Introduction to Physical Layer Properties, MAC and the IEEE

Introduction to Physical Layer Properties, MAC and the IEEE 802. 15
Introduction to Physical Layer Properties, MAC and the IEEE Lecture 6 September 26, EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor Networks Andreas Savvides Office: AKW 212 Tel Course Website

Medium Access Control Control access to the shared medium (radio channel) avoid interference between transmissions mitigate effects of collisions (retransmit) History ALOHA CSMA MACA MACAW 802.11 S-MAC 1970 1980 1990 2000 2010 proactive vs. reactive

Medium Access Control Control access to the shared medium (radio channel) avoid interference between transmissions mitigate effects of collisions (retransmit) Approaches contention-based: no coordination schedule-based: central authority (access point) proactive vs. reactive

Collision-based MAC protocols
ALOHA : packet radio networks send when ready 18-35% channel utilization CSMA (Carrier Sense Multiple Access): “listen before talk” 50-80% channel utilization

CSMA/CA: Collision Avoidance
B C MACA: Request To Send Clear To Send DATA MACAW (Wireless) additional ACK cs Time RTS CTS Blocked DATA MACA: Medium Access with Collision Avoidance [Phil Karn, 1990] ACK

Hidden terminal problem
C cs Time cs Carrier sense at sender may not prevent collision at receiver DATA DATA

Exposed terminal problem
C D cs Time RTS Parallel CSMA transfers are synchronized by CSMA/CA Collision avoidance can be too restrictive! CTS Blocked DATA ACK

IEEE 802.11 Operation infrastructure mode (access point) ad-hoc mode
Power save mechanism; not for multi-hop networks Protocol carrier sense collision avoidance (optional)

IEEE 802.11 Network Allocation Vector (NAV) collision avoidance
RTS DATA Contention Window Sender Receiver Others NAV(RTS) NAV(CTS) SIFS DIFS CTS ACK Network Allocation Vector (NAV) collision avoidance overhearing avoidance: other nodes may sleep

Schedule-based MAC protocols
Communication is scheduled in advance no contention no overhearing support for delay-bound traffic (voice) Time-Division Multiple Access time is divided into slotted frames access point broadcasts schedule coordination between cells required

TDMA Typical WLAN setup no direct communication between nodes
CP Frame n Frame n+2 Frame n+1 downlink uplink Typical WLAN setup no direct communication between nodes access point broadcast Traffic Control (TC) map (new) nodes signal needs in Contention Period (CP)

Why is MAC critical to Wireless Sensor Networks
Power, power & power Handle scarce resources CPU: 1 – 10 MHz memory: 2 – 4 KB RAM radio: ~100 Kbps energy: small batteries Unattended operation plug & play, robustness long lifetime

Energy consumption (mW)
25 20 15 10 5 Sleep 5 MHz 1 MHz LED Light Transmit Receive Standby Compass Accelerometer Transceiver Processor Sensors LED [Hoesel:2004]

OK, so we need to worry about power…
Let’s look into the radio propagation first…

Friss Free Space Propagation Model
Same formula in dB path loss form (with Gain constants filled in): How much is the range for a 0dBm transmitter 2.4 GHz band transmitter and pathloss of 92dBm?

Friss Free Space Propagation Model
Highly idealized model. It assumes: Free space, Isotropic antennas Perfect power match & no interference Represent the theoretical max transmission range Same formula in dB path loss form: How much is the range for a 0dBm transmitter 2.4 GHz band transmitter and pathloss of 92dBm?

A more realistic model: Log-Normal Shadowing Model
Model typically derived from measurements Statistically describes random shadowing effects values of n and σ are computed from measured data using linear regression Log normal model found to be valid in indoor environments!!!

IEEE 802.15.4 Radio Characteristics
Power output The standard does not specify a power output limit. Devices should be able to transmit -3dBm In US 1Watt limit in Europe 10mW for 2.4GHz band Receiver should be able to decode a packet with receive power of -85dBm in 2.4GHz and -92dBm in the lower frequency bands What does that mean in terms of range?

