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This paper explores data funneling as a means to improve routing efficiency in wireless sensor networks, emphasizing energy conservation and reduced packet collision. Sensor networks face challenges such as high energy consumption due to wireless communication. The approach discussed involves packet aggregation and data compression to minimize communication needs. Utilizing simulations, the results show significant energy savings through effective clustering and the dynamic hierarchy of nodes. By employing coding by ordering techniques, the network can suppress unnecessary packets while still transmitting essential readings, thus enhancing overall network life.
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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 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%
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 • 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
