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Protocols in Wireless Sensor Networks. From Vision to Reality. ZigBee and 802.15.4. The MAC Layer. The ZigBee Alliance Solution. Targeted at home and building automation and controls, consumer electronics, toys etc. Industry standard (IEEE 802.15.4 radios)

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zigbee and 802 15 4

ZigBee and 802.15.4

The MAC Layer

the zigbee alliance solution
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
the wireless market

TEXT

GRAPHICS

INTERNET

HI-FI

AUDIO

STREAMING

VIDEO

DIGITAL

VIDEO

MULTI-CHANNEL

VIDEO

The Wireless Market

LAN

802.11b

802.11a/HL2 & 802.11g

SHORT < RANGE > LONG

Bluetooth 2

ZigBee

PAN

Bluetooth1

LOW < DATA RATE > HIGH

applications
Applications

BUILDING

AUTOMATION

CONSUMER ELECTRONICS

security

HVAC

AMR

lighting control

accesscontrol

TV

VCR

DVD/CD

remote

PC & PERIPHERALS

patient monitoring

fitness monitoring

PERSONAL HEALTH CARE

ZigBee

Wireless Control that

Simply Works

mouse

keyboard

joystick

INDUSTRIAL

CONTROL

RESIDENTIAL/

LIGHT COMMERCIAL CONTROL

asset mgt

process control

environmental

energy mgt

security

HVAC

lighting control

access control

lawn & garden irrigation

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

Development of the Standard

Customer

APPLICATION

ZIGBEE STACK

ZigBee

Alliance

SILICON

IEEE

802.15.4

ieee 802 15 4 basics
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
ieee 802 15 4 standard
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

ZigBee Application Framework

  • Low complexity:
  • 26 service primitives
  • versus
  • 131 service primitives
  • for 802.15.1
  • (Bluetooth)

Networking App Layer (NWK)

Data Link Controller (DLC)

IEEE 802.2

IEEE 802.15.4 LLC

LLC, Type I

IEEE 802.15.4 MAC

IEEE 802.15.4

IEEE 802.15.4

868/915 MHz PHY

2400 MHz PHY

zigbee topology models
ZigBee Topology Models

Mesh

Star

ZigBee coordinator

Cluster Tree

ZigBee Routers

ZigBee End Devices

ieee 802 15 4 device types
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!
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.

ZigBee and Bluetooth

Optimized for different applications

zigbee and bluetooth1
ZigBee and Bluetooth

Timing Considerations

  • ZigBee:
  • Network join time = 30ms typically
  • Sleeping slave changing to active = 15ms typically
  • Active slave channel access time = 15ms typically
  • Bluetooth:
  • Network join time = >3s
  • Sleeping slave changing to active = 3s typically
  • Active slave channel access time = 2ms typically

ZigBee protocol is optimized for timing critical applications

motivation
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?
directed diffusion
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
motivating example
Motivating Example
  • Sensor nodes are monitoring animals
  • Users are interested in receiving data for all 4-legged creatures seen in a rectangle
  • Usersspecify the data rate
interest and event naming
Interest and Event Naming
  • Query/interest:
    • Type=four-legged animal
    • Interval=20ms (event data rate)
    • Duration=10 seconds (time to cache)
    • Rect=[-100, 100, 200, 400]
  • Reply:
    • Type=four-legged animal
    • Instance = elephant
    • Location = [125, 220]
    • Intensity = 0.6
    • Confidence = 0.85
    • Timestamp = 01:20:40
  • Attribute-Value pairs, no advanced naming scheme
directed diffusion1
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
interest propagation
Interest Propagation
  • Flood interest
  • Constrained or Directional flooding based on location is possible
  • Directional propagation based on previously cached data

Gradient

Source

Interest

Sink

data propagation
Data Propagation
  • Multipath routing
    • Consider each gradient’s link quality

Gradient

Source

Data

Sink

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

Gradient

Source

Data

Reinforcement

Sink

negative reinforcement
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

Gradient

Source

Data

Reinforcement

Sink

sampling forwarding
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
evaluation
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
slide28
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
metrics
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
average dissipated energy
Average Dissipated Energy

They claim diffusion can outperform omniscient multicast due to

in-network processing & suppression. For example, multiple sources can detect a four-legged animal in one area.