Going from Watts to dBm +20dBm=100mW +10dBm=10mW +7dBm=5mW +6dBm = 4mW

Frequency Bands and Data Rates
In 2.4GHz band 62.5 ksymbols/second 1 symbol is 4 bits 1 symbol is encoded into a 32-bit pseudorandom sequence the chip chip rate = 62.5 x 32 = 2000 kchips/s Raw data rate = Symbol rate * chips per symbol = * 4 = 250kb/s In 868/915 MHz bands 1 bit symbol (0 or 1) is represented by a 15-chip sequence

Physical Layer Transmission Process
Binary Data from PPDU Bit to Symbol Conversion Symbol to Chip Conversion O-QPSK Modulator RF Signal

Propagation Mechanisms in Space with Objects
Reflection Radio wave impinges on an object >> λ (30 GHz) Earth surface, walls, buildings, atmospheric layers Diffraction Radio path is obstructed by an impenetrable surface with sharp irregularities (edges) Secondary waves “bend” arounf the obstacle Explains how RF energy can travel without LOS Scattering When medium has large number of objects < λ GHz) Similar principles as diffraction, energy reradiated in many directions Rough surfaces, small objects (e.g foliage, lamp posts, street signs) Other: Fading and multipath

Transmit Power Levels in Chipcon CC2420
Radio supply voltage= 2.5V And Power = I*V = 1mW = 43.5mW

An Experiment at Yale XYZ sensor node designed at Yale ( CC2420 wireless radio from Chipcon 2.4 GHz IEEE /Zigbee-ready RF transceiver DSSS modem with 9 dB spreading gain Effective data rate: 250 Kbps 8 discrete power levels: 0, -1, -3, -5 , -7, , -15 and -25 dBm Power consumption: 29mW – 52mW Monopole antenna with length equal to 1.1inch. EWSN February 15th Dimitrios Lymberopoulos

P = RSSI + RSSIOFFSET [dBm]
Received Signal Strength Indicator (RSSI) The power P at the input RF pins can be obtained directly from RSSI: P = RSSI + RSSIOFFSET [dBm] RSSI is an 8-bit value computed by the radio over 8 symbols (128μs) RSSIOFFSET is determined experimentally based on the front-end gain. It is equal to -45dbm for the CC2420 radio Sources of RSSI Variability Intrinsic Radio transmitter and receiver calibration Extrinsic Antenna orientation Multipath, Fading, Shadowing EWSN February 15th Dimitrios Lymberopoulos

RSSI(d) = PT – PL(d0) – 10ηlog10(d/d0) + Xσ
Path Loss Prediction Model Log-normal shadowing signal propagation model: RSSI(d) = PT – PL(d0) – 10ηlog10(d/d0) + Xσ RSSI(d) is the RSSI value recorded at distance d PT is the transmission power PL(d0) is the path loss for a reference distance d0 η is the path loss exponent Xσ is a gaussian random variable with zero mean and σ2 variance Model verification using data from a basketball court EWSN February 15th Dimitrios Lymberopoulos

Radio Calibration For each location and orientation 20 packets were -15dBm 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- Receiver 1.31ft 0- 90- 180- 270- Transmitter 0- 90- 180- 270- 0- 90- 180- 270- EWSN February 15th Dimitrios Lymberopoulos

Radio Calibration For each location and orientation 20 packets were -15dBm 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- Receiver 1.31ft 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- Transmitter EWSN February 15th Dimitrios Lymberopoulos

Radio Calibration For each location and orientation 20 packets were -15dBm 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- Receiver 1.31ft Transmitter 0- 90- 180- 270- 0- 90- 180- 270- 0- 90- 180- 270- EWSN February 15th Dimitrios Lymberopoulos

Radio Calibration Experiment in an empty room
TX calibration: 9 different transmitters RX calibration: 6 different receivers TX Standard Deviation: 2.24dBm RX Standard Deviation: 1.86dBm EWSN February 15th Dimitrios Lymberopoulos

