1. 1 Searching the Physical World: Distributed Protocols for Data Coverage and Caching in WSNs @ Dept. of Computer & Communication Engineering, University of Thessaly Dimitrios Katsaros, Ph.D. Nicosia, June 17 th, 2008

2. 2 Outline of the talk WSNs – A working reality What is the “Sensory Web”? Data Coverage issues in WSNs Cooperative Caching for WSNs Concluding remarks

3. 3 Outline of the talk WSNs – A working reality What is the “Sensory Web”? Data Coverage issues in WSNs Cooperative Caching for WSNs Concluding remarks

4. 4 Wireless Sensor Networks (WSNs) Wireless Sensor Networks features Homogeneous devices Stationary nodes Dispersed network Large network size Self-organized All nodes acts as routers No wired infrastructure Potential multihop routes

5. 5 WSNs - Applications

6. 6 More exotic applications of WSNs

7. 7 What’s special about WSNs ? Resource constraints sensor nodes are battery-, memory- and processing- starving devices Variable channel capacity multi-hop nature of WSNs implies that wireless link capacity depends on the interference level among nodes Multimedia in-network processing sensor nodes store rich media (image, video), and must retrieve such media from remote sensor nodes with short latency

8. 8 Challenges … Huge network size Unknown/variable network topology Agnostic users Fault tolerance Sensor readings are simply votes

9. 9 Outline of the talk WSNs – A working reality What is the “Sensory Web”? Data Coverage issues in WSNs Cooperative Caching for WSNs Concluding remarks

10. 10 Research areas: Ultimately  ??? Overlay Nets Mobile/Pervasive Computing Wireless Sensors Networks Mobile Ad Hoc Information Retrieval Web IN-NETWORK INTELLIGENCE Sensory Web

11. 11 Search Engines for the Physical World Cooperating Sensors Distributed Protocols Energy-efficient Communication Short-latency Data Retrieval Unknown Network Topology Topology Control Storage in Flash Devices

12. 12 Outline of the talk WSNs – A working reality What is the “Sensory Web”? Data Coverage issues in WSNs Cooperative Caching for WSNs Concluding remarks

13. 13 Querying WSNs … Simple queries, e.g., “Report the value of the humidity” Aggregate queries, e.g., “Report the average humidity of all sensors in region X” Approximate queries, requiring data summarization to perform holistic data aggregation in the form of histograms, contour maps, e.g., “Report the contour of toxic chemical gas in region X” Complex queries, which, if expressed in SQL, would involve joins nested or conditioned-based sub-queries, e.g., “Among regions X and Y, report the average humidity of the region with the highest temperature” Advanced queries, such as top-k queries, e.g., “Report the k data objects with the highest temperature”

14. 14 Qyerying limitations (1/2)… Report the k smallest values of humidity within region X along with the sensors that sensed them What about sensor failures?

15. 15 Qyerying limitations (2/2)… Report the k smallest values of humidity across the whole sensornet along with the sensors that sensed them What about small shifts in the region boundaries?

16. 16 The concept of Data Coverage … Report the sensor(s) whose humidity value is not covered by any other humidity value across the whole sensornet Sensor with max humidity value

17. 17 The concept of k-Data Coverage … Report the sensor(s) whose humidity value is covered by at most k (e.g., k=2) other humidity values across the whole sensornet Sensor with max value Sensor with 2 nd max value Sensor with 3 rd max value

18. 18 Feature Distribution Maps Still, we can not find out what happens in neighborhoods, i.e., local minima, local maxima, etc. These are not network-wide (global)

19. 19 The concept of d-hop k-Data Coverage … Depict the points (i.e., sensors) with the largest, relative to their neighboring sensors, humidities localized definition of neighborhoods no region prespecification define d to be the sensornet diameter Network-wide k- coverage

20. 20 The d-hop k-Data Coverage problem Generalizes The k-skyband query The top-k query The d-hop dominating set formation problem Deals with Any number of readings by a sensor node Any number of measured quantities, e.g., humidity, temperature, etc. More generic predicates, not only maximum, minimum

21. 21 Data Coverage in Neighborhoods-DaCoN Distributed protocol for processing d-hop k- data coverage queries in WSNs Runs localized in neighborhoods No network spanners, e.g., aggregation tree, spanning tree No demanding initialization phase to construct the spanner Uniform energy consumption, no hot spots of communication Runs in 3 phases

22. 22 DaCoN’s execution In a 2-dimensional space, assume that we wish the maximization of the first dimension and the minimization of the second one v_i.d_x denotes the x-th dimension of value v_i v_i covers a value v_j, if it holds v_i.d_1 > v_j.d_1 and v_i.d_2 < v_j.d_2

23. 23 PHASE 1. First d-rounds Each sensor sends its k-th larger values to all its 1-hop neighbors It finds the k-th larger values taking account its own values and the values that has received from its neighbors It forms a message with these values and it stores the message into a buffer frb In the next d-1 rounds, the above procedure is repeated with the difference that now each sensor considers as its k-th larger values, the values of the last message of the frb

