DOCSLIB.ORG

Enabling Digital Twins – a Comparative Study on Messaging Protocols and Serialization Formats for Digital Twins in Iov

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Enabling Digital Twins – a Comparative Study on Messaging Protocols and Serialization Formats for Digital Twins in Iov Linköping University | Department of Computer and Information Science Master’s thesis, 30 ECTS | Datateknik 2019 | LIU-IDA/LITH-EX-A--19/066--SE Enabling Digital Twins – A comparative study on messaging protocols and serialization formats for Digital Twins in IoV Att möjliggöra digitala tvillingar Daniel Persson Proos Supervisor : Patrick Lambrix Examiner : Niklas Carlsson Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about the Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: http://www.ep.liu.se/. © Daniel Persson Proos Abstract In this thesis, the trade-offs between latency and transmitted data volume in vehicle- to-cloud communication for different choices of application layer messaging protocols and binary serialization formats are studied. This is done with the purpose of getting enough performance improvement to enable delay-sensitive Intelligent Transport System (ITS) fea- tures, and to reduce data usage in mobile networks. The studied protocols are Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP) and Message Queuing Telemetry Transport (MQTT), and the serialization formats studied are Protobuf and Flatbuffers. The results show that CoAP – the only User Datagram Protocol (UDP) based protocol – has the lowest latency and overhead while not being able to guarantee reliable transfer. The best performer that can guarantee reliable transfer is MQTT. For the serialization formats, Protobuf is shown to have three times smaller serialized message size than Flatbuffers and also faster serialization speed. Flatbuffers is the winner in the case of memory use and deserialization time, which could make up for the poor performance in other aspects of data processing in the cloud. Further, the implications of these results in ITS communication are discussed suggestions made into future research topics. Acknowledgments First, I want to thank Scania CV AB for letting me do my Master’s thesis at their company. I would also like to thank my company supervisors Håkan Carlqvist and Farshad Naghibi. Further, I would like to thank Lasse Öberg and all other helpful people at Scania. I also want to thank my examiner Niklas Carlsson for his never-ending support during this thesis. Further, I want to thank my friends and all the people that made my university studies such a great time. Lastly, I want to thank my girlfriend Johanna and my mother Cecilia for all their love and support through the years. iv Contents Abstract iii Acknowledgments iv Contents v List of Figures vii List of Tables viii 1 Introduction 1 1.1 Motivation . 1 1.2 Aim............................................ 2 1.3 Purpose . 2 1.4 Research Questions . 2 1.5 Delimitations . 3 1.6 Contributions . 3 2 Background 4 2.1 Internet of Vehicles . 4 2.2 LTE Networks . 4 2.3 Messaging Protocols . 6 2.4 Data Serialization . 8 2.5 Related Work . 9 3 Evaluation Platform Implementation 11 3.1 Pre-study . 11 3.2 Benchmarking . 13 3.3 Limitations . 16 4 Performance Results: Serialization Formats 18 5 Performance Results: Protocol Comparison 21 5.1 Large Chunks Case . 21 5.2 Small Chunks Case . 24 5.3 Cross-Case Observations . 25 5.4 Impact of Message Sizes and Throughput . 27 6 Discussion 30 6.1 Results . 30 6.2 Method . 32 6.3 The Work in a Wider Context . 34 7 Conclusion 35 v 7.1 Protocols . 35 7.2 Serialization Formats . 36 7.3 Future Work . 36 Bibliography 38 vi List of Figures 2.1 Structure of the EPS. 5 2.2 Illustration of the Internet Protocol suite. 6 3.1 Histogram showing the distribution of JSON message sizes. 13 3.2 Path taken by packets in the protocol experiments. 15 3.3 Upload bandwidth trace used for the protocol tests. 16 4.1 Serialization and deserialization speeds and serialized message sizes, averaged over all messages and over 1000 runs per message. 18 4.2 Histograms showing the distributions of serialized message sizes. 19 4.3 Histograms showing the distributions of serialization memory usage, averaged over 1000 serializations for each message. 19 4.4 Histograms showing the distributions of deserialization memory usage, averaged over 1000 serializations for each message. 20 5.1 Histogram showing sizes of the 100 chosen Protobuf messages. 21 5.2 Latencies per message for large chunks, averaged over all messages. 22 5.3 Message overhead for large chunks, averaged over all messages. 22 5.4 Percentage of unsuccessful message transfers for large chunks. 23 5.5 Latencies for small chunks, averaged over all messages. 24 5.6 Message overhead for small chunks, averaged over all messages. 25 5.7 Percentage of unsuccessful message transfers for small chunks. 25 5.8 Average latencies for different message sizes. 27 5.9 Average latencies when keeping the loss rate at 0% and scaling the bitrate of the used throughput traces for large chunks. ......................... 