SIGMOD'061 Energy-Efficient Monitoring of Extreme Values in Sensor Networks Adam Silberstein Kamesh Munagala Jun Yang Duke University

SIGMOD'062 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'063 Outline  Introduction to Max/Min Query  Algorithms  Query non-specific  Temporal Suppression (TS)  Range Caching (RS)  Query-specific  SLAT (single-level adaptive thresholds)  SLAT-A (single-level adaptive thresholds with aggregation)  HAT (hierarchical adaptive thresholds)  Experimental Results

SIGMOD'064 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'065 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' Temporal Suppression b a c d e f g hi j k l m n o p q r Low value Dropping value p30 k86 q65 k86, q65 Trigger

SIGMOD'067 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 82 No send Range: [21 68] Sensed value: 70 Send 70

SIGMOD'068 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} Query Reply 89

SIGMOD'069 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'0610 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 and query-based updates are equal

SIGMOD' {20,40} 86 {70,102} {78,86} {68,84} Range Caching b a c d e f g hi j k l m n o p q r {60,76} {18,28} {74,90} {60,92} p30 k86 Q Q Q Q Low value Queries

SIGMOD'0612 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 u i, v i <= t(u i ) <= current_max  Node sends Trigger if value breaks threshold  If current_max falls, root queries all nodes with threshold higher than current_max

SIGMOD' SLAT Reporting b a c d e f g hi j k l m n o p q r t=75 t=80 t=78 t=84 t=75 j84 k86 l83 m80 j84,k86,l83,m81 Unbroken Threshold, no Trigger Broken thresholds, all nodes send Triggers node thresh i j k Root stores thresholds 8586 … …

SIGMOD' SLAT Querying t=75 b a c d e f g hi j k l m n o p q r node thresh max drops: Threshold higher than max, node must be queried Q Q Q Query n72 i j k (or 72)

SIGMOD'0615 SLAT-A  SLAT-A : “ single level adaptive thresholds- aggregation ”  In particular round, if multiple values converge at an intermediate node for transmission, only the highest is forwarded  Similar to TinyAgg

SIGMOD' SLAT-A Reporting b a c d e f g hi j k l m n o p q r t=75 t=80 t=78 t=84 t=75 j84 k86 l83 m80 k86 Unbroken threshold Broken thresholds node thresh

SIGMOD' SLAT-A Querying t=75 b a c d e f g hi j k l m n o p q r max drops: 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=24 n72

SIGMOD'0618 HAT  HAT – “ hierarchical adaptive thresholds ”  Additional invariant: t(u i ) <= t(parent(u i ))  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 subtree  Querying: prune subtrees with threshold lower than fallen max

SIGMOD'0619 HAT Reporting b a c d e f g hi j k l m n o p q r t=75 t=80 t=78 t=84 t=75 j84 k86 l83 m80 t=88 t=90 t=95 thresh node d e Unbroken threshold Broken thresholds, Nodes send Triggers f’s threshold short-circuits Triggers Node stores child thresholds

SIGMOD' HAT Querying t=75 b a c d e f g hi j k l m n o p q r max drops: Q72 t=80 t=85 t=90 t=70 thresh node d e d pruned from queries, e not pruned n72

SIGMOD'0621 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' r More Suppression with HAT b a c d e f g hi j k l m n o p q r t=76 t=80 t=76 t=73 Green: r rises, value propagates to root Gold: q rises, short-circuited at n, which now has higher threshold q95

SIGMOD'0623 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'0624  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'0625 Experimental Results  Nodes rise with some probability  HAT beats SLAT-A at low probabilities, due to sharing between rounds

SIGMOD'0626 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'0627 Intel Lab Data (Temperature)

SIGMOD'0628 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 conduits  Enables nodes to make decisions (drop messages)  Enables filtering/aggregation across time steps