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SIGMOD'06 1
Energy-Efficient Monitoring of Extreme Values in Sensor Networks
Adam Silberstein Kamesh Munagala Jun Yang
Duke University
SIGMOD'06 2
Papers
C. Olston, B. Loo, and J. Widom, “Adaptive Precision Setting for Cached Approximate Values,” SIGMOD’01.
A. Silberstein, K. Munagala and J. Yang, "Energy-Efficient Monitoring of Extreme Values in Sensor Networks," SIGMOD '06.
SIGMOD'06 3
Outline
Introduction to Max/Min Query Algorithms
Query non-specificTemporal Suppression (TS)Range Caching (RS)
Query-specificSLAT (single-level adaptive thresholds)SLAT-A (single-level adaptive thresholds with aggregation)HAT (hierarchical adaptive thresholds)
Experimental Results
SIGMOD'06 4
Max Query
Max query Returns (node id, value) for node with highest value in
network
Addressed problem How to energy-efficient continually maintains the max
result
Uses: environmental, machine room monitoring Observe trends, get early alerts
SIGMOD'06 5
Temporal Suppression (TS)
Simple monitoring algorithm Not specifically tailored to query
Node transmits its value if it has changed since its last report Absence of report implies the value has not
changed
SIGMOD'06 6
65
86
30
Temporal Suppression
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6874
72
60
82
74 76
Low value Dropping value
p30
p30
p30
p30
k86
k86
q65
q65
q65
k86, q65
Trigger
SIGMOD'06 7
Range Caching (RS)
Range caching [Olston et al. SIGMOD 01]
Range: [20 60]
Sensed value: 40
Range: [55 80]
Sensed value: 82
Range: [75 90]
Sensed value: 89
Root: cache ranges [75 90], [21 68], [55 80], [20 60]
No send
Send 82No send
Range: [21 68]
Sensed value: 70
Send 70
SIGMOD'06 8
Range Caching (RS)
Range: [75 90]
Sensed value: 89
Root: cache ranges [75 90], [21 68], [55 80], [20 60]
Since 90 > max{70, 82}
QueryReply 89
SIGMOD'06 9
Range Caching (RS)
Range setting If a value fall outside its current range, it
expands the range by a factor > 1.To avoid value-initiated packets, the interval should
be wide enough to make it unlikely that modifications to the exact value will exceed the interval.
When a node receives a “Query”, it contracts the range length by a factor of .to avoid “Reply” packets, the interval should be as
narrow as possible.
SIGMOD'06 10
Total cost
[Olston et al. SIGMOD 01]
the probability that a value-based transmission will occur at each round.
the probability that a query-based transmission will occur at each round.
Assume the costs of value-based andquery-based updates are equal
SIGMOD'06 11
30
{20,40}
86
{70,102}
{78,86}
{68,84}
Range Caching
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6874
72
60
82
74 76
{60,76}
{18,28}
{74,90}
{60,92}
p30
p30
p30
p30
k86
k86
k86
Q
Q
Q
Q
Low value Queries
SIGMOD'06 12
SLAT
Direct translation of adaptive caching SLAT : “single level adaptive thresholds”
Ignore topology (use it for routing only) Current max node is temporally monitored Thresholds
At node ui, vi <= t(ui) <= current_max
Node sends Trigger if value breaks thresholdIf current_max falls, root queries all nodes with threshol
d higher than current_max
SIGMOD'06 13
8683
818470
SLAT Reporting
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75t=80
t=78t=84 t=75
j84k86 l83
m80
j84,k86,l83,m81
j84,k86,l83,m81
UnbrokenThreshold,no Trigger
Broken thresholds,all nodes send Triggers
nodethresh
i j k75 80 84
Root stores thresholds
85 86
……
SIGMOD'06 14
72
SLAT Querying
20
15 23
70
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75
b
a
c
d e f g
h i jk l
m n
o pq r
nodethresh max drops: 82 72
Threshold higher than max,node must be queried
Q
Q
Q
Query
n72
n72
n72
i j k75 80 84
71 (or 72)
SIGMOD'06 15
SLAT-A
SLAT-A : “single level adaptive thresholds-aggregation” In particular round, if multiple values converge a
t an intermediate node for transmission, only the highest is forwardedSimilar to TinyAgg
SIGMOD'06 16
8683
818470
SLAT-A Reporting
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75t=80
t=78t=84 t=75
j84k86 l83
m80
k86
k86
Unbrokenthreshold
Broken thresholds
nodethresh
SIGMOD'06 17
72
SLAT-A Querying
20
15 23
70
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75
b
a
c
d e f g
h i jk l
m n
o pq r
max drops: 82 72
Root does not know any thresholds, so must query all nodes, including those with very low thresholds
Q
Q
Q
Q
Q
Q Q
t=20 t=25
t=23
t=24n72
n72
n72
SIGMOD'06 18
HAT
HAT – “hierarchical adaptive thresholds” Additional invariant: t(ui) <= t(parent(ui))
Node’s threshold greater than all values in subtree
Each node stores child thresholds Combine advantages of SLAT, SLAT-A
Reporting: only propagate highest value in subtreeQuerying: prune subtrees with threshold lower than f
allen max
SIGMOD'06 19
HAT Reporting
8683
818470
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75t=80
t=78t=84 t=75
j84k86 l83
m80t=88
t=90
t=95
threshnode d e
Unbrokenthreshold
Broken thresholds,Nodes send Triggers
f’s thresholdshort-circuits Triggers
Node stores child thresholds
SIGMOD'06 20
72
HAT Querying
20
15 23
70
18 43
32
52
62
65
70 6874
72
60
82
74 76
t=75
b
a
c
d e f g
h i jk l
m n
o pq r
max drops: 82 72
Q72
Q72
Q72
t=80
t=85
t=90
t=70
threshnode d e
70 80d pruned from queries, e not pruned
n72
n72
n72
SIGMOD'06 21
Suppression across Space/Time
Another important feature of HAT: state at intermediate nodes carries over temporally E.g. nodes a and b have common ancestors;
both rise, but in subsequent rounds;b benefits from a’s raising of thresholds
One node’s previous value can help suppressing other nodes’ subsequent values at intermediate nodes!
SIGMOD'06 22
100100
100
100
100
100
r100
r100
r100
r100
9595
More Suppression with HAT
b
a
c
d e f g
h i jk l
m n
o pq r
20
15 23
60
18 43
32
52
62
65
70 6880
72
60
82
70 76t=76
t=76
t=80
t=80
t=76
t=73
Green: r rises, value propagates to rootGold: q rises, short-circuited at n, which now has higher threshold
q95
SIGMOD'06 23
Experiments
Simulation of Mica2 motes Computation of energy cost in mJ based on
number of bytes sent+received Both actual data and transmission overhead
200 nodes, 400x400 m area
SIGMOD'06 24
Comparison of 5 algorithms Nodes change its value with some probability, and then by a random amount chosen randomly from
[v-100, v+100]
Experimental Results
SIGMOD'06 25
Experimental Results
Nodes rise with some probability HAT beats SLAT-A at low probabilities, due to sharing between rounds
SIGMOD'06 26
Experimental Results
All nodes fall in value by some percent SLAT, HAT can prune when fall is small SLAT-A must search whole network
SIGMOD'06 28
Extensions & Conclusion
Straightforward to extend max to min, top-k Two key points for continuous query, in-
network processing Leverage query semantics Leverage network hierarchy: store state in-
network, rather than treating intermediate nodes as only conduitsEnables nodes to make decisions (drop messages)Enables filtering/aggregation across time steps