28 5.10 Average latencies using the best possible network conditions with small chunks. 29 vii List of Tables 3.1 Software placement for the networking experimental setup. 14 viii 1 Introduction 1.1 Motivation Scania CV AB (henceforth Scania) is moving from a manufacturer of trucks and busses to- wards being a provider of complete solutions in the transport industry. One thing that makes this possible is connected vehicles, which Scania has today. To use these in the best possi- ble manner, one goal is to extract as much data as possible from vehicles in use. This data can then be used to extract valuable information and to enable further services based on that data. Today, data is extracted at a course time granularity, often with significant time elapsing between each collection instance. This is something that Scania wants to change. The transport industry is headed into a connected future called the Internet of Vehicles (IoV) [1]. There have been technological developments in the last decade that allow this. This has opened up completely new opportunities to enable functionalities for vehicles that have previously been impossible. Connecting to the cloud creates possibilities in uploading data from a vehicle that can then be analyzed using computers which are much more powerful than what is economically feasible to put inside a vehicle. This, in turn, will lead to improve- ments in the quality of experience for the customers through new product functionalities. As the technology evolves, more functionalities that rely on the computing power enabled by cloud computing will emerge. These new functionalities will put higher demand on the net- work connection between the vehicle and computing resources in terms of both bandwidth and latency. One enabler of such functionalities is Digital Twins [2]. A Digital Twin is a model of a physical entity, in this case a vehicle, which is stored in the cloud. This approach enables the model to be regularly updated, while historical values are stored for data analysis purposes to provide solutions that in the end will benefit the customer. Amazon Web Services (AWS), which Scania uses, provides a service called AWS IoT Device Shadow to enable this [3]. Certain uses for Digital Twins are more time sensitive than others and therefore have stricter demands on, e.g., latency[4]. Others are more focused on reliable data transfer than latency. In this thesis, both latency and reliability in the connection between the vehicle and the cloud are investigated. Latency will be used as the term for the time it takes to deliver a network packet from the sender to the recipient. In other words, it is half a round-trip time (RTT). Looking at the connection between the vehicle and the cloud, there are several things that could be done to improve the overhead in terms of memory use and the latency added 1 1.2. Aim because of it. One thing that can be optimized for different use-cases is the choice of the application layer networking protocol that is used for messaging in this type of connection [5].
Load more

Recommended publications
  • Document Databases, JSON, Mongodb 17
    MI-PDB, MIE-PDB: Advanced Database Systems http://www.ksi.mff.cuni.cz/~svoboda/courses/2015-2-MIE-PDB/ Lecture 13: Document Databases, JSON, MongoDB 17. 5. 2016 Lecturer: Martin Svoboda [email protected] Authors: Irena Holubová, Martin Svoboda Faculty of Mathematics and Physics, Charles University in Prague Course NDBI040: Big Data Management and NoSQL Databases Document Databases Basic Characteristics Documents are the main concept Stored and retrieved XML, JSON, … Documents are Self-describing Hierarchical tree data structures Can consist of maps, collections, scalar values, nested documents, … Documents in a collection are expected to be similar Their schema can differ Document databases store documents in the value part of the key-value store Key-value stores where the value is examinable Document Databases Suitable Use Cases Event Logging Many different applications want to log events Type of data being captured keeps changing Events can be sharded by the name of the application or type of event Content Management Systems, Blogging Platforms Managing user comments, user registrations, profiles, web-facing documents, … Web Analytics or Real-Time Analytics Parts of the document can be updated New metrics can be easily added without schema changes E-Commerce Applications Flexible schema for products and orders Evolving data models without expensive data migration Document Databases When Not to Use Complex Transactions Spanning Different Operations Atomic cross-document operations Some document databases do support (e.g., RavenDB) Queries against Varying Aggregate Structure Design of aggregate is constantly changing → we need to save the aggregates at the lowest level of granularity i.e., to normalize the data Document Databases Representatives Lotus Notes Storage Facility JSON JavaScript Object Notation Introduction • JSON = JavaScript Object Notation .
