to report events with a high degree of confidence.

Vehicular ad-hoc networks (VANETs) aim to increase the safety of passengers by making information available beyond the driver’s knowledge. The challenging properties of VANETs such as their dynamic behavior and intermittently connected feature need to be considered when designing a reliable communication protocol in a VANET. In this study, we propose an epidemic and density adaptive protocol for data dissemination in vehicular networks, namely EpiDOL, which utilizes the opposite lane capacity with novel probability functions. We evaluate the performance in terms of end-to-end delay, throughput, overhead and usage ratio of the opposite lane under different vehicular traffic densities via realistic simulations based on SUMO traces in ns-3 simulator. We found out that EpiDOL achieves more than 90% throughput in low densities, and without any additional load to the network 75% throughput in high densities. In terms of throughput EpiDOL outperforms the Edge-Aware and DV-CAST protocols 10% and 40% respectively.

Background: An efficient health management information system (HMIS) improves health care delivery and outcomes. However, in most rural settings in Uganda, paper-based HMIS are widely used to monitor public health care services. Moreover, there are limited capabilities and capacity for quality HMIS in remote settings such as Kayunga district.Objectives: The quality assurance practices of HMIS in health centres (HCs) in Kayunga district were evaluated.Method: A cross-sectional descriptive study design was used to assess the quality of HMIS at 21 HCs in Kayunga district. Data were collected through in-depth interviews of HMIS focal persons as well as document analysis of HMIS records and guidelines between 15 June 2010 and 15 July 2010. The main outcomes were quality assurance practices, the HMIS programmatic challenges and opportunities. The practice of HMIS was assessed against a scale for good quality assurance practices. Qualitative data were coded and thematically analysed, whereas quantitative data were analysed by descriptive statistics using SPSS v22 software.Results: All the 21 HCs had manual paper-based HMIS. Less than 25% of HCs practised quality assurance measures during collection, compilation, analysis and dissemination of HMIS data. More than 50% of HCs were not practising any type of quality assurance during analysis and dissemination of data. The main challenges of the HMIS were the laborious and tedious manual system, the difficulty to archive and retrieve records, insufficient HMIS forms and difficulty in delivering hard copies of reports to relevant stakeholders influenced quality of data. Human resource challenges included understaffing where 43% of participating HCs did not have a designated HMIS staff.Conclusion: The HMIS quality assurance practices in Kayunga were suboptimal. Training and support supervision of HMIS focal persons is required to strengthen quality assurance of HMIS. Implementation of electronic HMIS dashboards with data quality checks should be integrated alongside the manual system.

Simulating a network by a smaller one with the same interconnection structure is also called emulation. In this paper, an efficient emulation for simulating an X-tree by another X;lree of smaller size is proposed. The proposed emulation is load-balanced and of 1-dilation. And, the congestion is The proposed emulation is optimal with respect to load and dilation. Besides, our emulation has the following advantages. First, it is simple and thus easy to compute. While using our emulation to simulate an X-tree C by a smaller X-tree H, we can easily determine the node that is responsible for simulating a given node of G in O(1) time. And, we can determine the nodes that are simulated by a given node of H in O(|G|/|H|)time. Second, our emulation can be directly applied to emulate a tree by another tree of smaller size without losing any efficiency. Finally, our emulation has the property that a root node is simulated by a root node and a leaf node is simulated by a leaf node. This property makes our emulation suitable for X-trees in which root nodes and leaf nodes should perform some special operations that cannot be performed by other internal nodes. For example, most algorithms designed on X-trees assume that only the root and leaf nodes can perform I/O operations. The m-ary tree is a generalization of the binary tree, where m ⩾ 2 and is an integer. In this paper, we also propose an efficient emulation for n;-ary trees. The proposed emulation for m-ary trees is load-balanced and of I-dilation. And, the congestion is

Utility maximization data scheduling in drone-assisted vehicular networks

Vehicular Ad-Hoc Networks (VANETs) are a fast growing technology that many governments and automobile manufacturers are investing in to provide not only safer and more secure roads, but also informational and entertainment-based applications for drivers. The applications developed for VANETs can be classified into multiple categories (safety, informational, entertainment). Most VANET applications, regardless of their category, depend on having certain vehicular data (vehicular speed, X position and Y position) available. Although these applications appear to use the same vehicular data, the characteristics of this data (i.e., amount, accuracy, and update rate) will vary based on the application category. For example, safety applications need an accurate version of the vehicular data with high frequency, but over short distances. Informational applications relax the data frequency constraint as they need the vehicular data to be reasonably accurate with less frequency, but over longer distances. If each of these applications shares the vehicular data with only its peers using its own mechanism, this behavior will not only introduce redundant functionalities (sending, receiving, processing, etc.) for handling the same data, but also wastefully consume the bandwidth by broadcasting the same data multiple times. Despite the differences in the data characteristics needed by each application, this data can be still shared. Vehicular networks introduce the potential for many co-existing applications. If we do not address the problem of data redundancy early, it may hinder the deployment and usefulness of many of these applications. Therefore, we developed a framework, cluster-based accurate syntactic compression of aggregated data in VANETs (CASCADE), for efficiently aggregating and disseminating commonly-used vehicular data. CASCADE is architccted as a layer that provides applications with a customized version of the vehicular data, based on parameters that each application registers with CASCADE. Additionally, the framework performs the common data handling functionalities (sending, receiving, aggregating, etc.) needed by the applications. This dissertation makes the following contributions: (1) a lossless data compression technique based on differential coding that is tailored for the characteristics of vehicular data; (2) a syntactic data aggregation mechanism that can represent the vehicular data in a 1.5 km area in one IEEE 802.11 frame; (3) a light-weight position verification technique that quickly detects false data with very low false positives; (4) a probabilistic data dissemination technique that alleviates the spatial broadcast storm problem and effectively uses the bandwidth to disseminate data to distant areas in a short amount of time in addition to having less redundancy and more coverage than other techniques. (5) a mechanism for recovering from the communication discontinuity problem in short time based on the traffic density in the opposite direction; (6) an investigation of the possible data structures for representing the vehicular data in a searchable format; (7) a parametric mechanism for matching the vehicular data and providing a customized version of the data that satisfies certain characteristics based on the parameter value. CASCADE through its four major components, local view, extended view, data security and data dissemination, provides an efficient solution for the problem of scalability for VANET applications.

