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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 17th, 2008

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Outline of the talk

•A few words about my research•Latest results:

“Cooperative Caching in WirelessMultimedia Sensor Networks”

•Appeared in MobiMedia Conf. 2007• 2nd round review @ ACM Mobile Networks & Applications

•PRIMITIVE: “Important” Sensor Nodes Identification•PROTOCOL: Cooperative Caching•GOAL: Latency Reduction & Uniform Energy Consumption

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Research areas: Ultimately ???

Overlay NetsMobile/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

Web

cast

ing

INTELLIG

ENCE

Sensor

Web

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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

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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

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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)

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WSNs - Applications

• Applications• Habitat monitoring• Disaster relief• Target tracking• Precision Agriculture

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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

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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

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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

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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

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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

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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

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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

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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

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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

• N1(v) : the set of one hop neighbours of v

• N2(v) : the set of two hop neighbours of v

• N12(v) : combined set of N1(v) and N2(v)

• LNv : is the induced subgraph of G associated with vertices in N12(v)

• dG(v,u) : distance between v and u

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A measure of sensor importance

• σuw= σwu : number of shortest paths from

u V to w V (σuu=0)

• σuw(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:

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The NI index in sample graphs13

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

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The NI index in sample graphs13 (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

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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 …

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The NI index in a localized algorithm

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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

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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

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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

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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

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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 )

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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

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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

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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

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

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

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

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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

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

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

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

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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

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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%

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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

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Thank you for your

attention!

Any questions?