data communication presentation topics

Data Communication and Networking

This document provides an introduction to data communication and networking. It discusses analog and digital data, data communication, data flow including simplex, half-duplex and full-duplex, and modems. It also covers local area networks, metropolitan area networks, wide area networks, network topologies including linear bus, star, tree and others. Finally, it discusses the OSI model and considerations for data communication networks including performance, consistency, reliability and recovery. Read less

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower.

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action.

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more!

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time.

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis.

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations.

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data.

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total.

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data.

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points from heat map software , you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable.

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval.

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories.

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category.

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist.

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total.

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points.

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples.

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data.

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way.

Instead of using numbers or complex graphs, you use simple icons or images to represent data points.

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction.

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started!

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

Begin with a clear and informative title that sets the context for your presentation.
State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

Present the most essential data points or findings that align with your objective.
Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

Provide a brief overview of the context in which the data was collected and why it’s significant.
Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

Summarize the main insights or conclusions that can be drawn from the data.
Highlight the key takeaways that the audience should remember.

5. Visuals and charts

Use clear and appropriate visual aids to complement the data.
Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

Discuss the practical implications of the data or any recommended actions.
If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

Allocate time for questions and open discussion to engage the audience.
Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way.

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out.

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized.

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic.

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis.

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own.

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen.

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Data Communication Topics to be discussed:  Data Communication Terminology.  Data Transmission Signals.  Data Transmission Circuits.  Serial & Parallel.

Published by Willie Seger Modified over 9 years ago

Presentation on theme: "Data Communication Topics to be discussed:  Data Communication Terminology.  Data Transmission Signals.  Data Transmission Circuits.  Serial & Parallel."— Presentation transcript:

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Understanding Data Communications: Concepts and Techniques

Aug 14, 2024

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Explore essential definitions, analog versus digital systems, encoding techniques, and more in data communications. Learn how data are encoded into signals and transmitted effectively.

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Chapter 2 – Topics in Data Communications

Data Communications ConceptsIntroduction • Essential definitions for Data Communications • Data, Signaling, & Transmission Systems • Analog & Digital • Data are entities that convey meaning, while signaling is the transfer of encoded data thru a transmission system • Analog versus digital signaling • Digital signaling usually less expensive than analog but care must be taken to properly engineer system (e.g. - attenuation) • Combinations of analog & digital data and signals • Analog data -> Analog signals • Digital data -> Analog signals (Key equipment is a modem) • Analog data -> Digital signals (Key equipment is a codec) • Digital data -> Digital signals

Data Communications ConceptsAnalog versus Digital Transmission Systems • Analog systems transmit analog signals without regard for the content of the signal • Amplifiers are used to boost the energy of the signal • Amplifiers also boost the strength of any noise on the line, introducing the possibility that the signal could be lost • Digital Transmission Systems are concerned with the content of the signal • Repeaters used to regenerate the signal, overcoming attenuation • Repeaters output a new copy cleansed of any noise, so noise is not cumulative (however, bit errors can still occur if the signal is not regenerated before it degrades too much)

Data Encoding TechniquesIntroduction • Encoding is the process of mapping digital data into the appropriate signal elements for transmission • Encoding may be very complex or as simple as using binary signal elements (0s and 1s) • Encoding schemes are chosen to assist the receiver in its two key tasks: • Determining when the signal element begins and ends (so sampling is done at the proper time) • Determining the value of the signal element (Is it a one? A zero?) • Attenuation, data rate, & noise all play a role at receiver • With analog data the encoding scheme also plays a key role in system performance but the details are a little different

Data Encoding TechniquesAnalog encoding of digital data • The basis for analog encoding is a base signal called the carrier signal • Digital data is encoded (and decoded at the other end) by a device called a modem • Three basic schemes for analog encoding of digital data: • Amplitude Shift-keying (ASK) • Frequency Shift-keying (FSK) • Phase Shift-keying (PSK) • These schemes can be combined for more sophisticated digital transmission systems that carry more data per signal element

Data Encoding TechniquesAnalog encoding of digital data • Amplitude-shift Keying • Data represented by different amplitude levels of the carrier signal • Simplest scheme, but inefficient and prone to noise • Most valuable use is in optical systems • Frequency-shift Keying • Data represented by different frequency values near the carrier signal frequency • Less prone to errors but requires more complex circuitry • Phase-shift Keying • Data represented by different phase shifts to the carrier frequency • More efficient and noise resistant than ASK or FSK but requires more complex circuitry

