1. Protocols in Wireless Sensor Networks
From Vision to Reality

2. ZigBee and
The MAC Layer

3. The ZigBee Alliance Solution
Targeted at home and building automation and controls, consumer electronics, toys etc.
Industry standard (IEEE radios)
Primary drivers are simplicity, long battery life, networking capabilities, reliability, and cost
Short range and low data rate

4. SHORT < RANGE > LONG LOW < DATA RATE > HIGH
The Wireless Market
TEXT
GRAPHICS
INTERNET
HI-FI
AUDIO
STREAMING
VIDEO
DIGITAL
MULTI-CHANNEL
LAN
802.11b
802.11a/HL2 & g
SHORT < RANGE > LONG
Bluetooth 2
ZigBee
PAN
Bluetooth1
LOW < DATA RATE > HIGH

5. LIGHT COMMERCIAL CONTROL
Applications
BUILDING
AUTOMATION
CONSUMER ELECTRONICS
security
HVAC
AMR
lighting control
access control TV
VCR
DVD/CD
remote
patient monitoring
fitness monitoring
PERSONAL HEALTH CARE
PC & PERIPHERALS
ZigBee
Wireless Control that
Simply Works
mouse
keyboard
joystick
Future applications include Toys and Games, like consoles controllers, portable game pads (like gameboys), educational toys like leap frog stuff, and fun toys like RC (remote control) toys, etc.
INDUSTRIAL
CONTROL
RESIDENTIAL/
LIGHT COMMERCIAL CONTROL
asset mgt
process control
environmental
energy mgt
security
HVAC
lighting control
access control
lawn & garden irrigation

6. Development of the Standard
ZigBee Alliance
50+ companies
Defining upper layers of protocol stack: from network to application, including application profiles
IEEE Working Group
Defining lower layers : MAC and PHY
APPLICATION
Customer
ZIGBEE STACK
ZigBee
Alliance
SILICON
IEEE

8. 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
MHz: 10 channels, 40 kbps
Works well for:
Long battery life, selectable latency for controllers, sensors, remote monitoring and portable electronics

9. ZigBee Application Framework
IEEE 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
Low complexity:
26 service primitives
versus
131 service primitives
for
(Bluetooth)
ZigBee Application Framework
Networking App Layer (NWK)
Channel scan for beacon is included, but it is left to the network layer to implement dynamic channel selection
Data Link Controller (DLC)
IEEE LLC
IEEE 802.2
LLC, Type I
IEEE MAC
IEEE
IEEE
868/915 MHz PHY
2400 MHz PHY

10. ZigBee Topology Models
Mesh
Star
ZigBee coordinator
Cluster Tree
ZigBee Routers
ZigBee End Devices

11. 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 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!

12. ZigBee and Bluetooth Optimized for different applications 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.

13. ZigBee protocol is optimized for timing critical applications
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

14. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks

15. 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?

16. Directed Diffusion Data centric Request driven
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

17. 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

18. 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:
Instance = elephant
Location = [125, 220]
Intensity = 0.6
Confidence = 0.85
Timestamp = 01:20:40
Attribute-Value pairs, no advanced naming scheme

19. 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

20. Interest Propagation Flood interest
Constrained or Directional flooding based on location is possible
Directional propagation based on previously cached data
Gradient
Source
Interest
Sink

21. Data Propagation Multipath routing
Consider each gradient’s link quality
Gradient
Source
Data
Sink

22. 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

23. 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

24. Summary of the protocol

25. 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

26. Design Considerations

27. Evaluation ns2 simulation
Modified 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

28. 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

29. Metrics Average dissipated energy Average delay
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

30. 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
Average Dissipated Energy
(Joules/Node/Received Event)
0.008
Omniscient Multicast
0.006
Diffusion
0.004
0.002 50
100
150
200
250
300
Network Size

31. Impact of In-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 50
100
150
200
250
300
Network Size

32. 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 50
100
150
200
250
300
Network Size
Reducing high-rate paths in steady state is critical

33. 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 50
100
150
200
250
300
Network Size
Standard is dominated by idle energy

34. Failures Dynamic failures Each source sends different signals
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?

35. 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?

36. Conclusions Data-centric communication between sources and sinks
Aggregation and duplicate suppression
More thorough performance evaluation is required

37. Extensions Push diffusion One-phase pull 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

38. 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

39. 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

40. 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?

41. Geographic Routing for Sensor Networks

42. 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

43. Geographic Routing: Greedy Routing
Closest to D A S D
Find neighbors who are the closer to the destination
Forward the packet to the neighbor closest to the destination

44. Greedy Forwarding does NOT always work
GF fails
If the network is dense enough that each interior node has a neighbor in every 2/3 angular sector, GF will always succeed

45. 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

46. Impact of Sensing Coverage on Greedy Geographic Routing Algorithms
Guoliang Xing, Chenyang Lu, Robert Pless, Qingfeng Huang
IEEE Trans. Parallel Distributed System

47. Metrics b v u c a

48. 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.

49. 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).

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

51. Haiyun Luo Fan Ye, Jerry Cheng Songwu Lu, Lixia Zhang UCLA CS Dept.
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.

52. Sensor Network Model
Sink
Stimulus
Source

53. Mobile Sink Excessive Power Consumption Increased Wireless
Transmission
Collisions
State Maintenance
Overhead

54. TTDD Basics Dissemination Node Data Announcement Source Data Sink
Query
Immediate
Dissemination
Node

55. TTDD Mobile Sinks Dissemination Node Trajectory Data Announcement
Forwarding
Data Announcement
Source
Immediate
Dissemination
Node
Data
Sink
Immediate
Dissemination
Node
Trajectory
Forwarding

56. TTDD Multiple Mobile Sinks
Source
Dissemination Node
Trajectory
Forwarding
Data Announcement
Source
Immediate
Dissemination
Node
Data

57. Conclusion TTDD: two-tier data dissemination Model Proactive sources
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