Antenna Characterization
Experiment took place in a basketball court Minimize multipath effect At each measurement point dBm were received Side View Top View 8ft 2ft 6.5ft 3.5ft 2ft 1.25ft 2ft : measurement point EWSN February 15th Dimitrios Lymberopoulos

Optimal antenna length-1.1inch Random RSSI values due to multipath Large communication range Suboptimal antenna with 2.9inch length EWSN February 15th Dimitrios Lymberopoulos

1.25ft 3.5ft 6.5ft Similar distances (<1ft difference) can produce very different RSSI values (even up to 11dBm) Very different distances ( even >18ft) can produce the same RSSI values EWSN February 15th Dimitrios Lymberopoulos

Best antenna orientation Worst antenna orientation
Antenna Characterization Best antenna orientation Worst antenna orientation Antenna orientation effect For a given height of the receiver very different RSSI values are recorded for different antenna orientations EWSN February 15th Dimitrios Lymberopoulos

Antenna Radiation Pattern
Side View Top View Communication range Symmetric Region Antenna orientation independent regions Communication range

Antenna Effects in Indoor Environments
The basketball court experiment was performed inside our lab We focused on the best antenna orientation EWSN February 15th Dimitrios Lymberopoulos

Large Scale Indoors Experiment
40 nodes were placed on the testbed (15ft (W) x 20ft(L) x 10ft(H)) Each node transmitted 10 packets at each one of the 8 power levels. The recorded RSSI values were transmitted to a base station for logging. Placement and Connectivity EWSN February 15th Dimitrios Lymberopoulos

Large Scale Indoors Experiment
Maximum (0dBm) Medium (-5dBm) Low (-15dbm) RSSI does not change linearly with the log of the distance Multipath 3-D antenna orientation EWSN February 15th Dimitrios Lymberopoulos

Link Asymmetry Asymmetric link between nodes A and B RSSI(A) ≠ RSSI(B)
One way links Asymmetric links EWSN February 15th Dimitrios Lymberopoulos

More than 30% of the links are affected by human presence or motion
What else can we do? Detection of: Human presence Human motion More than 30% of the links are affected by human presence or motion EWSN February 15th Dimitrios Lymberopoulos

Experiment Lessons Radio calibration has minimal effect on localization 3-D space is very different than 2-D space Antenna orientation effects are dominant in 3-D deployments 3-D deployments are a more realistic for evaluating RSSI localization methods RSSI distance prediction in 3-D deployments is almost impossible Ordering of the RSSI values is not helpful Even if antenna orientation is known! Probabilistic approaches A probabilistic model of RSSI exists for the symmetric region of the antenna Generalizing this model to 3-D deployments is extremely difficult if not impossible. EWSN February 15th Dimitrios Lymberopoulos

The IEEE 802.15.4 MAC Protocol Based on an IEEE standard for WPAN
Goal: Ultra-low cost, low power radios Support multiple configurations (e.g point-to-point, groups, ad-hoc etc) CSMA-CA based protocol Each packet can be individually acknowledged Key features Three types of node functionalities PAN Coordinator, Coordinator and Device Two device types FFD – Full Function Device RFD – Reduced Function Device

Frequencies and Data Rates
BAND COVERAGE DATA RATE # OF CHANNEL(S) 2.4 GHz ISM Worldwide kbps 868 MHz Europe kbps 915 MHz ISM Americas kbps

Now Back to IEEE MAC MAC supports 2 topology setups: star and peer-to-peer Star topology supports beacon and no-beacon structure All communication done through PAN coordinator

Start Topology: The PAN Coordinator
Any FFD may establish its own network by becoming the PAN coordinator After formation, STAR networks operate independently from neighboring networks PAN coordinate starts sending beacons Other devices can associate with the network by sending an association request

Peer-to-Peer Topology
Any FFD can communicate with any other FFD, can use multihop communication i.e this is ad-hoc networking RFDs can participate only as peripherals Do not have the capabilities of forwarding packets Each device responsible for proactively searching for other devices Once a device is found, then they can exchange information about what devices form