Data Encoding TechniquesDigital Encoding of Digital Data • The most common way to encode digital data is to use a binary signaling scheme consisting of two voltage levels • NRZ-L (Non-Return to Zero Level) • Each voltage level defines the value of the digital data • Used only in very short connections • NRZ-I (Non-Return to Zero Inverted) • A transition at the beginning of a signal unit denotes a binary one • This type of signaling is known as differential signaling; it is usually easier to detect a transition out of the background noise and the signals are polarity insensitive • Clocking and DC current are usually problems

Data Encoding TechniquesDigital Encoding of Digital Data • Manchester Encoding • Example of a bi-phase coding; up to two signaling transitions per signal element (needs more bandwidth to transmit a given data rate) • The mid-signal transition provides clocking as well as the data value (a zero data element is a high-to-low transition and a one is a low-to-high transition) • Used in Ethernet LANs (IEEE 802.3) • Differential Manchester Encoding • Another bi-phase code • The mid-signal transition provides clocking; the transition at the beginning of the signal element represents data (a zero data element has no transition at the beginning of a bit time while a one does) • Used in Token Ring LANs (IEEE 802.5)

Data Encoding TechniquesDigital Encoding of Analog Data • Pulse Code Modulation (PCM) is an example – used in the phone system to transmit analog data across digital networks • Sampling rate based on the Nyquist theorem • Digitized into 8 bit samples based on a nonlinear scale that provides good reproduction of the human voice • Other digital-to-analog encoding schemes: • Adaptive Differential Pulse Code Modulation (ADPCM) - used with voice transmission • Delta Modulation - used rarely but also for voice transmission systems • Code Excited Linear Prediction (CELP) - used in very low-bandwidth voice and multimedia communication systems

MultiplexingIntroduction • Allows a transmission system to carry multiple independent signals simultaneously for higher efficiency • Two general schemes are in use: FDM and TDM • Frequency Division Multiplexing (FDM) • Takes advantage of the fact that the useful bandwith of the transmission system exceeds the required bandwidth of a given signal • Allows frequency spectrum to be divided & allocated to different signal sources • Most commonly used with analog signaling and transmission • Time Division Multiplexing (TDM)

MultiplexingTechniques • Allows a transmission system to carry multiple independent signals simultaneously for higher efficiency • Two general schemes are in use: FDM and TDM • Time Division Multiplexing (TDM) • Takes advantage of the fact that the maximum bit rate of the system exceeds the required bit rate of the digital signal • Each source is allocated a ‘time slot’ in the multiplexer • Analog signals can be time division multiplexed, but it is very uncommon • Two varieties of TDM: statistical and fixed time-slot • Both FDM and TDM can be used in a synchronous or asynchronous manner

Transmission MediaIntroduction • The transmission media is the physical signal path between the transmitter and the receiver • Can be guided (cables, waveguides, etc.) or unguided (open air) • Our key concerns for transmission systems are data rate and distance • Influencing factors: • Bandwidth of the media • Transmission impairments • Interference • Number of Receivers

Transmission Media (2)Twisted Pair Cable • Consists of a minimum of two copper wires twisted together and enclosed within a protective sheath • Advantages: inexpensive, easy to work with, may already be installed where needed • Disadvantages: limited in distance, data rate, and bandwidth; susceptible to interference • Comes in two general varieties: shielded twisted pair (STP) and unshielded twisted pair (UTP) • Shielding provides more noise immunity, especially at lower data rates • STP costs more and is more difficult to work with

Transmission Media (3)Twisted Pair Cable • Category 3 and Category 5 UTP • Rating standards devised by the Electronic Industries Association (EIA) • The higher the category the better the cable; Cat 3 designed to support 10Mbps Ethernet while Cat 5 will support 100Mbps Ethernet • The key difference between the two categories is the number of twists per unit length of cable • Near-end Crosstalk (NEXT) is a key transmission impairment to minimize in any twisted pair cabling system • While these are regarded as the most commonly found UTP installations, there are higher performance UTP choices

