1. 1 Caching in Wireless Multimedia Sensor Networks @ Dept. of Computer & Communication Engineering, University of Thessaly @ Dept. of Informatics, Aristotle University Dimitrios Katsaros, Ph.D. Lausanne, March 17 th, 2008

2. 2 Outline of the talk A few words about my research Latest results: “ Cooperative Caching in Wireless Multimedia Sensor Networks” Appeared in MobiMedia Conf. 2007 2 nd round review @ ACM Mobile Networks & Applications PRIMITIVE: “Important” Sensor Nodes Identification PROTOCOL: Cooperative Caching GOAL: Latency Reduction & Uniform Energy Consumption

3. 3 My Research Areas (chronological info) WIRELESS NETWORKS Mobile & Pervasive Computing Data Management Caching ( ’04 ) Air-Indexing ( ’07 ) Data Dissemination Broadcast Scheduling ( ’04 ) Prediction Mobility Prediction ( ’03+’08 ) Prefetching ( ’03 ) Mobile Ad Hoc Networks Content-based Multimedia Retrieval ( ’05+’08 ) Broadcasting ( ’06+’08 ) Wireless Sensor Networks Sensor Network Clustering ( ’07 ) (Distr+Local) Data Indexing ( ’06+’08 ) Cooperative Caching ( ’07+’08 ) Data Dissemination ( ’08 ) WIRED NETWORKS Conventional and Streaming Media Distribution in the Web Replication ( ’03 ) Prefetching ( ’01+’02+03 ) Caching ( ’04 ) Overlay and P2P Networks Content Distribution Networks ( ’05+’06 ) Content Placement in CDNs ( ’07+’08 ) Indexing & Query Routing in P2P ( progress ) Distributed Structures over P2P ( progress ) Web Information Retrieval and Data Mining Web Link Mining ( ’05 ) Web Ranking ( ’07+’08 ) Rank Aggregation ( ’07+’08 ) Social Network Analysis ( ’07+’08 ) Bibliometrics (’06+’07+’08)

4. 4 Research areas: Ultimately  ??? Overlay Nets Mobile/Pervasive Computing Sensors Ad Hoc Information Retrieval Web Location Tracking Caching & Air-Indexing Content Distribution Networks Caching & Prefetching & Replication & Semistructured Data & Web views Web Ranking & Search Engines Social Network Analysis Cooperative Caching & Sensor Node Clustering & Distributed Indexing & Coverage/Connectivity & Flash storage & Content-Based MIR Broadcasting & Data Dissemination Webcasting INTELLIGENCE Sensor Web

5. 5 In the sequel … Wireless Sensor Networks Wireless Multimedia Sensor Networks Cooperative Caching Idea Relevant work Node-Importance Cooperative Caching protocol Which nodes are more important? Housekeeping information at NICoCa Cache Discovery & Cache Replacement Evaluation

6. 6 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

7. 7 Communication in WSNs Communication between two unconnected nodes is achieved through intermediate nodes Every node that falls inside the communication range r of a node u, is considered reachable (bidirectional links)

8. 8 WSNs - Applications Applications Habitat monitoring Disaster relief Target tracking Precision Agriculture

9. 9 Wireless Multimedia Sensor Nets (WMSNs) Cheap CMOS cameras: Cyclops imaging module is a light-weight imaging module which can be adapted to MICA2 or MICAz sensor nodes

10. 10 WMSNs - Applications Boost the existing application of WSNs Create new applications multimedia surveillance sensor networks : miniature video cameras that will communicate, process and store data relevant to crimes and terrorist attacks traffic avoidance and control systems : will monitor car traffic and offer routing advices to prevent congestion industrial process control : will be realized by WMSNs that will offer time-critical information related to imaging, temperature, pressure, etc

11. 11 What’s special about WMSNs ? [ Ian Akyildiz: Dec’06 & Dec’07 ] We have to rethink the computation-communication paradigm of traditional WSNs which focused only on reducing energy consumption WMSNs applications have a second goal, as important as the energy consumption delivery of application-level quality of service (QoS) mapping of this requirement to network layer metrics, like latency

12. 12 What’s special about WMSNs ? Resource constraints sensor nodes are battery-, memory- and processing- starving devices Variable channel capacity multi-hop nature of WMSNs 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

13. 13 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 Although Jim Gray predicted that flash memories will replace hard disks

14. 14 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

15. 15 Relevant work (2/2) 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

16. 16 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

17. 17 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

18. 18 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:

19. 19 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

20. 20 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

21. 21 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 …

22. 22 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

23. 23 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

24. 24 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

25. 25 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

26. 26 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 )

27. 27 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

28. 28 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

29. 29 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

30. 30 Cache vs. hits (MB files & uniform access) in a sparse WMSN (d = 4)

31. 31 Cache vs. hits (MB files & uniform access) in a dense WMSN (d = 7)

32. 32 Cache vs. hits (MB files & uniform access) in a very dense WMSN (d = 10)

33. 33 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

34. 34 Cache vs. hits (KB files & Zipfian access) in a sparse WMSN (d = 4)

35. 35 Cache vs. hits (KB files & Zipfian access) in a dense WMSN (d = 7)

36. 36 Cache vs. hits (KB files & Zipfian access) in a very dense WMSN (d = 10)

37. 37 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

38. 38 Summary Wireless Multimedia Sensor Networks (WMSNs) Features of WMSNs call for protocol designs that provide application-level QoS Cooperative caching protocol, NICoCa, suitable for WMSNs NICOCA evaluation with J-Sim and comparison to the state-of-the-art protocol NICOCA can: reduce the global hits at an average percentage of 50% increase the remote hits (due to the effective sensor cooperation) at an average percentage of 40%

39. 39 Important references 1.I. Akyildiz, T. Melodia, and K. R. Chowdhury. Wireless multimedia sensor networks: A survey. IEEE Wireless Communications magazine, 14(6), pp. 32- 39, Dec., 2007 2.Y. Diao, D. Ganesan, G. Mathur, and P. Shenoy. Rethinking data management for storage-centric sensor networks. Proceedings of the Conference on Innovative Data Systems Research (CIDR), pp. 22- 31, 2007 3.S. Nath and A. Kansal. FlashDB: Dynamic self-tuning database for NAND flash. Proceedings of the ACM International Conference on Information Processing in Sensor Networks (IPSN), pp. 410-419, 2007 4.L. Yin and G. Cao. Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1):77-89, 2006

40. 40 Thank you for your attention! Any questions?

41. 41 NI computation At a first glance, NI computation seems expensive, i.e., O( m * n 2 ) operations in total for a 2-hop neighbourhood, which consists of n nodes and m links: calculating the shortest path between a particular pair of vertices (assume for the moment that there exists only one) can be done using bfs in O( m ) time, and there exist O( n 2 ) vertex pairs Fortunately, we can do better than this by making some smart observations. The improved algorithm ( CalculateNodeImportanceIndex ) is quite complicated and beyond the scope of this presentation THEOREM. The complexity of the algorithm CalculateNodeImportanceIndex is O( n * m ) for a graph with n vertices and m edges

42. 42 Pseudocode for CalculateNodeImportanceIndex (1/2)

43. 43 Pseudocode for CalculateNodeImportanceIndex (2/2)