Star: Optional Beacon Structure
Generic Superframe Structure Beacon packet transmitted by PAN Coordinator to help Synchronization of network devices. It includes: Network identifier, beacon periodicity and superframe structure GTS: Guaranteed time Slots assigned by PAN coordinator

Star Network: Communicating with a Coordinator

Star Network: Communicating from a Coordinator
Beacon packet indicates that there is data pending for a network device Device sends request on a data slot Network device has to ask coordinator if there is data pending. If there is no data pending the Coordinator will respond with a zero Length data packet

Peer-to-Peer Data Transfer
Peer-to-peer data transfer governed by the network layer – not specified by the standard Four types of frames the standard can use Beacon frame – only needed by a coordinator Data frame – used for all data transfers ACK frame – confirm successful frame reception A MAC Command Frame – MAC peer entity controltransfers

Beacon Frame

ACK & Data Frames ACK Frame Data Frame

MAC Command Frame

Radio Energy Model: the Deeper Story….
Tx: Sender Rx: Receiver Incoming information Outgoing information Channel Transmit electronics Power amplifier Receive electronics Wireless communication subsystem consists of three components with substantially different characteristics Their relative importance depends on the transmission range of the radio

Energy Implication Active transceiver power consumption more related to symbol rate rather than raw data rate To minimize power consumption: Minimize Ton - maximize data rate Also minimize Ion by minimizing symbol rate Conclusion: Multilevel or M-ary signalling should be employed in the physical layer of sensor networks i.e need to send more than 1-bit per symbol

Energy-efficient MAC protocols
WSN-specific protocols starting from 2000 (1 paper) exponential growth (2004, 16+ papers) Classification (up to May 2004, 20 papers) the number of channels used the degree of organization between nodes the way in which a node is notified of an incoming msg

Protocol classification
Channels Organization Notification 2000 SMACS [34] FDMA frames schedule 2001 PACT [28] single PicoRadio [10] CDMA+tone random wakeup 2002 STEM [33] data+ctrl Preamble sampling [6] listening Arisha [2] S-MAC [36] slots PCM [18] Low Power Listening [13]

Protocol classification
2003 Sift [17] single random listening EMACs [15] frames schedule T-MAC [5] slots TRAMA [30] WiseMAC [7] 2004 BMA [24] Miller [27] data+tone wakeup+list DMAC [26] SS-TDMA [23] LMAC [14] B-MAC [29]

Latency Fairness Energy Case Study: S-MAC S-MAC Tradeoffs
Ye, Heidemann and Estrin, Infocom 2002 Tradeoffs Major components in S-MAC Periodic listen and sleep Collision avoidance Overhearing avoidance Massage passing Latency Fairness Energy We call our protocol Sensor-MAC or S-MAC. These are the major tradeoffs in S-MAC. We sacrifice the latency and fairness to gain better energy efficiency. S-MAC includes the following major components : periodic listen and sleep, collision avoidance, overhearing avoidance, and message passing. I’m going to talk about each of them in details.

Latency Energy Coordinated Sleeping
Problem: Idle listening consumes significant energy Solution: Periodic listen and sleep sleep listen Turn off radio when sleeping Reduce duty cycle to ~ 10% (120ms on/1.2s off) As we said, idle listening is a big problem. Listening consumes significant amount of energy. Our solution is to put nodes into periodic sleep state. After sleeping for some time, each node wakes up and listens to see if anyone wants to talk to it. If yes, it will stay awake. If no, it will go to sleep again. During the sleep time, the node turns off its radio. In our implementation , we have reduced the node duty cycle to about 10%, which is listening for 200 milliseconds and sleeping for 2 seconds. The major tradeoff here is the latency vs. energy savings. Latency is increased due to the periodic sleep. Latency Energy

