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Protocols in Wireless Sensor Networks

From Vision to Reality

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ZigBee and 802.15.4

The MAC Layer

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The ZigBee Alliance Solution

• Targeted at home and building automation and controls, consumer electronics, toys etc.

• Industry standard (IEEE 802.15.4 radios)

• Primary drivers are simplicity, long battery life, networking capabilities, reliability, and cost

• Short range and low data rate

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The Wireless MarketS

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AN

GE

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ON

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LOW < DATA RATE > HIGH

PAN

LAN

TEXT GRAPHICS INTERNET HI-FI AUDIO

STREAMINGVIDEO

DIGITALVIDEO

MULTI-CHANNELVIDEO

Bluetooth1

Bluetooth 2

ZigBee

802.11b

802.11a/HL2 & 802.11g

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Applications

ZigBeeWireless Control that

Simply Works

RESIDENTIAL/LIGHT

COMMERCIAL CONTROL

CONSUMER ELECTRONICS

TVVCRDVD/CDremote

securityHVAClighting controlaccess controllawn & garden irrigation

PC & PERIPHERALS

INDUSTRIALCONTROL

asset mgtprocess control

environmentalenergy mgt

PERSONAL HEALTH CARE

BUILDING AUTOMATION

securityHVAC

AMRlighting control

access control

mousekeyboardjoystick

patient monitoring

fitness monitoring

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Development of the Standard

• ZigBee Alliance

– 50+ companies

– Defining upper layers of protocol stack: from network to application, including application profiles

• IEEE 802.15.4 Working Group

– Defining lower layers : MAC and PHY

SILICON

ZIGBEE STACK

APPLICATION Customer

IEEE802.15.4

ZigBee Alliance

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IEEE 802.15.4 Basics• 802.15.4 is a simple packet data protocol:

– CSMA/CA - Carrier Sense Multiple Access with collision avoidance

– Optional time slotting and beacon structure– Three bands, 27 channels specified

• 2.4 GHz: 16 channels, 250 kbps• 868.3 MHz : 1 channel, 20 kbps• 902-928 MHz: 10 channels, 40 kbps

• Works well for:– Long battery life, selectable latency for

controllers, sensors, remote monitoring and portable electronics

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IEEE 802.15.4 standard• Includes layers up to and including Link Layer

Control– LLC is standardized in 802.1

• Supports multiple network topologies including Star, Cluster Tree and Mesh

IEEE 802.15.4 MAC

IEEE 802.15.4 LLC IEEE 802.2LLC, Type I

IEEE 802.15.42400 MHz PHY

IEEE 802.15.4868/915 MHz PHY

Data Link Controller (DLC)

Networking App Layer (NWK)

ZigBee Application Framework• Low complexity:

26 service primitives

versus 131 service primitives for 802.15.1 (Bluetooth)

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ZigBee Topology Models

ZigBee coordinatorZigBee RoutersZigBee End Devices

Star

Mesh

Cluster Tree

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IEEE 802.15.4 Device Types• Three device types

– Network Coordinator• Maintains overall network knowledge; most

memory and computing power– Full Function Device

• Carries full 802.15.4 functionality and all features specified by the standard; ideal for a network router function

– Reduced Function Device• Carriers limited functionality; used for network

edge devices• All of these devices can be no more complicated than

the transceiver, a simple 8-bit MCU and a pair of AAA batteries!

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ZigBee and Bluetooth

• ZigBee– Smaller packets

over large network– Mostly Static

networks with many, infrequently used devices

– Home automation, toys remote controls

– Energy saver!!!

• Bluetooth– Larger packets over small

network– Ad-hoc networks– File transfer; streaming – Cable replacement for items

like screen graphics, pictures, hands-free audio, Mobile phones, headsets, PDAs, etc.

Optimized for different applications

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Bluetooth:• Network join time = >3s• Sleeping slave changing to active = 3s typically• Active slave channel access time = 2ms typically

ZigBee:• Network join time = 30ms typically • Sleeping slave changing to active = 15ms typically• Active slave channel access time = 15ms typically

Timing Considerations

ZigBee protocol is optimized for timing critical applications

ZigBee and Bluetooth

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Directed Diffusion:A Scalable and Robust

Communication Paradigm for Sensor Networks

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Motivation

• Properties of Sensor Networks– Data centric– No central authority– Resource constrained– Nodes are tied to physical locations– Nodes may not know the topology– Nodes are generally stationary

• How can we get data from the sensors?