Transmission MediaTwisted Pair Cable • High-performance Twisted Pair • Category 5e (or enhanced Category 5): supports 125-MHz bandwidth on all four pairs, allowing Gigabit Ethernet to run over UTP up to 100 meters • Attenuation (db/100m)=12.3 for 100 MHz, 12.3=-10Log(Vin/Vout) gives Log(Vin/Vout)=-1.23 or Vin/Vout is about 1/10 (tenth of signal magnitude exits from the UTP. • Crosstalk=32db=-10Log(Vneighbor/Vsignal), about 1/1000 crosstalk. • Cat 5 UTP provides 100 Mbps over 100m. • Category 6: supports over 200-MHz bandwidth on all four pairs; could potentially run high data rate ATM connections • Category 7: will require special shielding and will likely support up to 700-MHz bandwidth on each pair

Transmission MediaCoaxial Cable • Provides a two conductor transmission system where one conductor is situated inside the outer hollow conductor with an insulating dielectric in between • Because of its structural characteristics coaxial cable is more resistant to noise than twisted pair • Harder to work with and more expensive than twisted pair • Coax systems can be grouped in three categories based on the type of signaling used: • Baseband: digital signaling occupies the entire spectrum of the cable • Broadband: carrier-band analog signaling is used, allowing multiple channels on the cable • Carrierband: carrier-band analog signaling with low-end components; signal occupies entire spectrum of cable • Coaxial cable provides 100 Mbps over 1Km.

Transmission MediaFiber Optic Cabling • A transmission system composed of a guided medium that allows the propagation of optical rays • A range of fiber optic cabling exists for various needs, from ultra-pure fused silica (expensive but high data rate) to plastic (cheap with lower data rate for short runs) • Advantages • Huge bandwidth capacity • Smaller size and lightweight • Lower attenuation • Electromagnetic isolation (high security & minimal interference) • 100 Gbps over 10 Km (multimode fiber) • Common transmitters used are LEDs for (low-cost & low-speed systems) or Injection Laser Diodes (long-haul high-speed systems)

Transmission MediaFiber Optic Cabling • Basic fiber types • Step-index multimode: cheapest to manufacture but allows light to travel different paths down the fiber, causing signal distortion & lowering the maximum data rate. • Graded-index multimode: Higher grade of fiber with a varying refractive index that limits distortion of the signal. • Singlemode: contains a core with a diameter close to the wavelength to be transmitted; allows only a single transmission path down the fiber which practically eliminates distortion • Three wavelength ‘windows’ provide the best light propagation: 850, 1300, & 1550 nm • Most multimode systems use the 850 nm window • Long-haul transmission systems use the 1550 nm window because loss is lower at higher wavelengths

Transmission MediaUnguided Media • Microwave • Occupies the frequency spectrum from 1GHz to 30GHz; can provide either a highly directional or omni-directional system • There are 3 main challenges to using microwave for data transmission: • Frequency Allocation and licensing • Interference • Security • Infrared • Uses light in the infrared spectrum for data transmission • Must be used line-of-sight or in an environment that allows infrared waves to be reflected • Less issues associated with microwave but only for specialized uses

Data Communication NetworksIntroduction • For most WAN and MANs, transmission of data usually involves a number of intermediate switching nodes that move the data between source and destination • The complete set of end nodes, data links, & intermediate switches is known as a communications network • There is a spectrum of communication switching techniques; the two main variations are circuit and packet switching

Data Communication NetworksCircuit Switching • Communication between end nodes is via a dedicated communications channel • Communications via circuit switching involves three phases: • Circuit establishment: the path is established before any data is transferred. The path is digital or analog and may include internal links operated using TDM or FDM. • Data transfer • Circuit disconnect: release of resource dedicated to the connection • The fixed capacity of the channel is allocated for the duration of the connection; can be very inefficient with bursty traffic (repeatedly ON during T and OFF later) • Circuit switching is best suited for synchronous data such as voice or real-time video

Data Communication NetworksPacket Switching • Packet switching breaks data up into a series of packets, each appended with enough control information to ensure the packet transits the network successfully from source to destination • Developed to address problems certain data sources have with circuit switching: • Bursty data transmission • Source and destination must operate at the same data rate • Inefficient resource allocation • Connection setup can be too slow for certain applications (set up Virtual Circuit along logical connection path links, send packets without routing decision over VC) • In addition to addressing the above problems, packet switching also has other benefits for data transmission: • Under heavy load the network will accept packets but delay increases • Priorities for transmission of the packets can be set • Data rate conversion along links with short store and forward

