# ENERGY: THE ROOT OF ALL PERVASIVENESS April 29, 2004 Anthony Ephremides

```ENERGY: THE ROOT OF ALL
PERVASIVENESS
Anthony Ephremides
University of Maryland
April 29, 2004
1
“PERVASIVE” NETWORKING
• Ability to access the network
(“Anywhere,” Anytime,” “Anyone”)
• Focus on wireless
(multihop)
2
KEY ELEMENTS
• Wireless Channel
• interference
• SINR &lt;&gt;
• Portable Energy Supply
• efficiency
vs.
• limited
– CROSS-LAYER COUPLING
3
TWO ILLUSTRATIONS
1. MAC/ROUTING
with
Energy Metrics
2. Processing vs. Transmission
in
Sensor Networks
4
MAC/ROUTING
• Routing algorithm
• MAC assigns resources to competing flows
• Actual link throughput depends on MAC
• New link quality metric values
• Routing Algorithm
new flows
5
NETWORK
•
•
•
•
Single channel – slotted time
Separate control channel
Single transceiver per node
Power control - Pmax
– regulate interference
– save energy
• Simple attenuation model
– free-space, distance based
• SINR
•
&gt;
&lt;
6
SCHEDULING
Scheduling Rules:
• A node can only be associated with one active link at a time.
PG
i ij
• SIR requirements are satisfied.
2
G : Attenuation fator of link(i,j).    Pk Gkj
ij
k i
• The link with the lowest metric has the top priority.

Qk
neighbors of i or j
   j 
1
k i
D  i, j   a 
b
 c  i
.
1  Qi
1
 Qk   max 
neighbors of i or j
k i
Qi : Total queue size at node i.
i  j  : Average traffic rate at link (i,j).
 max : Maximum rate among all links.
7
Scheduling Algorithms:
1. Power is preset. Links are added (if SIRs are satisfied) in the
order of link metric. Easy for distributed implementation.
satisfied) in the order of link metric. Difficult for distributed
implementation.
3. First find maximal number of links that can coexist, then run
iterative power control. Remove links until SIRs are satisfied.
Difficult for distributed implementation.
8
Simulation Results
No rerouting.
Throughput and Delay for different scheduling algorithms.
9
JOINT SCHEDULING AND
ROUTING
Routing: Bellman-Ford algorithm with routing distance:
 Qij
Dij  d  
 Qmax


 Rij 
  e 
 .

 Rmax 
Qij : Queue size at link (i,j),
Qmax : Buffer size for each link,
Rij : Distance between node i and j,
Rmax : Maximal reachable distance.
10
CONTROL OF
SENSOR NETWORKS
• Application : Major Driver
• But, in all cases: Longevity
(energy efficiency)
• Major Challenges:
– MAC
– Routing
(map application-related objective function to link
metric or MAC priority)
11
SENSOR NETWORK FOR
DETECTION
control node
Ignore Routing Component
12
MODEL
1
2
K
control center
Each Node
Collects Independently
T independent
Binary Measurements
• Simplified Model
13
MODEL (cont.)
Three Operating Options
- Centralized :
All data transmitted to CC
- Distributed :
Each node decides &amp; transmits its decision to CC
- Quantized :
Each node sends a quantized M-bit quantity to CC
where 1  M  log 2 (T  1)
14
ENERGY CONSUMPTION
ANALYSIS
- Energy for Data Processing
- based on # of comparisons
EP  Ec * c
- E c represents the energy consumed for one comparison
- c is the # of comparisons
- Energy for Transmission
- based on the distance from sensor nodes to control center and # of bits
2
transmitted ET  Et * d * t
- Et represents the energy consumed for transmitting one bit data
over a unit distance, for a fixed communication system
- d represents the distance from sensor nodes to control center
- t is the # of bits transmitted
- Total Energy
E  EP  ET  Ec * c  Et * d 2 * t
15
ENERGY CONSUMPTION
ANALYSIS (cont.)
- Energy Consumption per Node for Three Options
- Centralized Option
- option 1: transmit all observations to CC
E  Et * d 2 * T
- option 2: transmit # of 1 out of T observations to CC
E  Ec * T  Et * d 2 *log 2 (T  1)
- Distributed Option
E  Ec *(T  1)  Et * d 2
- Quantized Option (suboptimal solution)
E  Ec *[T  x]  Et * d 2 * M
where
x represents the expected # of comparisons needed for
the suboptimal
solution, which is a function of T , M , p, p0 , p1
16
Energy Consumption
Analysis (cont.)
- Energy Consumption vs. Accuracy
fix K  4 , p  0.5 , p0  0.2 , p1  0.7 , M  2; vary T  3 ~ 10
example 1: Ec  5 nJ bit , Et  0.2 nJ (bit * m 2 ) , d  10m
example 2: Ec  20 nJ bit , Et  0.05 nJ (bit * m2 ) , d  10m
17
NEXT STEPS
1. Spatial/Temporal Correlation
2. Routing (map objective function to link metric)
MORE FUNDAMENTALLY
1. Couple processing energy (dictated by the chosen
SP algorithm) to the embedded system design.
(memory management, signal flow graphs for
software vs. hardware split, computing fabric)
2. Trade-off transmission to processing under such
“INTERACTIVE” design (ultimate cross-layering)
18
CONCLUSIONS
1. Holistic cross-layer design from energy point
of view
2. Application dependency/exploitation
3. “It takes courage to succeed”
“It takes energy to be pervasive”
19
```