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Directed Diffusion

• Data centric – Individual nodes are unimportant

• Request driven– Sinks place requests as interests– Sources satisfying the interest can be found– Intermediate nodes route data toward sinks

• Localized repair and reinforcement• Multi-path delivery for multiple sources,

sinks, and queries

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Motivating Example• Sensor nodes are monitoring animals

• Users are interested in receiving data for all 4-legged creatures seen in a rectangle

• Users specify the data rate

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Interest and Event Naming• Query/interest:

1. Type=four-legged animal2. Interval=20ms (event data rate)3. Duration=10 seconds (time to cache)4. Rect=[-100, 100, 200, 400]

• Reply:1. Type=four-legged animal2. Instance = elephant3. Location = [125, 220]4. Intensity = 0.65. Confidence = 0.856. Timestamp = 01:20:40

• Attribute-Value pairs, no advanced naming scheme

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Directed Diffusion

• Sinks broadcast interest to neighbors– Initially specify a low data rate just to find sources

for minimal energy consumptions

• Interests are cached by neighbors• Gradients are set up pointing back to where

interests came from • Once a source receives an interest, it routes

measurements along gradients

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Interest Propagation• Flood interest

• Constrained or Directional flooding based on location is possible

• Directional propagation based on previously cached data

Source

Sink

Interest

Gradient

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Data Propagation

• Multipath routing – Consider each gradient’s link quality

Source

Sink

Gradient

Data

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Reinforcement

• Reinforce one of the neighbor after receiving initial data.– Neighbor who consistently performs better than others– Neighbor from whom most events received

Source

Sink

Gradient

Data

Reinforcement

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Negative Reinforcement

• Explicitly degrade the path by re-sending interest with lower data rate.

• Time out: Without periodic reinforcement, a gradient will be torn down

Source

Sink

Gradient

Data

Reinforcement

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Summary of the protocol

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Sampling & forwarding

• Sensors match signature waveforms from codebook against observations

• Sensors match data against interest cache, compute highest event rate request from all gradients, and (re) sample events at this rate

• Receiving node:– Find matching entry in interest cache

• If no match, silently drop– Check and update data cache (loop prevention,

aggregation)– Resend message along all the active gradients,

adjusting the frequency if necessary

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Design Considerations

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Evaluation

• ns2 simulation• Modified 802.11 MAC for energy use calculation

– Idle time: 35mW– Receive: 395mw– Transmit: 660mw

• Baselines– Flooding – Omniscient multicast: A source multicast its event to all

sources using the shortest path multicast tree – Do not consider the tree construction cost

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• Simulate node failures• No overload• Random node placement

– 50 to 250 nodes (increment by 50)– 50 nodes are deployed in 160m * 160m

• Increase the sensor field size to keep the density constant for a larger number of nodes

– 40m radio range

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Metrics

• Average dissipated energy– Ratio of total energy expended per node to number of

distinct events received at sink– Measures average work budget

• Average delay– Average one-way latency between event transmission and

reception at sink– Measures temporal accuracy of location estimates

• Both measured as functions of network size

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Average Dissipated Energy

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0 50 100 150 200 250 300

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Network Size

DiffusionDiffusion

Omniscient MulticastOmniscient Multicast

FloodingFlooding

They claim dThey claim diffusion iffusion can can outperform omniscient multicastoutperform omniscient multicast due to due toin-network processing & suppression. For example, multiple in-network processing & suppression. For example, multiple

sources can detect a four-legged animal in one area.sources can detect a four-legged animal in one area.

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Impact of In-network Processing

0

0.005

0.01

0.015

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0 50 100 150 200 250 300

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Network Size

Diffusion With Diffusion With SuppressionSuppression

Diffusion Without Diffusion Without SuppressionSuppression

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Impact of Negative Reinforcement

0

0.002

0.004

0.006

0.008

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0.012

0 50 100 150 200 250 300

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Network Size

Diffusion With Negative Diffusion With Negative ReinforcementReinforcement

Diffusion Without Diffusion Without Negative ReinforcementNegative Reinforcement

Reducing high-rate paths in steady state is criticalReducing high-rate paths in steady state is critical

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Average Dissipated Energy (802.1802.111 energy model)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 50 100 150 200 250 300

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Network Size

DiffusionDiffusion

Omniscient MulticastOmniscient MulticastFloodingFlooding

Standard 802.11 is dominated by idle energyStandard 802.11 is dominated by idle energy

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Failures

• Dynamic failures – 10-20% failure at any time

• Each source sends different signals• <20% delay increase, fairly robust• Energy efficiency improves:

– Reinforcement maintains adequate number of high quality paths

– Shouldn’t it be done in the first place?

