collaborative processing in sensor networks lecture 2 - mobile-agent-based computing

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Collaborative Processing in Sensor Networks

Lecture 2 - Mobile-agent-based Computing

Hairong Qi, Associate ProfessorElectrical Engineering and Computer ScienceUniversity of Tennessee, Knoxvillehttp://www.eecs.utk.edu/faculty/qiEmail: [email protected]

Lecture Series at ZheJiang University, Summer 2008

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Research Focus - Recap

• Develop energy-efficient collaborative processing algorithms with fault tolerance in sensor networks

– Where to perform collaboration?– Computing paradigms

– Who should participate in the collaboration?– Reactive clustering protocols– Sensor selection protocols

– How to conduct collaboration?– In-network processing– Self deployment

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Architecture of Mobile Agent

• Itinerary– Route of migration

• Identification– Unique for each mobile agent

• Data buffer– Carries the partially integrated results

• Method– Execution code carried with the agent

160.10.30.100

itinerarydata buffer

method

identification

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Distributed Computing Paradigms

Mobile-agent-based ComputingClient/Server Computing

Transfer Unit Computing

Client/Server Computing DataCentralized, occurs at the

servers

Mobile agent Computing Mobile agentDistributed evenly among

sensor nodes

• Energy and network bandwidth requirement

• Scalability• Reliability• Progressive accuracy• Task adaptivity• Fault tolerance

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Temporal and Spatial Comparison

Data migration Mobile agent migration

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Performance Evaluation of Computing Paradigms

• Different conditions may affect the performance of computing paradigms, need to determine the affecting factors

• Need a thorough comparison of two paradigms, determine under which condition one paradigm performs better than the other

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Metrics

•Execution Time

•Energy Consumption

m: number of mobile agents n: number of nodes each agent migrates : overhead of mobile agent : overhead of data file

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Simulation Method

• Using ns-2• 4 experiments are designed• In each experiment, only one parameter is changed• Randomly deployed in a 10m by 10m area• MAC layer protocol: 802.11• Routing protocol: DSDV• Transmission power is 0.6W and receiving power is 0.3W• Default parameters:

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Experiments and Results - 1

Effect of the number of nodes (p): Number of nodes changes from 2 to 30

(A) Execution Time (B) Energy Consumption

10

0 5 10 15 20 25 30 35 40 45 5050

100

150

200

250

300

Number of mobile agents

Execution time (seconds)

client/server basedmobile-agent-based


Effect of the number of mobile agents (m): 100 nodes, number of mobile agent changes from 1 to 50

0 5 10 15 20 25 30 35 40 45 5040

45

50

55

60

65

70

Number of mobile agents

Total energy usuage(Joules)


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Effect of data size/mobile agent size : the ratio changes from 1 to 50

0 5 10 15 20 25 30 35 40 45 501

2

3

4

5

6

7

8

9

10

Size of data/Size of the mobile agent (Sf/Sa)



0 5 10 15 20 25 30 35 40 45 500.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Size of data/Size of the mobile agent (Sf/Sa)



/f as s

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Overhead ratio : changes from 0.1 to 4

0 0.5 1 1.5 2 2.5 3 3.5 43

3.5

4

4.5

5

5.5

6

6.5

7

7.5

Overhead ratio (Of/Oa)



0 0.5 1 1.5 2 2.5 3 3.5 40.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Overhead ratio (Of/Oa)



/f ao o

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Discussion

• Situations to use the mobile agents computing paradigm

– the number of nodes is large– is large – is large

• In sensor networks with large amount of sensors, mobile agent computing paradigm provides an energy efficient solution

af oo /af ss /

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Hybrid Computing Paradigms

Scheme A Scheme B

Scheme C Scheme D

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Simulation Results

• 100 nodes• Keep other default parameters• Number of clusters changes from 1 to 50

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Discussion

• Can further improve performance by dividing the sensor network into clusters and having different computing paradigms within clusters and between clusters

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Mobile Agent Planning (MAP)

• How to select a subset of sensor nodes? How to choose the order of migration?

• Mobile agent itinerary has a significant impact on– Energy consumption– Network lifetime– Fusion accuracy– Execution time

?

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Mobile Agent Planning

• Determine a mobile agent route that has low energy consumption, long network lifetime, and less execution time.

• Two branches– Static Mobile Agent Planning (SMAP): Derive an

efficient path at a central processing center before dispatching the agents. Less computation, suitable for less dynamic environment

– Dynamic Mobile Agent Planning (DMAP): Determine the route on the fly at each stop. Need more computation, suitable for dynamic environment

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Beacon Frames

• Beacons are periodically broadcasted by a sensor node to its neighbors

• Functions– Obtain location and measurement information from a neighbor node for

the target localization algorithm– Calculate cost function values to the neighbor nodes– Indicate the aliveness of the neighbor nodes

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Which Sensor to Migrate to?

