On-Board-Unit Data: A Big Data Platform for Scalable storage and Processing

For safe and efficient navigation, self-driving cars rely on determining their position through a system called Autonomous Vehicle Localization (AVL). Traditional self-driving cars face challenges related to security and speed in finding their location. To address these problems, this article presents a more secure way, called the Secured Localization (SL), to locate the vehicle, even when signals are weak. First, vehicles are registered and logged in. If Global Positioning System (GPS) signals are available, they are processed securely utilizing Gini-Montgomery Curve Cryptography (GMCC); If GPS signals are not available, then the car uses nearby signal points to find its location. SL data and sensed data from the sensors, including Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and Camera are given to On Board Unit (OBU). Then, the vehicle’s position is matched with a pre-stored map for accurate navigation. Finally, various methods such as Fisher Score Chi-Hell Square based K Means Clustering (FSCH-KMC), Cosine Gramian-Kalman Filter (CG-KF), and Hadoop Distributed File System (HDFS) are applied to prioritize and refine the vehicle’s location for better navigation. The proposed framework is simulated and the results show an accuracy of 98%, precision of 98%, and recall of 99%, with improvements in security and faster location finding compared to previous systems.

In last half decade, there is a tremendous growth in the network applications; we are experiencing an information explosion era and for that large amount of distributed data being stored and managed. Distributed file system is designed to handle these types of data. Major design issues in DFS are scalability, fault tolerance, flexibility and availability. The most prevalent DFS to deal with these challenges is the Hadoop Distributed File System (HDFS) which is a variant of the Google File System (GFS). Apache Hadoop able to solve current issues of Big Data by simplifying the implementation of data intensive and exceptionally parallel distributed applications. HDFS handles fault tolerance using Data Replication. HDFS replicates each data block on different datanode for reliability and availability. The existing implementation of HDFS in Hadoop performs replication in a pipelined manner which takes much time for replication. Here proposed system is an alternative parallel approach for efficient replica placement in HDFS to improve throughput. The experimentation has been performed to compare its performance with existing pipelined replication approach, which improve HDFS write throughput up to 10% testified by the TestDFSIO benchmark. This paper also depicts the analysis on the basis of different HDFS configuration parameter like file block size and replication factor which affects HDFS write performance in both approaches.

The Hadoop Distributed File System (HDFS) is designed to store, analysis, transfer larg datasets reliably, and stream it at high bandwidth to the user applications. HDFS is a variant of the Google File System (GFS). It handles fault tolerance by using data replication, where each data block is replicated and stored on multiple DataNodes. Therefore, the HDFS supports availability and reliability. The existed implementation of the HDFS in Hadoop performs replication in a pipelined manner that takes much time for replication. In this paper, an alternative technique for efficient replica placement, called Enhancement Lazy replication technique, has been proposed. The main principle of this technique is that, the client allows to write a block to two DataNodes in parallel, which store the packet. which will send acknowledgement directly to the client without waiting of receiving acknowledgement from other DataNodes. The experiment has been performed to evaluate the performance of the proposed HDFS replication technique with the default pipelined replication technique and the existed replication techniques; using TestDFSIO benchmark. According to the experimental results (i.e., the execution time and throughput), it is found that the HDFS availability has been improved in the proposed replication technique.

The Hadoop Distributed File System (HDFS) is a cornerstone of modern big data ecosystems, offering robust and scalable storage solutions. Optimizing data loading processes into HDFS is crucial for enhancing the overall performance of data - intensive applications. This paper explores various strategies and best practices for efficient data loading, focusing on key factors such as data locality, parallelism, and network configurations. Firstly, we delve into the architecture of HDFS, highlighting the roles of NameNode, DataNodes, and the block structure, which are pivotal for understanding data distribution and management within the system. We then evaluate different methods for loading data, including direct HDFS commands, WebHDFS, HttpFS, and tools like DistCp, Apache Flume, and Sqoop, discussing their relative efficiencies and use - case applicability. Further, we present detailed optimization techniques starting with data preprocessing, which involves data cleaning and the adoption of suitable serialization formats such as Avro and Parquet to minimize I/O operations. The impact of data compression on storage and performance is also examined, alongside methods for balancing data across DataNodes to prevent data skewness. Advanced strategies such as enhancing data locality to reduce latency and configuring high - bandwidth networks to expedite data transfer are discussed comprehensively. Additionally, the use of parallel data loading techniques is explored to maximize throughput. Monitoring and tuning data loading performance are addressed in the latter part of the paper, where key performance metrics and the tools necessary for performance assessment are outlined. Recommendations on tuning HDFS configurations to optimize data loading are provided based on empirical data and industry practices. This paper aims to serve as a comprehensive guide for practitioners looking to enhance their HDFS implementations, ensuring efficient data handling and optimal operational performance.

Hadoop Distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30% $\sim$ 95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50.

