1. Collecting Correlated Information from a Sensor Network Micah Adler University of Massachusetts, Amherst

2. Fundamental Problem Collecting information from distributed sources. –Objective: correlations reduce bits that must be sent. Correlation examples in sensor networks: –Weather in geographic region. –Similar views of same image. Our focus: information theory –Number of bits sent. –Ignore network topology.

3. Modeling Correlation k sensor nodes each have n-bit string. Input drawn from distribution D. –Sample specifies all kn bits. –Captures correlations and a priori knowledge. Objective: –Inform server of all k strings. –Ideally: nodes send H(D) bits. –H(D): Binary entropy of D. x1x1 x2x2 x3x3 x4x4 x5x5 x6x6 xkxk

4. Binary entropy of D Optimal code to describe sample from D: –Expected number of bits required ≈ H(D). Ranges from 0 to kn. –E asy if entire sample known by single node. Idea: shorter codewords for more likely samples. Challenge of our problem: input distributed.

5. Distributed Source Coding [Slepian-Wolf, 1973]: –Simultaneously encode r independent samples. –As r  , Bits sent by nodes  rH(D). Probability of error  0. Drawback: relies on r   –Recent research: try to remove this.

6. Recent Research on DSC Survey [XLC 2004]: over 50 recent DSC papers –All previous techniques: Significant restrictions on D and/or Large values of r required. –Can also be viewed as restriction on D. –Generalizing D most important open problem. Impossibility result: –There are D such that no encoding for small r achieves O(H(D)+k) bits sent from nodes. Our result: general D, r=1, O(H(D)+k) bits.

7. New approach Allow interactive communication! –Nodes receive “feedback” from server. Also utilized for DSC in [CPR 2003]. –Server at least as powerful as nodes. Power utilization: –Central issue for sensor networks. –Node sending: power intensive. –Node receiving requires less power. Analogy: portable radio vs. cellphone. x1x1 x2x2 x4x4 x5x5 xkxk x3x3

8. New approach Communication model: –Synchronous rounds: Nodes send bits to server. Server sends bits back to nodes. Nothing directly between nodes. Objectives: –Minimize bits sent by nodes. Ideally O(H(D)+k). –Minimize bits sent by server. –Minimize rounds of communication. x1x1 x2x2 x4x4 x5x5 xkxk x3x3

9. Asymmetric Commmunication Channels Adler-Maggs 1998: k=1 case. Subsequent work: [HL2002] [GGS2001] [W2000] [WAF2001] Other applications: –Circumventing web censorship [FBHBK2002] –Design of websites [BKLM2002] Sensor networks problem: natural parallelization. x D

10. Who knows what? Nodes: only know own string. –Can also assume they know distribution D. Server: knows distribution D. –Typical in work on DSC. –Some applications: D must be learned by server Most such cases: D varies with time. Crucial to have r as small as possible. D X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8, D

11. New Contributions New technique to communicate interactively: –O(H(D)+k) node bits. –O(kn + H(D) log n) server bits. –O(log min(k, H(D))) rounds. Lower bound: –kn bits must be exchanged if no error. If server is allowed error with probability ∆: –O(H(D)+k) node bits. –O(k log (kn/∆) + H(D) log n) server bits. –O(log min(k, H(D))) rounds.

12. General strategy Support uniform case: –D is uniform over set of possible inputs. General distributions: –Technique from [Adler-Maggs 1998] “Reduce” to support uniform case. –Requires modification to support uniform protocol. Allowing for Error: –Same protocol with some enhancements.

13. Support Uniform Input D: k-dimensional binary matrix –side length 2 n Choose X: uniform 1 entry of matrix. Server is given matrix, wants to know X. Node i given ith coordinate of X. H(D) = log M –M: number of possible inputs.

14. Basic Building Block Standard fingerprinting techniques: –Class of hash functions f: n-bit string  1 bit. For randomly chosen f, –If x  y, then Pr[ f(x) = f(y)] ≈ 1/2 Description of f requires O(log n) bits.

15. Not possible inputs Possible inputs Node 1 bits: Protocol for k=1 Server sends node log M fingerprint fcts. Node sends back resulting fingerprint bits. Intuition: each bit removes half inputs left.

16. What about k=2? Node 1 bits: Node 2 bits:

17. What about k=2? Node 1 bits: Node 2 bits:

18. First step: allow many rounds. Each round: –Server chooses one node. –That node sends single fingerprint bit. Objectives: –Ideal: each bit removes half remaining inputs. –Our goal: expected inputs removed is constant fraction. –Possibility: no node can send such a bit. Need to distinguish “useful” bits from “not useful” bits.

19. Balanced Bits Fingerprint bit sent by node i is balanced: –No value for i has > 1/2 possible inputs, –given all information considered so far. Balanced bits, in expectation, eliminate constant fraction of inputs. - Protocol goal: –Collect O(log M) balanced bits. –Don’t collect  (log M) unbalanced bits. Balanced bit:Unbalanced bit:

20. Objective: minimize rounds. Must send multiple bits from multiple nodes. –But shouldn’t send too many unbalanced bits! Difficulty: –Must decide how many at start of round. –As bits processed, matrix evolves. Node may only send unbalanced bits.

21. Number of bits sent per round Defined node: only one possible value left. –Should no longer send bits. First try: –Round i: k i undefined nodes, each send bits Possible problem: –Most nodes become defined at start of round i –Nodes might send total bits.

22. Protocol Description Phases: first round and second round. –First of phase i: undefined nodes send  b i  bits. Server processes bits in any order. Counts number of balanced bits. –Second: if node had any unbalanced bits Query if it has first heavy string. Continue until  (log M) balanced bits –Or until know entire input. –Send nodes remaining possibilities.

23. Performance of protocol Theorem: –Expected bits sent by nodes: O(k + log M) –Expected bits sent by server: O(kn + log M log n) –Expected number of rounds: O(min(log log M, log k))

24. Proof: O(log M + k) node bits. Key: if node i sends unbalanced bit in phase –Pr[i defined by end of phase]  1/2 Expected answers to heavy queries: O(1). Accounting for unbalanced bits: –Charge to bit sent by same node: In most recent balanced round –If none, then first round. Spread bits charged to round evenly.

25. Proof: O(log M + k) node bits. Expected unbalanced bits charged to any bit: Total balanced bits: O(log M) Total first round bits:

26. Proof: server bits and rounds Server bits: after O(log M) balanced bits –Pr[incorrect input not eliminated] = 1/M –Expected possible inputs remaining: O(1) Rounds: –Sparse phase: < 2/3 log M fingerprint bits –O(1) non-sparse phases. –O(min(log k, log log M)) sparse phases.

27. Conclusions New technique for collecting distributed and correlated information. Allows for arbitrary distributions and correlations. Single sample sufficient. Open problems: –Lower bound on rounds. –Achieving full Slepian-Wolf rate region. –Incorporating network topology.