Data funneling : routing with Data funneling : routing with aggregation and compression for aggregation and compression for

wireless sensor networkswireless sensor networks

Petrovic, D.; Shah, R.C.; Ramchandran, K.; Rabaey, Petrovic, D.; Shah, R.C.; Ramchandran, K.; Rabaey, J. ;J. ;

SNPA 2003SNPA 2003

OutlineOutline

• Introduction• Data funneling • Simulation result• Coding by ordering• Conclusion

IntroductionIntroduction

• There is a multiplicity of scenarios in sensor networks– Environmental control in office building– Monitoring of seismic activity– Smart home providing security– Interactive museum

IntroductionIntroduction

• Energy consumption determines the life time of a sensor network

• Communication wirelessly consumes more power at the nodes than other activity

• We want to minimize the amount of communication required by the sensor nodes

IntroductionIntroduction

• Two methods are discussed to improve the lifetime– Packet aggregation technique– Data compression

Data funnelingData funneling

• The network environment– Sensors

• Numerous• Sense physical phenomena• Generate readings

– Controllers• Fewer in number• Observe the readings from multiple sensors

Data funnelingData funneling

• Sensors may – Report to the controller at

approximately the same time– Have similar headers

• Savings may be realized by combining different packets into one large packet with a single header

Data funnelingData funneling

• It reduces the overhead of packet headers

• Decreases the probability of packet collision – It allows the same amount of

information to be transmitted by fewer nodes

Data funnelingData funneling

Data funnelingData funneling

Data funnelingData funneling

Data funnelingData funneling

Data funnelingData funneling

• Data funneling creates clusters within the sensor network– The clusters it creates have a dynamic

hierarchy– There is not a single cluster head

• Border nodes take turns acting as cluster head

• Spreading out the responsibility and the load

Simulation resultSimulation result

• OpNet network simulator• Each sensor sends it reading to the

controller every 10 seconds• If the average number of sensor

readings per packet is 7– The energy expected on packet header

is reduced by 6/7=86%

Simulation resultSimulation result

• α is the ratio of bits in a packet header to the total number of bits in a packet

• m is the average number of sensor readings per transmitted packet

• Total energy reduced by– α*((m-1)/m)*100%

Simulation resultSimulation result

Coding by orderingCoding by ordering

• The border node receives the packets from n sensors and make up a super-packet

• Super-packet– Contain each node’s

• ID • Payload

Coding by orderingCoding by ordering

• The border node has the freedom to choose the ordering of the packets within the super-packet

• The border node is allowed to choose to suppress some of the packets– Not to include them in the super-packet

Coding by orderingCoding by ordering• For example

– Four node with ID 1,2,3,and 4– Each generates an independent reading

which is a value from the set {0,…,5}– The border node can choose

• To suppress the packet from node 4• An appropriate ordering among the 3!=6

– Possible orderings of the packets from nodes 1,2,3 indicate the value generated by node 4

Coding by orderingCoding by ordering

Coding by orderingCoding by ordering

• n : the number of packets present at the encoder

• k : the range of possible values generated by each sensor(2k)

• d : the range of node ID’s of the sensor nodes

• l : the largest number of packet that can be suppressed

Coding by ordering-Coding by ordering- achievable achievable with simple codecwith simple codec

To alleviate this problem , Stiring’s approximation is used to convert (1)

ConclusionConclusion

• This work proposes a routing algorithm-Data Funneling

• It can reduce the amount of energy spent on communication

• It also reduces the probability of packet collision