Big Data OverviewDriven by the need to generate business value, the enterprise has started to adopt Big Data as a solution, migrating from the classical databases and data stores which lack the flexibility and are not optimized enough [1].The changes in the environment make big data analytics attractive to all types of organizations, while the market conditions make it practical. The combination of simplified models for development, commoditization, a wider palette of data management tools, and low-cost utility computing has effectively lowered the barrier to entry. [2]. The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources.In recent approaches, Big Data is characterized by principles known as the 4VVolume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.Each day more businesses realize that Big Data is relevant as the applications generate large volumes of data generated automatically, from different data sources, centralized or autonomous. As traditional databases hit limitations when the need of analyzing this data, dedicated solutions must be considered.Important BigData Solutions:* Apache HBase/Hadoop is based on Google's BigTable distributed storage system, which runs on top of Hadoop as a distributed and scalable big data store. This means that HBase can leverage the distributed processing paradigm of the Hadoop Distributed File System (HDFS) and benefit from Hadoop's MapReduce programming model. It combines the scalability of Hadoop with real-time data access as a key/value store and deep analytic capabilities of Map Reduce [4].HBase allows to query for individual records as well as derive aggregate analytic reports across a massive amount of data. It can host large tables with billions of rows, millions of columns and run across a cluster of commodity hardware. HBase is composed of three types of servers in a master slave type of architecture. Region servers are responsible to serve data for reads and writes. When accessing data, clients communicate with HBase Region Servers directly. Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process.* Apache Cassandra is a distributed database used for the administration and management of large amounts of structured data across multiple servers, while providing highly available service and no single point of failure. It provides features such as continuous availability, linear scale performance, data distribution across multiple data centers and cloud availability zones. Cassandra inherits its data architecture from Google's BigTable and it borrows its distribution mechanisms from Amazon's Dynamo.The nodes in a Cassandra cluster are completely symmetrical, all having identical responsibilities. Cassandra also employs consistent hashing to partition and replicate data. it has the capability of handling large amounts of data and thousands of concurrent users or operations per second across multiple data centers.Cassandra has a hierarchy of caching mechanisms and carefully orchestrated disk I/O which ensures speed and data safety. Write operations are sent first to a persistent commit log (ensuring a durable write), then to a write-back cache called a memTable; when the memTable fills, it is flushed to a sorted string table - SSTable - on disk. A Cassandra cluster is organized as a ring, and it uses a partitioning strategy to distribute data evenly.* Redis represents an in-memory data structure store used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlogs and geospatial indexes with radius queries. Redis stores all data in RAM, allowing lightning fast reads and writes. It runs extremely efficiently in memory and handles high-velocity data, needing simple standard servers to deliver millions of operations per second with submillisecond latency. …

The vehicle industry manufacturer is showing an increased interest in remote diagnostics by improving customer relationship management and commercial interest. The special nature of construction machines fact that architectures and applications proposed for vehicular communication are not optimized for these types of vehicles. This paper proposes a new remote diagnostic architecture based on IEEE 802.11n for construction machines communication to the infrastructure on site. The mobile Internet is used to transmit collected data to the central monitoring application. We discuss the possible network selection algorithms, mobility and security protocols that can be implemented on the On Board Units (OBU). The proposed architecture is based on Host Identity Protocol (HIP). This protocol introduces a Host Identifier (HI) for naming the endpoints and use intensive cryptographic operation for security and mobility management. Due to their minimal computing capabilities, many single board computers used as OBU themselves suffer from applications which need intensive CPU usage. Thus, a performance analysis of HIP, implemented on single board computers, is proposed by the end of this paper.

A Smartphone VANET Based Forward Collision Detection System

Distributed storage systems are increasingly transitioning to the use of erasure codes since they offer higher reliability at significantly lower storage costs than data replication. However, these codes tradeoff recovery performance as they require multiple disk reads and network transfers for reconstructing an unavailable data block. As a result, most existing systems use an erasure code either optimized for storage overhead or recovery performance.In this paper, we present HACFS, a new erasure-coded storage system that instead uses two different erasure codes and dynamically adapts to workload changes. It uses a fast code to optimize for recovery performance and a compact code to reduce the storage overhead. A novel conversion mechanism is used to efficiently upcode and downcode data blocks between fast and compact codes. We show that HACFS design techniques are generic and successfully apply it to two different code families: Product and LRC codes.We have implemented HACFS as an extension to the Hadoop Distributed File System (HDFS) and experimentally evaluate it with five different workloads from production clusters. The HACFS system always maintains a low storage overhead and significantly improves the recovery performance as compared to three popular single-code storage systems. It reduces the degraded read latency by up to 46%, and the reconstruction time and disk/network traffic by up to 45%.