• Given– A set of neighbor nodes

• Find– A sensor i whose measurement zi gives greatest

contribution to the success of the task

• Model of information gain

• A simplified model

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Dynamic Mobile Agent Planning Modeling

Need to consider Energy consumption Information gain on the neighbor nodes Remaining energy on the neighbor nodes

Define cost function

Total cost is ∑−

=+

=1

01

Hop

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CTotal

∑=

>=Hop

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Decision

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Information-driven Dynamic Mobile Agent Planning Algorithm (IDMAP)

Step 1: at t=0 Step 2: at time t Step 3: return to the processing center

Target localization

Calculate information gains

Carry=Ik

))(

1()1()(

minargmax

2max

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2

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maxe

teba

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xtxb

d

daj j

j

Nj

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−⋅−−+−

⋅+⋅=∈

Target localization


Update Carry=Carry+Ik

Carry>Desire

Go to Step 3 Migrate to neighbor node

t=t+1

))(

1()1()(

minargmax

2max

2^

2

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maxe

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d

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Dynamic Mobile Agent Planning

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Prediction of Target Movement

Mobile agent on node A, which node, B or C, to migrate?

∧∧∧

−−=+ )1()(2)1( txtxtx

Assume in very short interval, the direction and the speed of target are constant,

)1()()()1( −−=−+ txtxtxtxso that

∧

− )1(tx

Then the predicted position at 1+t

The mobile agent at time t performs target localization to estimate the target ∧

)(txlocation , it also carries the previous estimated target location .

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Predictive Information-driven Dynamic Mobile Agent Planning Algorithm (P-IDMAP)

Step 1: at t=0 Step 2: at time t Step 3: return to the processing center

Target localization


Carry=Ik

))(

1()1()(

minargmax

2max

2^

2

2

maxe

teba

dt

xtxb

d

daj j

j

Nj

kj

k

−⋅−−+−

⋅+⋅=∈

Target localization


Update Carry=Carry+Ik

Carry>Desire

Go to Step 3

Migrate to neighbor node

t=t+1

))(

1()1()1(

minargmax

2max

2^

2

2

maxe

teba

dt

xtxb

d

daj j

j

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kj

k

−⋅−−+−+

⋅+⋅=∈

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Y

Predict target location ^

)1( +tx

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Predictive Dynamic Mobile Agent Planning

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(a) Static itinerary result

(b) Dynamic itinerary result

(c) Predictive dynamic itinerary result

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Simulation and Algorithms Evaluation• Develop a sensor network simulator in JAVA• Metrics

– Energy consumption: the total energy consumes to finish a processing task

– Network lifetime: the time from node deployment to the time the first node is out of function because of energy depletion

– The number of hops: reflects the time spent for the mobile agent to finish a task

• Parameters in simulation– Network area: 20m by 20m– Number of nodes: 500– Sensing range: 10m– Beacon interval: 0.1s– Desired information gain: 18 Units– Initial energy: 36 Joule

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The Effect of the Target Speed (v)

(A) Energy Consumption

(B) Network lifetime

(C) The number of hops

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The Effect of the Number of Nodes - Target Speed at 10m/s

(A) Energy Consumption

(B) Network lifetime

(C) The number of hops

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Discussion

• Predictive Dynamic Itinerary algorithm is suitable for a wide range of target speed. It has advantages over other algorithms in terms of energy consumption, network lifetime, and the number of hops. It provides an energy efficient, near optimal, and fault tolerant itinerary solution for collaborative processing in wireless sensor networks.

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Implementation of MAF

CSIP API (C++)

SWIGShared Libraries

MA Daemon - Python

Execution code and partial result

Pickled/Unpickled


Diffusion API (C++)

Sensoria RF modem API

CSIP API (C++)


MA Daemon - Python

Execution code and partial result

Pickled/Unpickled


Diffusion API (C++)

Sensoria RF modem API

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Reference

• H. Qi, Y. Xu, P. T. Kuruganti, “Chapter 41: The mobile agent framework for collaborative processing in sensor networks,” Frontiers in Distributed Sensor Networks. Editor: R. Brooks, S. S. Iyengar, pages 783-800, CRC Press, 2004.

• Y. Xu, H. Qi, “Mobile agent migration modeling and design for target tracking in wireless sensor networks,” Ad Hoc Networks (Elsevier) Journal, 6(1):1-16, January 2008.

• Y. Xu, H. Qi, “Distributed computing paradigms for multi-sensor data fusion in sensor networks,” Journal of Parallel and Distributed Computing, 64(8):945-959, August 2004.

• Y. Xu, H. Qi, “On mobile agent itinerary for collaborative processing,” IEEE Wireless Communications and Networking Conference (WCNC), vol. 4, pages 2324-2329, Las Vegas, NV, April 3-6, 2006.

• Y. Xu, H. Qi, P. T. Kuruganti, “Mobile-agent-based computing model for collaborative processing in sensor networks,” IEEE Global Telecommunications Conference (GLOBECOM), vol. 6, pages 3531 - 3535, Los Angeles, CA, December 2003.

• Y. Xu, H. Qi, “Performance evaluation of distributed computing paradigms in mobile ad hoc sensor networks,” The 9th IEEE International Conference on Parallel and Distributed Systems (ICPADS), pages 451-456, Taiwan, Dec 2002.

collaborative processing in sensor networks lecture 2 - mobile-agent-based computing

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