24. 24 PHASE 2. Next d-rounds Similarly to the previous rounds, but … Each sensor finds its k-th values by taking into account the previous message and the messages that has received from its neighbors as follows: each v_i value (1 ≤ i ≤ k) is selected by keeping the smaller i-th value of these messages These values form a message that is stored into a buffer srb

25. 25 PHASE 3. Answer of query Each value v_i (1 ≤ i ≤ k) of the answer is selected as follows: the sensor compares the messages of frb and srb and tries to find pairs of values in the first i-th values of each message After the identification of all pairs of values, the sensor selects the minimum pair as the i-th value of its answer If a pair of values does not exist, the sensor selects the maximum of the first i-th values of the messages of frb

26. 26

27. 27 DaCoN evaluation No competing methods Network topologies, existence and “strength” of clusters of sensors density of sensor nodes, etc Sensor data generator

28. 28 Impact of sensornet size: messages

29. 29 Impact of sensornet size: activated sens

30. 30 Impact of assortativity: messages

31. 31 Impact of assortativity: activated sens

32. 32 Impact of k (500 sensors): activated sens

33. 33 Impact of k (1000 sensors): activated sens

34. 34 d-hop k-data coverage Feature Distribution Maps Fully distributed solution: DaCoN Little overhead Little storage Light computational load Few messages & no hotspots in communication How do we improve upon the latency, when the sensors need data from other sensors? Cooperative Caching

35. 35 Outline of the talk WSNs – A working reality What is the “Sensory Web”? Data Coverage issues in WSNs Cooperative Caching for WSNs Concluding remarks

36. 36 Our proposal … Cooperative Caching: NICOCA protocol multiple sensor nodes share and coordinate cache data to cut communication cost and exploit the aggregate cache space of cooperating sensors Each sensor node has a moderate local storage capacity associated with it, i.e., a flash memory Jim Gray predicted that flash memories will replace hard disks

37. 37 Relevant work (1/2) Caching in OSs, DBMS, Web No extreme resource constraints Caching for wireless broadcast cellular networks more powerful nodes, one-hop communication with resource-rich base stations Most relevant research works: cooperative caching protocols for MANETs GroCoca : organize nodes into groups based on data request pattern & mobility pattern) ECOR, Zone Co-operative, Cluster Cooperative : form clusters of nodes based geographical proximity or adopting node clustering algorithms for MANETs

38. 38 Relevant work Protocols that deviated from such approaches: CacheData : intermediate nodes cache the data to serve future requests instead of fetching data from their source CachePath : mobile nodes cache the data path and use it to redirect future requests to the nearby node which has the data instead of the faraway origin node Amalgamation of them: the champion HybridCache cooperative caching for MANETs

39. 39 NICoCa consists of … A metric for estimating the importance of a sensor node, which will imply short latency in retrieval A cooperative caching protocol which strives to achieve uniform energy consumption Datum discovery and cache replacement component subprotocols Performance evaluation of the protocol and comparison with the state-of-the-art cooperative caching for MANETs, with J-Sim

40. 40 Terminology and assumptions WMSN is abstracted as a graph G(V,E) edge e=(u,v) exists iff u is in the transmission range of v and vice versa (bidirectional links) The network is assumed to be connected N 1 (v) : the set of one hop neighbours of v N 2 (v) : the set of two hop neighbours of v N 12 (v) : combined set of N 1 (v) and N 2 (v) LN v : is the induced subgraph of G associated with vertices in N 12 (v) d G (v,u) : distance between v and u

41. 41 A measure of sensor importance σ uw = σ wu : number of shortest paths from u  V to w  V ( σ uu = 0) σ uw (v) : number of shortest paths from u to w that some vertex v  V lies on Node importance index NI(v) of a vertex v is:

42. 42 The NI index in sample graphs 13 15 20 19 17 1 2 3 6 5 4 7 14 12 8 18 16 11 10 9 W R U P A C X Y T V Q B

43. 43 The NI index in sample graphs 13 (0) 15 (0) 20 (0) 19 (0) 17 (1) 1 (0) 2 (0) 3 (68) 6 (0) 5 (0) 4 (96) 7 (156) 14 (233) 12 (0) 8 (26) 18 (97) 16 (131) 11 (0) 10 (0) 9 (0) W (3,33) R (9,33) U (54) P (41) A (6,67) C (0) X (0) Y (0) T (1,33) V (1,33) Q (8) B (13) Nodes with large NI:  Articulation nodes (in bridges), e.g., 3, 4, 7, 16, 18  With large fanout, e.g., 14, 8, U

44. 44 Centralized solution ??? Create a broadcast tree to coordinate the identification of NI’s lot of messages larger latency Hot-spots in communication (nodes with large NI) Localized Algorithms are preferable NI’s in neighborhoods …

45. 45 The NI index in a localized algorithm 13 15 20 19 17 1 2 3 6 5 4 7 14 12 8 18 16 11 10 9 2-hop neighbors of node 8 node 8 calculates the NI of its 2-hop neighbors