0.018

0.016

Flooding

0.014

0.012

0.01

0.008

Omniscient Multicast

(Joules/Node/Received Event)

Average Dissipated Energy

0.006

Diffusion

0.004

0.002

0

0

50

100

150

200

250

300

Network Size

impact of in network processing
Impact ofIn-network Processing

0.025

Diffusion Without Suppression

0.02

0.015

(Joules/Node/Received Event)

Average Dissipated Energy

0.01

Diffusion With Suppression

0.005

0

0

50

100

150

200

250

300

Network Size

impact of negative reinforcement
Impact of Negative Reinforcement

0.012

0.01

Diffusion Without Negative Reinforcement

0.008

Average Dissipated Energy

(Joules/Node/Received Event)

0.006

0.004

Diffusion With Negative Reinforcement

0.002

0

0

50

100

150

200

250

300

Network Size

Reducing high-rate paths in steady state is critical

average dissipated energy 802 11 energy model
Average Dissipated Energy (802.11 energy model)

0.14

Diffusion

0.12

Flooding

Omniscient Multicast

0.1

0.08

Average Dissipated Energy

(Joules/Node/Received Event)

0.06

0.04

0.02

0

0

50

100

150

200

250

300

Network Size

  • Standard 802.11 is dominated by idle energy
failures
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?
analysis
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?
conclusions
Conclusions
  • Data-centric communication between sources and sinks
  • Aggregation and duplicate suppression
  • More thoroughperformance evaluation is required
extensions
Extensions
  • 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
  • One-phase pull
    • Propagate interest
    • A receiving node pick the link that delivered the interest first
    • Assumes the link bidirectionality
teen threshold sensitive energy efficient sensor network protocol
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
leach hicss00
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
discussions
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?
motivation1
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
geographic routing greedy routing

Closest to D

A

Geographic Routing: Greedy Routing

S

D

  • Find neighbors who are the closer to the destination
  • Forward the packet to the neighbor closest to the destination
greedy forwarding does not always work
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

slide45

Dealing with Void

  • Apply the right-hand rule to traverse the edges of a void
    • Pick the next anticlockwise edge
    • Traditionally used to get out of a maze
impact of sensing coverage on greedy geographic routing algorithms
Impact of Sensing Coverage on Greedy Geographic Routing Algorithms

Guoliang Xing, Chenyang Lu, Robert Pless, Qingfeng Huang

IEEE Trans. Parallel Distributed System

metrics1
Metrics

b

v

u

c

a

theorem
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.
gf always finds a next hop node
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).
theorem1
Theorem
  • In a sensing-covered network, GF can always find a routing path between source u and destination v no longer than hops.
ttdd a two tier data dissemination model for large scale wireless sensor networks

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.

sensor network model

Sink

Sink

Sensor Network Model

Stimulus

Source

mobile sink
Mobile Sink

Excessive Power

Consumption

Increased Wireless

Transmission

Collisions

State Maintenance

Overhead

ttdd basics

Source

Sink

TTDD Basics

Dissemination Node

Data Announcement

Data

Query

Immediate

Dissemination

Node

ttdd mobile sinks

Source

Sink

Trajectory

Forwarding

TTDD Mobile Sinks

Dissemination Node

Trajectory

Forwarding

Data Announcement

Immediate

Dissemination

Node

Data

Immediate

Dissemination

Node

ttdd multiple mobile sinks

Source

Source

TTDD Multiple Mobile Sinks

Dissemination Node

Trajectory

Forwarding

Data Announcement

Immediate

Dissemination

Node

Data

conclusion
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
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