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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 17th, 2008

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Outline of the talk

• WSNs – A working reality• What is the “Sensory Web”?• Data Coverage issues in WSNs• Cooperative Caching for WSNs• Concluding remarks

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Outline of the talk

• WSNs – A working reality• What is the “Sensory Web”?• Data Coverage issues in WSNs• Cooperative Caching for WSNs• Concluding remarks

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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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WSNs - Applications

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More exotic applications of WSNs

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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

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Challenges …

• Huge network size• Unknown/variable network topology• Agnostic users• Fault tolerance• Sensor readings are simply votes

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Outline of the talk

• WSNs – A working reality• What is the “Sensory Web”?• Data Coverage issues in WSNs• Cooperative Caching for WSNs• Concluding remarks

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Research areas: Ultimately ???

Overlay NetsMobile/Pervasive

Computing

Wireless Sensors

Networks

Mobile Ad Hoc

Information Retrieval

WebIN

-NETW

ORK

INTELLIG

ENCE

Sensory

Web

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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

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Outline of the talk

• WSNs – A working reality• What is the “Sensory Web”?• Data Coverage issues in WSNs• Cooperative Caching for WSNs• Concluding remarks

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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”

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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?

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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?

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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

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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 2nd max valueSensor with 3rd

max value

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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)

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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

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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

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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

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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

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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

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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

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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

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DaCoN evaluation

• No competing methods• Network topologies,

• existence and “strength” of clusters of sensors

• density of sensor nodes, etc• Sensor data generator

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Impact of sensornet size: messages

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Impact of sensornet size: activated sens

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Impact of assortativity: messages

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Impact of assortativity: activated sens

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Impact of k (500 sensors): activated sens

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Impact of k (1000 sensors): activated sens

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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

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Outline of the talk

• WSNs – A working reality• What is the “Sensory Web”?• Data Coverage issues in WSNs• Cooperative Caching for WSNs• Concluding remarks

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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

• Jim Gray predicted that flash memories will replace hard disks

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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

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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 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

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Thank you for your

attention!

Any questions?