X1X1 X2X2 Encoding : Bits are transmitting over 2 different independent channels.  Rn bits Correlation channel  (1-R)n bits Wireless channel Code Design: Possible Design Methodologies: 1)Design an LDPC code for the equivalent channel 2)Design a non-uniform LDPC code Use ensemble of bipartite graphs, where, is the variable node degree distribution of each set and is the check node degree distribution. Simulation: 0. H(X 2 |X 1 ) ber P=0.11 R X 2 =H(p)=0.5 LDPC rate=2/3, n=1000 Extensions of Distributed Source coding of correlated sources Mina Sartipi, Nazanin Rahnavard, Faramarz Fekri Abstract Energy-Efficient Data Gathering and Broadcasting in Sensor Networks using Channel Codes Goal: Energy-efficient and reliable communication in wireless sensor networks Communication involves:  Data Gathering (Sensors to sink)  Multicasting / Broadcasting (Sink to sensors) Data Gathering:  Correlated Data  Distributed Source Coding Multicasting / Broadcasting  Redundant Transmission  Correlated Data  Rateless Code RX1RX1 RX2RX2 A B C H(X 2 |X 1 ) H(X 2 ) H(X 1 |X 2 ) H(X 1 ) + Corner Point: R X 1 = H(X 1 ) R X 2 = H(X 2 | X 1 ) Encode X 2 as follows:  X 2 is fed into a rate R systematic LDPC encoder.  P X 2, the corresponding parity bits, is sent through the wireless channel. R X 2 =1/R-1 bit per input bit. RX1RX1 H(X 1 |X 2 ) RX2RX2 H(X 2 |X 1 ) R X 1 + R X 2 H(X 1,X 2 ) Correlation Model: Distributed Source Coding on Corner Points: X 1, X 2 : I.I.D binary sequence ; Prob [ X i =0] = Prob [ X i =1]=1/2. Prob [ X 1 X 2 | X 1 ]=p BSC p Slepian-Wolf rate region for two sources : Distributed Source coding of correlated sources using LDPC Codes Motivation: Distributed Source Coding: Many sensors have highly correlated data that is slowly varying. How do we exploit correlation structure with low- power algorithms? X2X2 Encoder Decoder X1X1 Goal: Compressing X 2  With the knowledge that X 1 is present at the decoder  Without communicating with X 1 c1c1 (X 2,P X 2 ) k (1-R)n Decoder P'X2P'X2 PX2PX2 X2X2 Channel X1X1 Encoder X2X2 Correlation Channel Wireless n Systematic Channel Rate R Rn c2c2 X2X2 Non-uniform Channels Modeling Distributed Source Coding with Parallel Channels: method 2 outperforms method 1  Scaling to more than two correlated sources  DSC at arbitrary rate on Slepian-Wolf rate region  DSC with unknwon correlation parameter Future activity: Energy-efficient broadcasting  Motivation  An easy, energy-efficient, and scalable broadcasting scheme  Providing reliability with little penalty  Low complexity  Require no optimization and no topology information  Proposed Approach  Use an efficient erasure coding (rateless coding) to recover for losses  Channel parameters are different and unknown  A source can generate potentially infinite supply of encoding packets from the original data  Any receiver collects as many packets as it needs to complete the decoding  Receivers are at one hop distance from the sender  Extra cares needed for multi-hop wireless networks! BEC (  2 ) Rec 1 Rec 2 Rec i Rateless coding BEC (  1 ) BEC (  i ) 0 Future work Rateless (Fountain) Codes  Distributed source coding  Implement the algorithm on testebed to evaluate the real energy saving benefits (considering the power usage for encoding/decoding)  Study the extensions of DSC  Multicasting / Broadcasting:  Propose an energy-efficient method for broadcasting / multicasting  Apply distributed source coding to eliminate redundancy  Need route optimization while having load balancing