Data Communication NetworksPacket Switching • Two main varieties: datagrams or virtual circuits • Datagram Approach: Each packet is routed independently of all others, leading to the following consequences: • Packets don’t take the same routes, may arrive out of sequence • Routing based on neighboring info on traffic and failure • Possible packet discard (overflow in queues) and no control-flow • No circuit setup time, so data flow begins without delay • Data can easily flow around problems in the network • Virtual Circuit Approach: a preplanned route through the network is established before any data is sent • Requires logical circuit setup and teardown but routes along the connection are shared (identical) with other packets • A routing decision does not have to be made for every packet • May provide enhanced services such as error & flow control, and packet sequencing not available in a datagram environment

Problem: M bit to be transmitted using UDP and Virtual Circuit (VC). t(UDP) and t(VC) is routing overhead for UDP and VC for each link. There are K links on the path. The largest packet has only N bits and data rate is R. Under what condition UDP and VC have the same transmission time. Using UDP: T(UDP)= kM/N(1/R+t(UDP)) Using VC: T(UDP)= k(t(VC) + M/NR) The same transmission time when kMt(UDP)/N =kt(VC). However, UDP produces shorter time when M/N is small.

Data Communication NetworksHybrids • Multi-rate Circuit Switching (to improve circuit switching) • Extends circuit switching to allow one or more fundamental channels to be bundled together to provide a range of data connection rates • Examples of multi-rate switching are ISDN (2x64 Kbps and 1x16Kbps channels) and inverse multiplexing • Frame Relay (FR) • Packet switching was operating under high error rate and overhead added to enhance redundancy and reduce errors which produced the FR scheme (from 64 Kbps to 2 Mbps by removing overhead). • WAN service based on a connection-oriented packet data protocol • Frame Relay evolved from X.25; the new protocol was streamlined by eliminating features necessary on earlier, less reliable X.25 data communications networks • Cell Relay (ATM) • A further evolution of connection-oriented packet data services • Unlike frame relay fixed length data units (cells) are used which allow high-speed hardware based switching • Connection oriented, fixed cell size, fast switching devices.

Comparison Packet Switching vs. Circuit Switching Is packet switching a “clear winner?” • Great for bursty data • Resource sharing • No call setup • Excessive congestion: packet delay and loss • Protocols needed for reliable data transfer, congestion control • Q: How to provide circuit-like behavior? • Bandwidth guarantees needed for audio/video apps • Still an unsolved problem

Routing Routing in Packet-Switched Networks • Goal: move packets among routers from source to destination • We’ll study several path selection algorithms (chapter 5) • Datagram network: • Destination address determines next hop • Routes may change during session • Analogy: postal service • Virtual circuit network: • Each packet carries tag (virtual circuit ID), tag determines next hop • Fixed path determined at call setup time, remains fixed through call • Routers maintain per-call state

Telecommunication networks Circuit-switched networks Packet-switched networks FDM TDM VC Based networks Datagram networks Taxonomy

Network Access • End hosts are connected to edge routers through access networks • Types of access networks: • Residential access • Company access • Mobile access • Types of physical media technologies for access networks: • Fiber • Coaxial cable • Twisted-pair telephone wire • Radio spectrum

Access Network Access Network: Residential Access • Connects home end systems to the network edge • Typically, through an ISP • End hosts are PCs • AKA last mile • Means of residential access: dialup, DSL, Cable, etc. • Dial-up modem • Uses POTS line  twisted pair copper wire • Calls ISP’s number • Max. data rate: 56 Kbps • Phone line is tied up when connected to ISP • Digital Subscriber Line (DSL) • Does not tie up the phone line • Uses existing twisted-pair line • Asymmetric upstream and downstream data rates • Downstream: 384 Kb/s—1.5 Mb/s • Upstream: 128—256 Kb/s • Hybrid Fiber Coaxial (HFC) Cable • Utilizes distribution network of video broadcast cable • Cable modem uses two channels for data transmission • Shared among subscribers • 10 Base-T Ethernet port

LAN access LAN access • Company/university local area network (LAN) connects end system to edge router • Ethernet: • Shared or dedicated cable connects end system and router • 10 Mbs, 100Mbps, Gigabit Ethernet • Deployment: institutions, home LANs soon • LANs: Link layer (chapter 5) Wireless Access Networks • Shared wireless access network connects mobile end system to router at a base station • Laptops, PDAs, etc. • Wireless LANs: • Radio spectrum replaces wire • Wireless LANs are based on IEEE 802.11 b standard (11 Mbps) • Wider-area wireless access • CDPD (Cellular Digital Packet Data): wireless access to ISP router via cellular network • Third Generation (3G) wireless: packet-switched wide-area Internet access at 384 Kbps