    [Show full text]
  • Smart Grid Serialization Comparison
    Downloaded from orbit.dtu.dk on: Sep 28, 2021 Smart Grid Serialization Comparison Petersen, Bo Søborg; Bindner, Henrik W.; You, Shi; Poulsen, Bjarne Published in: Computing Conference 2017 Link to article, DOI: 10.1109/SAI.2017.8252264 Publication date: 2017 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Petersen, B. S., Bindner, H. W., You, S., & Poulsen, B. (2017). Smart Grid Serialization Comparison. In Computing Conference 2017 (pp. 1339-1346). IEEE. https://doi.org/10.1109/SAI.2017.8252264 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Computing Conference 2017 18-20 July 2017 | London, UK Smart Grid Serialization Comparision Comparision of serialization for distributed control in the context of the Internet of Things Bo Petersen, Henrik Bindner, Shi You Bjarne Poulsen DTU Electrical Engineering DTU Compute Technical University of Denmark Technical University of Denmark Lyngby, Denmark Lyngby, Denmark [email protected], [email protected], [email protected] [email protected] Abstract—Communication between DERs and System to ensure that the control messages are received within a given Operators is required to provide Demand Response and solve timeframe, depending on the needs of the power grid.
    [Show full text]
  • Spindle Documentation Release 2.0.0
    spindle Documentation Release 2.0.0 Jorge Ortiz, Jason Liszka June 08, 2016 Contents 1 Thrift 3 1.1 Data model................................................3 1.2 Interface definition language (IDL)...................................4 1.3 Serialization formats...........................................4 2 Records 5 2.1 Creating a record.............................................5 2.2 Reading/writing records.........................................6 2.3 Record interface methods........................................6 2.4 Other methods..............................................7 2.5 Mutable trait...............................................7 2.6 Raw class.................................................7 2.7 Priming..................................................7 2.8 Proxies..................................................8 2.9 Reflection.................................................8 2.10 Field descriptors.............................................8 3 Custom types 9 3.1 Enhanced types..............................................9 3.2 Bitfields..................................................9 3.3 Type-safe IDs............................................... 10 4 Enums 13 4.1 Enum value methods........................................... 13 4.2 Companion object methods....................................... 13 4.3 Matching and unknown values...................................... 14 4.4 Serializing to string............................................ 14 4.5 Examples................................................. 14 5 Working
    [Show full text]
  • Rdf Repository Replacing Relational Database
    RDF REPOSITORY REPLACING RELATIONAL DATABASE 1B.Srinivasa Rao, 2Dr.G.Appa Rao 1,2Department of CSE, GITAM University Email:[email protected],[email protected] Abstract-- This study is to propose a flexible enable it. One such technology is RDF (Resource information storage mechanism based on the Description Framework)[2]. RDF is a directed, principles of Semantic Web that enables labelled graph for representing information in the information to be searched rather than queried. Web. This can be perceived as a repository In this study, a prototype is developed where without any predefined structure the focus is on the information rather than the The information stored in the traditional structure. Here information is stored in a RDBMS’s requires structure to be defined structure that is constructed on the fly. Entities upfront. On the contrary, information could be in the system are connected and form a graph, very complex to structure upfront despite the similar to the web of data in the Internet. This tremendous potential offered by the existing data is persisted in a peculiar way to optimize database systems. In the ever changing world, querying on this graph of data. All information another important characteristic of information relating to a subject is persisted closely so that in a system that impacts its structure is the reqeusting any information of a subject could be modification/enhancement to the system. This is handled in one call. Also, the information is a big concern with many software systems that maintained in triples so that the entire exist today and there is no tidy approach to deal relationship from subject to object via the with the problem.
    [Show full text]
  • CBOR (RFC 7049) Concise Binary Object Representation
    CBOR (RFC 7049) Concise Binary Object Representation Carsten Bormann, 2015-11-01 1 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 2 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 3 Slide stolen from Douglas Crockford History of Data Formats • Ad Hoc • Database Model • Document Model • Programming Language Model Box notation TLV 5 XML XSD 6 Slide stolen from Douglas Crockford JSON • JavaScript Object Notation • Minimal • Textual • Subset of JavaScript Values • Strings • Numbers • Booleans • Objects • Arrays • null Array ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"] [ [0, -1, 0], [1, 0, 0], [0, 0, 1] ] Object { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } Object Map { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } JSON limitations • No binary data (byte strings) • Numbers are in decimal, some parsing required • Format requires copying: • Escaping for strings • Base64 for binary • No extensibility (e.g., date format?) • Interoperability issues • I-JSON further reduces functionality (RFC 7493) 12 BSON and friends • Lots of “binary JSON” proposals • Often optimized for data at rest, not protocol use (BSON ➔ MongoDB) • Most are more complex than JSON 13 Why a new binary object format? • Different design goals from current formats – stated up front in the document • Extremely small code size – for work on constrained node networks • Reasonably compact data size – but no compression or even bit-fiddling • Useful to any protocol or application that likes the design goals 14 Concise Binary Object Representation (CBOR) 15 “Sea Boar” “Sea Boar” 16 Design goals (1 of 2) 1.
