Data Communication
Data Communication
Introduction
Motivation
Sensor network characteristics
Limited energy resources
Transient nodes and links
Mobility?
No network infrastructure
Indefinite network size
Data communication?
Limited communication range
Collaborating intermediate nodes required
Desirable property
Minimal control overhead
Delivery guarantees
Loop free operation
Good path quality
Source
Destination A
B
Delivery Guarantees
Definition: reachability
Definition: guaranteed delivery
Unicast
Multicast
Geocast
Anycast
Broadcast
Necessary condition: ideal MAC layer
Each message transmission is successful
Can be approximated by acknowledgement scheme
However, what about unidirectional links
However, sometimes messages just get lost
Delivery Guarantees by Flooding?
Pro
Simple
Works also for highly dynamic topology changes
Con
Lots of message duplicates
All nodes always involved
Loops have to be avoided
Redundant message transmissions
Broadcast storms
Memorization of sent messages
Improvements? Single path strategies are desirable to prevent large energy expenditure, but energy consumption is also affected by path quality
Data Communication Approaches
Global
Maintain global view
Proactive or Reactive
Network dynamics?
Close to shortest path
Localized
Detect nodes in vicinity only
1-hop neighbor information
k-hop neighbor information
Do not memorize any traffic
Beaconless reactive approaches
Properties?
Minimal control overhead: no
Delivery guarantees: yes
Loop free operation: yes
Good path quality: yes
Properties?
Minimal control overhead: yes
Delivery guarantees: ?
Loop free operation: ?
Good path quality: ?
Localized approaches scale with any network size
Local message exchange does not depend on network size
Network change affects only nearby nodes
Fundamental question: are such protocols possible at all?
Data Communication
Localized Geographic Greedy
Packet Forwarding
Localized Geographic Routing
Determine own location
Acquire destination’s location
Unicast and Multicast
Geocast
Anycast
Routing message
Constant Size
Stores destination position
Localized forwarding decision
Destination
Source
Greedy Packet Forwarding
Select neighbor with the “best” location regarding the metric being optimized
Each node applies this greedy principle until destination is eventually reached
T S
A B
F
D C
E
Basic Single-Path Strategies
Produce nearly the same path
If successful performance close to SP
Delivery rate decreases significantly in sparse networks
MFR GREEDY
Basic Single Path Strategies
Rationale: try to minimize Euclidean path length a packet has to travel
DIRLoop-Freedom of Greedy Routing
The discussed forwarding based on distance and progress consider nodes in forward direction only to provide loop-free operation (see Fig. (a))
Direction-based strategies do not guarantee loop-free operation (see Fig. (b))
S
A B
D
(a) (b)
Data Communication
Localized Routing Metrics
Energy Efficiency
Energy is a very limited resource in WSNs
Energy efficiency is often a primary optimization goal
How to make data communication energy efficient?
Apply data communication on an energy efficient topology; example:
Run MECN topology control first
Apply greedy routing over MECN links only
Incorporate energy efficiency in the protocol directly; example:
Elaborate an energy aware greedy routing weight
Apply greedy routing using this weight
Definition of energy minimizing localized routing metrics?
Using the Path Loss Formula
Remember: channel model for RF communication
Energy required to send a message from S to T amounts u(d) = d^a + c, while d = |ST|
Observation
Assume an arbitrary number n of equidistant intermediate forwarding nodes can be placed between S and T
Power required to send a message from S to T amounts: n * (d/n)^a + c) = n*(d/n)^a + n*c
Define f(x)=x*(d/x)^a + x*c
Is there an optimal x minimizing f(x)?
If x0 or x
∞then f(x)∞
There exists one solution x
0which satisfies f’(x)=0
S T
d
Using the Path Loss Formula
n = floor(x
0) can be expressed in a closed form
We can compute constant c1
Which depends on a and c only and
Which satisfies n = c1 * |ST|
Power consumption v(|ST|) in this case can be expressed in a closed form as well
We can compute constant c2
Which depends on a and c only and
Which satisfies v(|ST|) = c2 * |ST|
How can we use this result to express a localized
routing metric?
Using the Path Loss Formula
Estimate on total power consumption when selecting next hop node A: u(|SA|) + v(|AD|)
Greedy routing: select the node in forward direction which minimizes the expression u(|SA|) + v(|AD|)
Result directly related to path loss formula d^a + c
What if other models are used?
New theoretical analysis to compute u(.) and v(.) are necessary
Problem if the model function can not be derived
Problem if the power metric is given by empirical values
Other localized metric approaches A
S D
Assume minimal power consumption v(|AD|) on remaining path from A to D Assume power
consumption u(|SA|) to send a message from S to A
The Cost over Progress Framework
Progress achieved by selecting node A: d-t
Assume each node provides same progress
Number of routing steps: d / (d-t)
Assume each routing step consumes energy u(s)
Approximation of total energy consumption: u(s) * (d / (d-t))
Greedy routing: select neighbor A which minimizes u(s) / (d-t)
Observe: any cost function can be plugged into this expression
S D
A t
d s
Increasing Network Lifetime
Define: network lifetime – time it takes until first node dies
Are energy optimized paths increasing network lifetime?
Observation
Selecting minimum energy consuming path p(S,D) will only use nodes on this path
If p(S,D) is used continuously, nodes along this path will die first
This motivates the use of cost metric c(v)
The cost to use node amounts c(v) = 1/g(v)
g(v) reflects v’s remaining power in [0,max_power]
Try to find a path p=v1… vn which reduces total cost
Local approximation: node s selects node v which minimizes c(v) / (|sd| - |vd|)