Example 1 • How long will it take to send a file of 640,000 bits from host A to host B over a circuit-switched network. • Suppose all links in the network are TDM with: • 24 slots and • have a bit rate of 1.536Mbps • It takes 500 msec to establish an end-to-end circuit before host A begins transmitting to B • How long will it take to send file? • Transmission rate for each circuit = 1.536 Mbps / 24 = 64 Kbps • Time to send 640 Kbits file = (640000 bits)/(64 Kbits/sec) = 10 seconds • Including circuit setup overhead, time to send file is 10.5 seconds • This calculation is independent of the # of end-to-end links and does not include propagation delays

Example 2 Packet Switching: • Two forwarding mechanisms: • No segmentation  message switching • With segmentationpipelining • Example: 7.5 million bits message sent over 3 links, each of 1.5 Mbps • Time required without segmentation = (7.5/1.5)x3=15 sec • Now segment packet into 5000 chunks each of 1500 bits • Time for whole packet = 5.002 sec • Pipelining results in reduction of delays as all links are being utilized simultaneously

Delay in Packet-Switched networks Transmission delay: • R=link bandwidth (bps) • L=packet length (bits) • Time to send bits into link = L/R Propagation delay: • d = length of physical link • s = propagation speed in medium (~2x108 m/sec) • Propagation delay = d/s

Example 3 Packet Switching Calculation of delay: • A packet of L bits • Q links between source and destination hosts • Each link has a data rate of R bits/sec • Assume: • No queuing delays • No end-to-end propagation delays • No connection establishment is required • How long it takes to send this L bit packet from source to destination? • Time to traverse the first link from source host: L/R seconds • Q-1 more such links are traversed before reaching destination • Thus, total delay: QL/R seconds  more delay for larger packets

Example: LAN Hierarchy • N stations to be arranged in a LAN hierarchy • Each ST transfers on the average M bits per second, of which qM are to STs that are local to its segment and (1-q)M to STs in other segments • How to arrange the LAN Hierarchy so that traffic in one LAN segment does not exceed the segment capacity B, where B is 0.8xData.Rate of a LAN segment, i.e. a reference to a congestion point.

Solution • Suppose there are K segments each consists of N/K stations, where k rages from 1 (1 seg) to N (N segs). • In a segment, the locally destined traffic by is qMN/K • In a segment, the exported traffic by is (1-q)MN/K • In a segment, the imported traffic by is (1-q)MN/K, because: • there are (K-1) segments each generates (1-q)MN/K external traffic, • Total external traffic for K-1 segments is (K-1)(1-q)MN/K destined to (k-1) segments. • Thus, a segment imports (1-q)MN/K data. • Total traffic for a segment is Q= qMN/K (local) + (1-q)MN/K (exported) + (1-q)MN/K (imported)=MN/K + (1-q)MN/K= MN(2-q)/K • Let’s derivate Q w.r.t. K to find its minimum value: dQ/dK=-MN(2-q)/K^2 which is minimum for K maximum or K=N. • We may incease the number of STs in a segment until Q(K)=0.8xData.Rate or MN(2-q)/K <= 0.8xData.Rate or • MN(2-q)/(0.8xData.Rate) <= K.

wireless transmission • • Wireless local area networks (e.g IEEE 802.11) • – 2.4 GHz (microwave band) 1-2 Mbps, 150 m • – Infrared (IR) link 1-10 Mbps, 10 m • • Earth based cellular data • – Basic individual connection, 13 Kbps, 3 km • – Higher rate PCS (e.g. EDGE), 384 Kbps, 3 km • • Satellite links (GHz frequencies) • – geosynchronous, 600-1000 Mbps, continent • – Low earth orbit (LEO) 13 kbps - 400 Mbps, 800 km • (e.g., Motorola Iridium, 66 satellite constellation) services available from carriers • Integrated Services Digital Network (ISDN) 144 Kbps • Asymmetric Digital Subscriber Lines 1.5-8 Mbps/16-640 Kbps • Cable modems 0.5-2 Mbps • T1 (old electronic telephony standard) 1.544 Mbps • T2 6.312 Mbps • T3 44.736 Mbps • Synchronous Transport Signal-1 (STS-1) 51.840 Mbps • STS-3 (a common ATM rate) 155.250 Mbps • STS-12 (another common ATM rate) 622.080 Mbps • STS-24 1.244160 Gbps • etc.

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