Data replication is a fundamental mechanism of the Hadoop Distributed File System (HDFS). However, the way data is spread across the cluster directly affects the replication balancing. The HDFS Balancer is a Hadoop integrated tool which can balance the storage load on each machine by moving data between nodes, although its operation does not address the specific needs of applications while performing block rearrangement. This paper proposes a customized balancing policy for HDFS Balancer based on a system of priorities, which can be adapted and configured according to usage demands. The priorities define whether HDFS parameters, or whether cluster topology should be considered during the operation, thus making the balancing more flexible.

This paper proposes a roadside unit (RSU)-coordinated synchronous multi-channel medium access control (MAC) scheme for vehicular ad hoc networks (VANETs). The proposed scheme allows the on board unit (OBU) to reserve service channels (SCHs) on the control channel (CCH) during almost the whole synchronous interval and supports simultaneous transmissions on different SCHs. It enhances the performance of VANETs and decreases the CCH congestion. Moreover, we use RSU to record the rendezvous information and broadcast it to the OBUs. This method avoids the multi-channel hidden terminal problem. An analytical model is developed to evaluate the aggregate throughput on SCHs. This model considers the following factors: non-safety message transmission probability in each synchronous interval, average size of the non-safety message, and the number of OBUs. Furthermore, the requirement of obtaining the maximum throughput is computed. Simulation results are provided to validate the analytical model and to demonstrate the improvement in throughput. The results indicate that the proposed scheme can provide higher aggregate throughput than that of the vehicular enhanced multi-channel MAC and IEEE 1609.4, and better performance in CCH congestion control especially under high network load conditions.

In urban vehicular network, existing routing protocols using beacon message scheme and P2P (peer to peer) density discovery scheme can hardly make a good performance as well as keep a high feasibility. This paper proposes an IGOR (improved geography and overlay routing) in urban vehicular environment. In IGOR, RSUs (road side units) are introduced to support the road density discovery. A passive neighbor discovery scheme is used by OBUs (on board units) instead of the beacon message scheme. The performance of IGOR is evaluated against the benchmark routing protocol of LOUVRE (landmark overlays for urban vehicular routing environment). Results show that IGOR has a higher accuracy in density discovery as well as a lower cost of the routing establishment and maintenance.

The emergence of the Internet of Things (IoT) is gradually changing people’s lives and is widely used in various fields. Wireless body area networks (WBAN) and vehicular ad-hoc networks (VANET) are typical examples of IoT applications. In both environments, an attacker can tamper, intercept, and eavesdrop on data transmitted in the public channel. Therefore, it is necessary to design authentication and key agreement (AKA) protocols to protect the communication of entities. Recently, Wu et al. proposed a novel AKA protocol in WBAN. Jagriti et al. proposed an anonymous AKA protocol in VANET. In this paper, we analyse of Wu et al.’s protocol and Jagriti et al.’s protocol, respectively. Unfortunately, we find that Wu et al.’s protocol cannot resist sensor node capture attacks, and Jagriti et al.’s protocol cannot resist session key disclosure attacks, on board unit (OBU) capture attacks, and man-in-the-middle attacks. Finally, we propose some suggestions for the two protocols.

This paper conveys the theoretical perspectives of sustainable industrial systems of On board Unit (OBU) in Electronic Toll and its transportation industry. It proposes kernel density analysis of artificial intelligence approach to industry 4.0. Regulation for technology developments in artificial intelligence and robotics are deemed as one of beneficial yet structurally neglected domain. This domain refers to human perspective on augmenting automation. This regulation was emphasized in 2017 by the European Parliament report level. The mentioned regulation comprises attention in Indonesia transportation industry indicating positive innovation domains in term of safeguards and regulations are needed. Prior, current and posterior trends of the Internet of Things, Industry 4.0, and Physical Internet constitutes the results of the data development and understanding. Therefore, the topical framework of automation and robotics are triggered by these developments. The mention triggers has impacted the most important wide range implementation of industries in the future. Electronic Toll Collection (ETC) system is 40% higher in cost efficiency than Manual Toll Collection (MTC) system. In this situation, Indonesian government has already issued full swing policy implementation on non-contact freeway toll collection system by the end of 2018. Structural Equation Modelling (SEM) is capitalized to proceed to data processing. Result of this research shown the driver's characteristics that significantly affect willingness to pay an on-board unit are education expenses, distance and frequency. The average value of driver willing to pay an on-board unit was 225.781 IDR and factors that affecting values of the willingness to pay an on-board unit are expenses and distance.

As urbanization grows and the cost of vehicle production decreases, urban traffic has become a major problem of modern life. Developing intelligent vehicles alongside standardizing inter-vehicle communications promises that this technology will be a part of future life. In this research, we propose a method in which clustering is used to gather vehicles' movements information in a vehicle ad-hoc network. This method is based on extending green wave using road-side units as a fixed agent and on board units (OBU) in vehicles as a mobile agent. This information is then transmitted to traffic lights for decision making. This algorithm evaluated using Monte Carlo simulation. The simulation results show this method has a positive effect on reducing the average waiting time and overall stop of the vehicles behind the traffic lights.