    [Show full text]
  • Msgpack Documentation Release 1.0
    msgpack Documentation Release 1.0 Author 2021-03-18 Contents 1 API reference 3 2 Advanced usage 9 Python Module Index 11 Index 13 i ii msgpack Documentation, Release 1.0 MessagePack is a efficient format for inter language data exchange. Contents 1 msgpack Documentation, Release 1.0 2 Contents CHAPTER 1 API reference msgpack.pack(o, stream, **kwargs) Pack object o and write it to stream See Packer for options. dump() is alias for pack() msgpack.packb(o, **kwargs) Pack object o and return packed bytes See Packer for options. dumps() is alias for packb() msgpack.unpack(stream, **kwargs) Unpack an object from stream. Raises ExtraData when stream contains extra bytes. See Unpacker for options. load() is alias for unpack() msgpack.unpackb(packed, *, object_hook=None, list_hook=None, bool use_list=True, bool raw=False, int timestamp=0, bool strict_map_key=True, unicode_errors=None, object_pairs_hook=None, ext_hook=ExtType, Py_ssize_t max_str_len=-1, Py_ssize_t max_bin_len=-1, Py_ssize_t max_array_len=-1, Py_ssize_t max_map_len=-1, Py_ssize_t max_ext_len=-1) Unpack packed_bytes to object. Returns an unpacked object. Raises ExtraData when packed contains extra bytes. Raises ValueError when packed is incomplete. Raises FormatError when packed is not valid msgpack. Raises StackError when packed contains too nested. Other exceptions can be raised during unpacking. See Unpacker for options. max_xxx_len options are configured automatically from len(packed). loads() is alias for unpackb() 3 msgpack Documentation, Release 1.0 class msgpack.Packer(default=None, *, bool use_single_float=False, bool autoreset=True, bool use_bin_type=True, bool strict_types=False, bool datetime=False, uni- code_errors=None) MessagePack Packer Usage: packer= Packer() astream.write(packer.pack(a)) astream.write(packer.pack(b)) Packer’s constructor has some keyword arguments: Parameters • default (callable) – Convert user type to builtin type that Packer supports.
    [Show full text]
  • JSON JSON (Javascript Object Notation) Ecmascript Javascript
    ECMAScript JSON ECMAScript is standardized JavaScript. The current version is the 12th edition: Péter Jeszenszky Ecma International, ECMAScript 2021 Language Specification, Standard ECMA-262, 12th ed., June 2021. https://www.ecma- international.org/publications-and-standards/standards/ecma-262/ October 8, 2021 The next version currently under development is ECMAScript 2022: ECMAScript 2022 Language Specification https://tc39.es/ecma262/ Péter Jeszenszky JSON October 8, 2021 1 / 94 Péter Jeszenszky JSON October 8, 2021 3 / 94 JSON (JavaScript Object Notation) JavaScript Lightweight, textual, and platform independent data exchange format. Used for representing structured data. The term JavaScript is used for the implementations of ECMAScript by different vendors. Can be read and written easily by humans. See also: JavaScript technologies overview Can be generated and processed easily by computer programs. https://developer.mozilla.org/en- US/docs/Web/JavaScript/JavaScript_technologies_overview Originates from the ECMAScript programming language. Website: https://www.json.org/ Péter Jeszenszky JSON October 8, 2021 2 / 94 Péter Jeszenszky JSON October 8, 2021 4 / 94 JavaScript Engines (1) Node.js (1) SpiderMonkey (written in: C/C++; license: Mozilla Public License 2.0) https://spidermonkey.dev/ A JavaScript runtime environment built on the V8 JavaScript engine The JavaScript engine of the Mozilla Project. that is designed to build scalable network applications. V8 (written in: C++; license: New BSD License) https://v8.dev/ Website: https://nodejs.org/ https://github.com/nodejs/node https://github.com/v8/v8/ License: MIT License The JavaScript engine of Chromium. Written in: C++, JavaScript JavaScriptCore (written in: C++; license: LGPLv2) https://developer.apple.com/documentation/javascriptcore https: Platform: Linux, macOS, Windows //github.com/WebKit/webkit/tree/master/Source/JavaScriptCore The JavaScript engine developed for the WebKit rendering engine.