46. 46 The NI index in a localized algorithm 13 (0) 15 (0) 20 19 17 1 2 3 6 5 4 7 (0) 14 (65) 12 (0) 8 (14) 18 (0) 16 (23) 11 (0) 10 (0) 9 (0) nodes 14 and 16 are more important than the others from the viewpoint of node 8 Each node can identify its own “important” nodes

47. 47 Housekeeping information in NICoCa Ultimate source of multimedia data: Data Center Each node is aware of its 2-hop neighborhood Uses NI to characterize some neighbors as mediators Can be either a mediator or an ordinary node Each sensor node stores the dataID, and the actual datum the data size, TTL interval for each cached item characterized either as O (i.e., own) or H (i.e., hosted) the timestamps of the K most recent accesses

48. 48 The cache discovery protocol (1/2) A sensor node issues a request for a multimedia item Searches its local cache and if it is found ( local cache hit ) then the K most recent access timestamps are updated Otherwise ( local cache miss ), the request is broadcasted and received by the mediators These check the 2-hop neighbors of the requesting node whether they cache the datum ( proximity hit ) If none of them responds ( proximity cache miss ), then the request is directed to the Data Center

49. 49 The cache discovery protocol (2/2) When a mediator receives a request, searches its cache If it deduces that the request can be satisfied by a neighboring node ( remote cache hit ), forwards the request to the neighboring node with the largest residual energy If the request can not be satisfied by this mediator node, then it does not forward it recursively to its own mediators, since this will be done by the routing protocol, e.g., AODV If none of the nodes can help, then requested datum is served by the Data Center ( global hit )

50. 50 The cache replacement protocol Each sensor node first purges the data that it has cached on behalf of some other node Calculate the following function for each cached datum i The candidate cache victim is the item which incurs the largest cost Inform the mediators about the candidate victim If it is cached by a mediator, the metadata are updated If not, it is forwarded and cached to the node with the largest residual energy

51. 51 Evaluation setting (1/2) We compared NICOCA to: Hybrid, state-of-the-art cooperative caching protocol for MANETs Implementation of protocols using J-Sim simulation library

52. 52 Evaluation setting (2/2) Measured quantities number of hits (local, remote and global) residual energy level of the sensor nodes average latency for getting the requested data the number of packets dropped Present here only results for number of hits representative of: latency, collisions and energy consumption A small number of global hits less network congestion, fewer collisions and packet drops. Large number of remote hits  effectiveness of cooperation Large number of local hits ≠ effective cooperation the cost of global hits vanishes the benefits of local hits

53. 53 Cache vs. hits (MB files & uniform access) in a sparse WMSN (d = 4)

54. 54 Cache vs. hits (MB files & uniform access) in a dense WMSN (d = 7)

55. 55 Cache vs. hits (MB files & uniform access) in a very dense WMSN (d = 10)

56. 56 Observe: MB files & uniform access For all network topologies (sparse, dense and very dense), NICoCa achieves more remote hits and less global hits than HybridCache This performance gap widens in favor of NICoCa as we move from sparse to denser WMSNs For very dense sensor deployments, NICoCa achieves double the remote hits of HybridCache and only half of its global hits For sparse WMSNs HybridCache achieves slightly more local hits than does NICoCa, but this gap vanishes completely when moving to denser network This small gain of HybridCache for sparse topologies is not advantageous at all, since it incurs global hits as many as twice the number of its local hits

57. 57 Cache vs. hits (KB files & Zipfian access) in a sparse WMSN (d = 4)

58. 58 Cache vs. hits (KB files & Zipfian access) in a dense WMSN (d = 7)

59. 59 Cache vs. hits (KB files & Zipfian access) in a very dense WMSN (d = 10)

60. 60 Observe: KB files & Zipfian access For all network topologies (sparse, dense and very dense), NICoCa achieves more remote hits and less global hits than HybridCache For very dense WMSNs, the requests reaching Data Center for NICoCa are less than half those of HybridCache! NICoCa's global hits do not vary significantly with varying network topologies and varying local sensor storage Global hits of HybridCache are severely affected by the topology and the cache size For cache equal to 1% of the total data, HybridCache's global hits increase at a pace of 50%! The results for Zipfian access on megabyte-sized data more impressively in favor of NICoCa

61. 61 Summary Wireless Sensor Networks (WSNs) Cooperation among sensors Distributed protocols A brand new world or Distributed Algorithms reloaded? Exploit the unknown network topology! Impresice/incomplete queries! New storage devices (flash) Minimize energy consumption Minimize latency

62. 62 Thank you for your attention! Any questions?

63. 63 Important references 1.N. Dimokas, D. Katsaros, Y. Manolopoulos. Cooperative caching in wireless multimedia sensor networks. ACM Mobile Networks and Applications, accepted, May 2008 2.M. Kontaki, D. Katsaros, Y. Manolopoulos. The d-hop k- data coverage query problem in wireless sensor networks. Submitted, June 2008 3.D. Katsaros, Y. Manolopoulos. Prediction in wireless networks by Markov chains. IEEE Wireless Communications magazine, (under second round review), April 2008 4.L. Yin and G. Cao. Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1):77-89, 2006