Reinforcement learning based on routing with infrastructure nodes for data dissemination in vehicular networks (RRIN)

The prosperity of advanced applications in vehicular networks leads to frequent data requests of vehicles, necessitating an increased emphasis on data dissemination capabilities. Multicast transmission, enabling the simultaneous delivery of data to multiple receivers, stands out as a spectrally efficient approach for data dissemination. However, existing approaches in multicast transmission strategy for vehicular networks largely ignore the interference that can arise between multicast links, which results in a degradation of system performance. In this paper, we investigate the issue of joint multicast transmission and bandwidth allocation strategy for enhancing data dissemination in vehicular networks and formulate a problem to maximize the number of packets effectively transmitted by optimizing multicast link establishment, transmitted packets and bandwidth allocation. Because of the coupling of variables in the formulated problem, we decompose the problem into two subproblems and propose the conflict graph based multicast transmission strategy and the graph coloring algorithm based bandwidth allocation strategy for each subproblem. Then, an iterative algorithm is designed for joint optimization. Extensive simulations demonstrate the advantages of proposed strategy.

PremiseHeterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources.MethodsIn 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek).ResultsOver 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals.DiscussionWe highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.

Hospital surveillance for infection control purposes is a well-accepted method of following nosocomial infections in U.S. hospitals. However, hospital surveillance is being increasingly performed for nosocomial events in noninfectious areas, such as quality assurance and other areas of outcomes research. For the continued development of hospital surveillance in all these areas, dramatic growth in the amount of information collected will occur. To accommodate this growth and to validate new approaches in these areas, large amounts of data collection will be necessary. Collection of these data will be quite difficult without the creation of clinical hospital data bases in which large amounts of information are collected as a routine part of patient care, not as an elaborate addition to patient care. Automated hospital information systems, such as the HELP system, can facilitate the conduct of ongoing hospital surveillance not only in infection control but also in a broad range of areas, such as quality improvement outcomes research and cost-containment areas.

In this paper, we for the first time consider the space-time network coding (STNC) to propose a novel cooperative data dissemination strategy in the time-division multiple access (TDMA) manner for the vehicle network, to overcome the shortcomings of the traditional network coding (NC) techniques with carrier sense multiple access/collision avoidance (CSMA/CA) protocol, i.e, high signal detection complexity and channel collision problem. An optimal relay selection strategy in the procedure of cooperative data dissemination is proposed to maximize the average system capacity for the data dissemination. In a addition, a suboptimal relay selection strategy is put forward to reduce the computation complexity and thus makes the developed data dissemination strategy more practical. Compared with the non-cooperative data dissemination, the proposed cooperative data dissemination via STNC has better performance in terms of the average system capacity, the average user equipment (UE) capacity, and the average data dissemination delay.

Vehicular plays an important role in day-to-day world in the field of automobiles. VANETs are the subclass of mobile ad-hoc Networks which have no central infrastructure. Nodes are highly mobile in VANET. VANET is distinguished from MANET by their features, design and applications. A Vehicular Ad-Hoc Network or VANET is a sub form of Mobile Ad-Hoc Network or MANET that provides communication between vehicles and between vehicles and road-side base stations with an aim of providing efficient and safe transportation. A vehicle in VANET is considered to be an intelligent mobile node capable of communicating with its neighbors and other vehicles in the network. VANET introduces more challenges aspects as compare to MANET because of high mobility of nodes and fast topology changes in VANET. In this paper, VANET basics structure, its architecture, challenges, applications are discussed. Data dissemination is a challenging task because by utilizing limited bandwidth, maximum data has to disseminate over the vehicular network. Many researchers have provided several techniques to disseminate data so that the data can be accessed more efficiently. Major stress is given to this topic where data dissemination types and its protocols are compared.

Empirical studies to explain the reasons behind the 2007-2008 price volatility in the global food grain markets showed that among other factors, food grain stock especially stock-to-use ratio, was a major factor in price volatility. It has also been found that current data on stock at both global and national levels are inadequate, of poor quality and there is much to gain from investing in improving its quality for better understanding the price volatility in food grain markets and for take more appropriate policy decisions. Efforts are currently underway through a global project on Agricultural Market Information System (AMIS) for improving the mechanism of collection and dissemination of food grain stock data. This study has been conducted as a part of that project implemented in Bangladesh. Past efforts in the collection and dissemination of food grain stock data in Bangladesh and their conceptual and methodological strengths and weaknesses have been reviewed with a view to identify data gaps, methodological deficiencies and suggest measures required for improvement. The review revealed that currently there is no mechanism for regular collection and dissemination of food grain stock data in spite of its usefulness in the formulation of food policy and monitoring food grain markets and prices. The accuracy of the economy-wide monthly stocks depends on the accuracy of the parameters and the assumptions made about gross and net production, marketed surplus and sales pattern by month, population, per capita consumption as well as estimates on carryover stocks, domestic procurement and disposal. There is hardly any consensus on the accuracy of the available statistics on these parameters either from nationwide or from sample surveys. Recommendations have been made on mechanism of good quality stock data collection using proper conceptual and methodological approaches.