    [Show full text]
  • Stupeflask Documentation
    Stupeflask Documentation Numberly Jan 20, 2020 Contents 1 Install 3 2 Comparison 5 3 Tests 7 4 License 9 5 Documentation 11 5.1 Better application defaults........................................ 11 5.2 Easier collection of configuration values................................. 13 5.3 Native ObjectId support......................................... 13 5.4 API Reference.............................................. 14 5.5 Cursor support.............................................. 18 6 Indices and tables 19 Python Module Index 21 Index 23 i ii Stupeflask Documentation a.k.a. « Flask on steroids » An opinionated Flask extension designed by and for web developers to reduce boilerplate code when working with Marshmallow, MongoDB and/or JSON. Documentation: https://flask-stupe.readthedocs.io • Return any object type in views, and it will be coerced to a flask.Response • Validate payloads through Marshmallow schemas • Easily add JSON converters for any custom type • Fetch all the blueprints from a whole module in one line • Native ObjectId support for both Flask and Marshmallow • Powerful configuration management • Decorators to handle authentication, permissions, and pagination • 100% coverage and no dependency Contents 1 Stupeflask Documentation 2 Contents CHAPTER 1 Install $ pip install flask-stupe 3 Stupeflask Documentation 4 Chapter 1. Install CHAPTER 2 Comparison Here is a comparison of a bare Flask application and its equivalent Stupeflask version. They both rely on MongoDB, handle input and output in JSON, and allow to create a user and retrieve
    [Show full text]
  • Advanced JSON Handling in Go 19:40 05 Mar 2020 Jonathan Hall Devops Evangelist / Go Developer / Clean Coder / Salsa Dancer About Me
    Advanced JSON handling in Go 19:40 05 Mar 2020 Jonathan Hall DevOps Evangelist / Go Developer / Clean Coder / Salsa Dancer About me Open Source contributor; CouchDB PMC, author of Kivik Core Tech Lead for Lana Former eCommerce Dev Manager at Bugaboo Former backend developer at Teamwork.com Former backend developer at Booking.com Former tech lead at eFolder/DoubleCheck 2 Show of hands Who has... ...used JSON in a Go program? ...been frustrated by Go's strict typing when dealing with JSON? ...felt limited by Go's standard JSON handling? What have been your biggest frustrations? 3 Today's Topics Very brief intro to JSON in Go Basic use of maps and structs Handling inputs of unknown type Handling data with some unknown fields 4 A brief intro to JSON JavaScript Object Notation, defined by RFC 8259 Human-readable, textual representation of arbitrary data Limted types: null, Number, String, Boolean, Array, Object Broad applications: Config files, data interchange, simple messaging 5 Alternatives to JSON YAML, TOML, INI BSON, MessagePack, CBOR, Smile XML ProtoBuf Custom/proprietary formats Many principles discussed in this presentation apply to any of the above formats. 6 Marshaling JSON Creating JSON from a Go object is (usually) very straight forward: func main() { x := map[string]string{ "foo": "bar", } data, _ := json.Marshal(x) fmt.Println(string(data)) } Run 7 Marshaling JSON, #2 Creating JSON from a Go object is (usually) very straight forward: func main() { type person struct { Name string `json:"name"` Age int `json:"age"` Description string `json:"descr,omitempty"` secret string // Unexported fields are never (un)marshaled } x := person{ Name: "Bob", Age: 32, secret: "Shhh!", } data, _ := json.Marshal(x) fmt.Println(string(data)) } Run 8 Unmarshaling JSON Unmarshaling JSON is often a bit trickier.