Coordinated Sleeping Schedules can differ Node 1 Node 2 Schedule 1
listen Prefer neighboring nodes have same schedule — easy broadcast & low control overhead Schedule 2 Schedule 1 Before nodes perform periodic listen and sleep, they need to choose a schedule about when to listen and when to sleep. This figure shows that even if two nodes have different schedules, they can still talk to each other as long as they know each others’ schedules. For example, if node 1 wants to talk to node 2, it just wait until node 2 is listening. However, we prefer neighboring nodes to have the same schedule, so that it’s easy to do broadcast and the control overhead is low. But in a large network, we cannot guarantee that all nodes follow the same schedule. For example, in this figure, there are two different schedules on each side. The node on the border will follow both schedules. When it broadcasts a packet, it needs to do it twice, first for nodes on schedule 1 and then for those on schedule 2. Border nodes: two schedules or broadcast twice

Coordinated Sleeping Schedule Synchronization
New node tries to follow an existing schedule Remember neighbors’ schedules — to know when to send to them Each node broadcasts its schedule every few periods of sleeping and listening Re-sync when receiving a schedule update Periodic neighbor discovery Keep awake in a full sync interval over long periods Here are some procedures for synchronization on schedules. Nodes need to remember their neighbors’ schedules so that they know when to send to each other. Each node periodically broadcasts its schedule and re-synchronizes on a neighbor’s schedule when receiving an update. This prevents long-term clock drift. The schedule packets also serve as beacons for new nodes to join a neighborhood.

Coordinated Sleeping t1 t2 Adaptive listening
Reduce multi-hop latency due to periodic sleep Wake up for a short period of time at end of each transmission 1 2 3 4 RTS CTS CTS t1 t2 listen listen Reduce latency by at least half

Collision Avoidance S-MAC is based on contention
Similar to IEEE ad hoc mode (DCF) Physical and virtual carrier sense Randomized backoff time RTS/CTS for hidden terminal problem RTS/CTS/DATA/ACK sequence The second component in S-MAC is collision avoidance. If multiple senders want to talk to the same receiver, they need to avoid collisions. We argue that contention-based protocols have better scalability in node density than TDMA protocols, and S-MAC is contention based. The collision avoidance procedure is similar to that in ad hoc mode. That is, RTS/CTS/DATA/ACK sequence.

Overhearing Avoidance
Problem: Receive packets destined to others Solution: Sleep when neighbors talk Basic idea from PAMAS (Singh, Raghavendra 1998) But we only use in-channel signaling Who should sleep? All immediate neighbors of sender and receiver How long to sleep? The duration field in each packet informs other nodes the sleep interval The third component in S-MAC is overhearing avoidance. Receiving packets destined to other nodes is a waste of energy. The basic solution is to put a node into sleep when its neighbors are talking. This idea is from PAMAS. PAMAS uses a second control channel to achieve the goal. In our solution, we only use in-channel signaling. To appropriately put nodes into sleep, we need to answer two questions. The first is, who should sleep. The short answer is, all immediate neighbors of the sender and receiver should go to sleep. The second question is, how long for them to sleep. In S-MAC, each packet has a duration field, which is the remaining time that is needed for current transmission. If a node receives any packet from its neighbor, it will learn from the duration field about how long it should sleep.

Energy Fairness Msg-level latency Message Passing
Problem: Sensor net in-network processing requires entire message Solution: Don’t interleave different messages Long message is fragmented & sent in burst RTS/CTS reserve medium for entire message Fragment-level error recovery — ACK — extend Tx time and re-transmit immediately Other nodes sleep for whole message time The last component in S-MAC is message passing. It is motivated by the in-network data processing, which requires efficient transmission of a meaningful unit of message, which can be quite long. Our approach is to fragment a long message into short ones, and transmit them in burst. The key is that do not interleave the transmission of different messages, since the receiver cannot start data processing if only partial of a message is received. The RTS and CTS reserve medium for the entire message. The receiver will send ACK for each received fragment. If an ACK is not received, the sender will extend transmission time and immediately re-transmit current fragment. Other nodes will sleep for long time until the whole message transmission is done. The major tradeoff here is, the fairness vs. energy and message-level latency. It’s unfair for a node with a short message to wait for a long transmission even if there are errors in the middle. Energy savings is obtained by putting nodes into sleep for long time. Message-level latency can be reduced by not interleaving different messages and by the fast retransmission of erroneous fragments. Fairness Energy Msg-level latency

