Data funneling : routing with aggregation and compression for wireless sensor networks

Data funneling : routing with aggregation and compression for wireless sensor networks Petrovic, D.; Shah, R.C.; Ramchandran, K.; Rabaey, J. ; SNPA 2003

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

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

Introduction • 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

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

Data funneling • The network environment • Sensors • Numerous • Sense physical phenomena • Generate readings • Controllers • Fewer in number • Observe the readings from multiple sensors

Data 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 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 funneling

Data 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 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 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 result

Coding 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 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 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 ordering

Coding 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-achievable with simple codec To alleviate this problem , Stiring’s approximation is used to convert (1)

Conclusion • 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

Data funneling : routing with aggregation and compression for wireless sensor networks