    [Show full text]
  • JSON As an XML Alternative
    JSON The Fat-Free Alternative to XML { “Lecture”: 27, “Course”: “CSC375”, “Days”: ”TTh", “Instructor”: “Haidar Harmanani” } JSON as an XML Alternative • JSON is a light-weight alternative to XML for data- interchange • JSON = JavaScript Object Notation – It’s really language independent – most programming languages can easily read it and instantiate objects or some other data structure • Defined in RFC 4627 • Started gaining tracking ~2006 and now widely used • http://json.org/ has more information JSON as an XML Alternative • What is JSON? – JSON is language independent – JSON is "self-describing" and easy to understand – *JSON uses JavaScript syntax for describing data objects, but JSON is still language and platform independent. JSON parsers and JSON libraries exists for many different programming languages. • JSON -Evaluates to JavaScript Objects – The JSON text format is syntactically identical to the code for creating JavaScript objects. – Because of this similarity, instead of using a parser, a JavaScript program can use the built-in eval() function and execute JSON data to produce native JavaScript objects. Example {"firstName": "John", l This is a JSON object "lastName" : "Smith", "age" : 25, with five key-value pairs "address" : l Objects are wrapped by {"streetAdr” : "21 2nd Street", curly braces "city" : "New York", "state" : "NY", l There are no object IDs ”zip" : "10021"}, l Keys are strings "phoneNumber": l Values are numbers, [{"type" : "home", "number": "212 555-1234"}, strings, objects or {"type" : "fax", arrays "number” : "646 555-4567"}] l Aarrays are wrapped by } square brackets The BNF is simple When to use JSON? • SOAP is a protocol specification for exchanging structured information in the implementation of Web Services.
    [Show full text]
  • Performance Comparison of Data Serialization Schemes for ETSI ITS Car-To-X Communication Systems
    International Journal on Advances in Telecommunications, vol 8 no 1 & 2, year 2015, http://www.iariajournals.org/telecommunications/ 48 Performance Comparison of Data Serialization Schemes for ETSI ITS Car-to-X Communication Systems Sebastian Bittl, Arturo A. Gonzalez, Michael Spahn,¨ and Wolf A. Heidrich Fraunhofer ESK Munich, Germany Email: fsebastian.bittl, arturo.gonzalez, michael.spaehn, [email protected] Abstract—Wireless Car-to-X communication is about to enter sense collision avoidance mechanism (CSMA-CA) for multi- the mass market in upcoming years. The non-licensed, but user channel access, longer packets imply longer transmission reserved bandwidth for such communications is relatively narrow times and hence a higher channel busy ratio. This means that in terms of the few usable channels and the expected number nodes near a transmitter, which are willing to transmit as well, of communicating entities. Among other communication aspects, must wait longer until the channel is free to use. Moreover, data serialization schemes that allow compact encoding as well as the wireless channel has a limited capacity allowing only a fast encoding and decoding are required. Compact representation of data helps in keeping a minimal bandwidth overhead in limited number of transmitted bits per unit of time. The lower the wireless channel. Moreover, since delay is an important the number of transmitted messages, the higher the number of performance parameter in Car-to-X communication, the pro- users that could use the given frequency spectrum in parallel. cessing time of the serialization/deserialization schemes shall be kept to a minimum. So far, no detailed analysis of different The data serialization schemes also influence processing data serialization schemes for Car-to-X communication regarding power requirements in both ETSI Intelligent Transport Sys- important properties such as runtime, memory consumption tems (ITS) in Europe [2] and Wireless Access in Vehicular and encoded output length has been published.
    [Show full text]
  • A Lightweight and Efficient Document Representation
    docrep: A lightweight and efficient document representation framework Tim Dawborn and James R. Curran -lab,e School of Information Technologies University of Sydney NSW 2006, Australia tim.dawborn,james.r.curran @sydney.edu.au { } Abstract Modelling linguistic phenomena requires highly structured and complex data representations. Document representation frameworks (DRFs) provide an interface to store and retrieve multiple annotation layers over a document. Researchers face a difficult choice: using a heavy-weight DRF or implement a custom DRF. The cost is substantial, either learning a new complex system, or continually adding features to a home-grown system that risks overrunning its original scope. We introduce DOCREP, a lightweight and efficient DRF, and compare it against existing DRFs. We discuss our design goals and implementations in C++, Python, and Java. We transform the OntoNotes 5 corpus using DOCREP and UIMA, providing a quantitative comparison, as well as discussing modelling trade-offs. We conclude with qualitative feedback from researchers who have used DOCREP for their own projects. Ultimately, we hope DOCREP is useful for the busy researcher who wants the benefits of a DRF, but has better things to do than to write one. 1 Introduction Computational Linguistics (CL) is increasingly a data-driven research discipline with researchers us- ing diverse collections of large-scale corpora (Parker et al., 2011). Representing linguistic phenomena can require modelling intricate data structures, both flat and hierarchical, layered over the original text; e.g. tokens, sentences, parts-of-speech, named entities, coreference relations, and trees. The scale and complexity of the data demands efficient representations. A document representation framework (DRF) should support the creation, storage, and retrieval of different annotation layers over collections of hetero- geneous documents.
    [Show full text]