Implementation on Testbed Nodes
Platform Mica Motes (UC Berkeley) 8-bit CPU at 4MHz, 128KB flash, 4KB RAM 20Kbps radio at 433MHz TinyOS: event-driven Configurable S-MAC options Low duty cycle with adaptive listen Low duty cycle without adaptive listen Fully active mode (no periodic sleeping)

Experiments: two-hop network
Topology and measured energy consumption on source nodes 2 4 6 8 10 200 400 600 800 1000 1200 1400 1600 1800 Average energy consumption in the source nodes Message inter-arrival period (second) Energy consumption (mJ) like protocol without sleep Overhearing avoidance S-MAC w/o adaptive listen Source 1 Source 2 Sink 1 Sink 2 S-MAC consumes much less energy than like protocol w/o sleeping At heavy load, overhearing avoidance is the major factor in energy savings At light load, periodic sleeping plays the key role We used a simple topology in our experiments. It’s a two-hop network with 2 source nodes and two sinks. In each test, there are 10 messages generated on each source node. Each message has 10 fragments, and each fragment has 40 bytes. We measure the total energy consumption of each node for sending this fixed amount of data. This is the measured energy consumption on the source nodes. The X-axis indicates the traffic load. It’s denoted by the message inter-arrival time in seconds. For example, the point 2 means that every 2 seconds, there will be a message generated on each source node. So a small value indicates a heavy traffic load. The Y-axis is the energy consumption of the radio in milli-joule. The red line is the result of the like protocol without sleeping. The blue line is the message passing plus overhearing avoidance. The black one is the complete S-MAC which incorporates the periodic listen and sleep. We can see that in all cases, the blue line and black line outperform the always listening MAC. When traffic load is high, the blue line and the black line are about the same. In this case, there are few chances to go to periodic sleep, and the energy savings is mainly due to the overhearing avoidance. When traffic becomes lighter, the periodic sleep plays a key role, and makes S-MAC much better than the always-listening MAC.

Energy Consumption over Multi-Hops
Ten-hop linear network at different traffic load 3 configurations of S-MAC 2 4 6 8 10 5 15 20 25 30 Message inter-arrival period (S) Energy consumption (J) 10% duty cycle without adaptive listen No sleep cycles 10% duty cycle with adaptive listen Energy consumption at different traffic load At light traffic load, periodic sleeping has significant energy savings over fully active mode Adaptive listen saves more at heavy load by reducing latency

Latency as Hops Increase
Adaptive listen significantly reduces latency causes by periodic sleeping 2 4 6 8 10 12 Latency under lowest traffic load 2 4 6 8 10 12 Latency under highest traffic load 10% duty cycle without adaptive listen 10% duty cycle without adaptive listen Average message latency (S) Average message latency (S) 10% duty cycle with adaptive listen 10% duty cycle with adaptive listen No sleep cycles No sleep cycles Number of hops Number of hops

Throughput as Hops Increase
Adaptive listen significantly increases throughput 2 4 6 8 10 20 40 60 80 100 120 140 160 180 200 220 Effective data throughput under highest traffic load Using less time to pass the same amount of data No sleep cycles Effective data throughput (Byte/S) 10% duty cycle with adaptive listen 10% duty cycle without adaptive listen Number of hops

Combined Energy and Throughput
Energy-time cost on passing 1-byte data from source to sink 2 4 6 8 10 0.5 1 1.5 2.5 3 Periodic sleeping provides excellent performance at light traffic load With adaptive listening, S-MAC achieves about the same performance as no-sleep mode at heavy load No sleep cycles Energy-time product per byte (J*S/byte) 10% duty cycle without adaptive listen 10% duty cycle with adaptive listen Message inter-arrival period (S)

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