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Analysis

• Energy gains are dependent on 802.11 energy assumptions

• Can the network always deliver at the interest’s requested rate?

• Can diffusion handle overloads?

• Does reinforcement actually work?

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Conclusions

• Data-centric communication between sources and sinks

• Aggregation and duplicate suppression

• More thorough performance evaluation is required

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Extensions

• One-phase pull– Propagate interest– A receiving node pick the link that

delivered the interest first– Assumes the link bidirectionality

• Push diffusion– Sink does not flood interest– Source detecting events disseminate

exploratory data across the network– Sink having corresponding interest reinforces

one of the paths

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TEEN (Threshold-sensitive Energy Efficient sensor Network protocol)

• Push-based data centric protocol

• Nodes immediately transmit a sensed value exceeding the threshold to its cluster head that forwards the data to the sink

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LEACH [HICSS00]

• Proposed for continuous data gathering protocol

• Divide the network into clusters• Cluster head periodically collect &

aggregate/compress the data in the cluster using TDMA

• Periodically rotate cluster heads for load balancing

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Discussions

• Criteria to evaluate data-centric routing protocols?– Or, what do we need to try to optimize?

Energy consumption? Data timeliness? Resilience? Confidence of event detection? Too many objectives already? Can we pick just one or two?

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Geographic Routing for Sensor Networks

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Motivation• A sensor net consists of hundreds or thousands of nodes

– Scalability is the issue– Existing ad hoc net protocols, e.g., DSR, AODV, ZRP, require

nodes to cache e2e route information– Dynamic topology changes– Mobility

• Reduce caching overhead– Hierarchical routing is usually based on well defined, rarely

changing administrative boundaries– Geographic routing

• Use location for routing

• Assumptions – Every node knows its location

• Positioning devices like GPS • Localization

– A source can get the location of the destination

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Geographic Routing: Greedy Routing

S D

Closest to D

A

- Find neighbors who are the closer to the destination- Forward the packet to the neighbor closest to the destination

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Greedy Forwarding does NOT always work

If the network is dense enough that each interior node has a neighbor in every 2/3 angular sector, GF will always succeed

GF fails

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Dealing with Void

Apply the right-hand rule to traverse the edges of a voidPick the next anticlockwise edgeTraditionally used to get out of a maze

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Impact of Sensing Coverage on Greedy Geographic Routing Algorithms

Guoliang Xing, Chenyang Lu, Robert Pless, Qingfeng Huang

IEEE Trans. Parallel Distributed System

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Metrics

uv

a

b

c

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Theorem.• Definition: A network is sensing-covered if

any point in the deployment region of the network is covered by at least one node.

• In a sensing-covered network, GF can always find a routing path between any two nodes. Furthermore, in each step (other than the last step arriving at the destination), a node can always find a next-hop node that is more than Rc-2Rs closer (in terms of both Euclidean and projected distance) to the destination than itself.

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GF always finds a next-hop node

• Since Rc >> 2Rs, point a must be outside of the sensing circle of si.

• Since a is covered, there must be at least one node, say w, inside the circle C(a, Rs).

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Theorem

• In a sensing-covered network, GF can always find a routing path between source u and destination v no longer than hops.

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TTDD: A Two-tier Data Dissemination Model for Large-scale Wireless Sensor Networks

Haiyun Luo

Fan Ye, Jerry Cheng

Songwu Lu, Lixia Zhang

UCLA CS Dept.

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Sensor Network Model

Source

Stimulus

Sink

Sink

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Mobile Sink

Excessive PowerConsumption

Increased WirelessTransmissionCollisions

State MaintenanceOverhead

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TTDD Basics

Source

Dissemination Node

Sink

Data Announcement

Query

Data

Immediate DisseminationNode

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TTDD Mobile Sinks

Source

Dissemination Node

Sink

Data Announcement

Data

Immediate DisseminationNode

Immediate DisseminationNode

TrajectoryForwarding

TrajectoryForwarding

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TTDD Multiple Mobile Sinks

Source

Dissemination Node

Data Announcement

Data

Immediate DisseminationNode

TrajectoryForwarding

Source

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Conclusion

• TTDD: two-tier data dissemination Model– Exploit sensor nodes being stationary and

location-aware– Construct & maintain a grid structure with low

overhead

• Proactive sources– Localize sink mobility impact

• Infrastructure-approach in stationary sensor networks– Efficiency & effectiveness in supporting mobile

sinks