Collecting Correlated Information from Wireless Sensor Networks

1. Collecting Correlated Information from Wireless Sensor Networks Presented by Bo Han Joint work with Aravind Srinivasan and Amol Deshpande

2. Outline • Introduction of wireless sensor networks • Communication architecture • Design factors of sensor networks • Applications of sensor networks • Correlated information collection • What we have done so far • Conclusion

3. Introduction • A sensor network is composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it. • Random deployment, the position of sensor nodes need not be engineered or predetermined. • Self-organizing capabilities. • Cooperative effort of sensor nodes.

4. Sensor Networks vs Ad-Hoc Networks • The number of nodes in a sensor network can be several orders of magnitude higher than the nodes in an Ad-Hoc network. • Sensor nodes are densely deployed. • Sensor nodes are prone to failures. • The topology of a sensor network changes very frequently.

5. Sensor Networks vs Ad-Hoc Networks • Sensor nodes mainly use broadcast, most ad hoc networks are based on point-to-point communication. • Sensor nodes are limited in power, computational capacities and memory. • Sensor nodes may not have global ID.

6. Communication Architecture • The sensor nodes are usually scattered in a sensor field. • Each of these scattered sensor nodes has the capabilities to collect data and route data back to the sink. • Data are routed back to the sink by a multi-hop infrastructureless architecture. • The sink may communicate with the task manager node via Internet or satellite.

7. Example of Sensor Networks

8. Data Delivery Models • Continuous: sensors communicate their data continuously at a prespecified rate. • Event driven: sensors report information only when the event of interest occurs. • Observer initiated (request-reply): sensors only reports their results in response to an explicit request from the observer. • Hybrid: all three approaches coexist.

9. Protocol Stack

10. Five Layers • The physical layer addresses the needs of simple but robust modulation, transmission, and receiving techniques. • The MAC protocol must be power-aware and able to minimize collision with neighbors’ broadcasts. • The network layer takes care of routing the data supplied by the transport layer. • The transport layer helps to maintain the flow of data if the sensor networks application requires it. • Different types of application software can be built and used on the application layer.

11. Three Plans • The power management plane manages how a sensor node uses its power. • The mobility management plane detects and registers the movement of sensor nodes, so a route back to the user is always maintained, and the sensor nodes can keep track of who their neighbor sensor nodes are. • The task management plane balances and schedules the sensing tasks given to a specific region.

12. Design Factors • Fault tolerance • Scalability • Production costs • Hardware constraints • Transmission media • Power consumption • Sensor network topology • Environment

13. Fault Tolerance • Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures. • The fault tolerance level depends on the application of the sensor networks.

14. Scalability • Depending on the application, the number may reach an extreme value of millions. New schemes must be able to work with this number of nodes. • Basically, the density gives the number of nodes within the transmission radius of each node in a region. • Must also utilize the high density of the sensor networks.

15. Production Costs • The cost of a single node is very important to justify the overall cost of the networks, since sensor networks consist of large number of sensor nodes. • The cost of a sensor node should be less than a dollar.

16. Transmission Media • In a multihop sensor network, communicating nodes are linked by a wireless medium. • To enable global operation, the chosen transmission medium must be available worldwide. • Radio, infrared and optical media.

17. Power Consumption • Limited power source. • Battery lifetime is limited. • Each sensor node plays a dual role of data originator and data router (data processor). • The malfunctioning of a few nodes consumes lot of energy (rerouting of packets and significant topological changes).

18. Sensor Network Topology • Pre-deployment and deployment phase, either thrown in as a mass or placed one by one. • Post-deployment phase, topology changes are due to change in sensor nodes’ position, reachability, available energy, malfunctioning, and task details. • Re-deployment of additional nodes phase, additional sensor nodes can be redeployed at any time to replace malfunctioning nodes or due to changes in task dynamics.

19. Environment • Sensor nodes are densely deployed either very close or directly inside the phenomenon to be observed. • They usually work unattended in remote geographic areas. • They may be working in the interior of large machinery, at the bottom of an ocean, in a biologically or chemically contaminated field, in a battlefield beyond the enemy lines, and in a home or large building.

20. Applications of Sensor Networks • Military: Battlefield surveillance, Nuclear, biological and chemical attack detection and reconnaissance. • Environment: Forest fire detection, Flood detection. • Health: Telemonitoring of human physiological data, tracking and monitoring patients and doctors inside a hospital. • Home application: Home automation and smart environment.

21. Sensor Devices and Applications • Berkeley Motes • iBadge - UCLA • MIT d'Arbeloff Lab – The ring sensor • Nose-on-a-chip • Zilog’s eZ80 • iButton

22. Berkeley Motes • Small (under 1” square) microcontroller. • It consists of: • Microprocessor • A set of sensors for temperature, light, acceleration and motion • A low power radio for communicating with other motes • C compiler inclusion.

23. Berkeley Motes

24. iBadge - UCLA • Investigate behavior of children/patient. • Features: • Speech recording / replaying • Position detection • Direction detection / estimation (compass) • Weather data: temperature, humidity, pressure and light

25. iBadge - UCLA

26. MIT d'Arbeloff Lab – The Ring Sensor • An ambulatory, telemetric, continuous health monitoring device developed by d'Arbeloff Laboratory for Information Systems and Technology at MIT. • Monitor the physiological status of the wearer and transmit the information to the medical professional over the Internet. • Clinical trials have been done in conjunction with Massachusetts General Hospital's Emergency Room, and researchers are now working on commercialization of the ring-sized device.

27. Nose-on-a-chip • Nose-on-a-chip is a MEMS-based sensor, developed at Oak Ridge National Laboratory. • Can detect 400 species of gases and transmit a signal indicating the level to a central control station. • Consists of an array of tiny sensors on one integrated circuit and electronics on another. • The chip can be customized to detect virtually any chemical or biological species.

28. Zilog’s eZ80 • Provides a way to internet-enabled process control and monitoring applications. • Temperature sensor, water leak detector and many more applications. • Enables users to access Webserver data and files from anywhere in the world.

29. iButton • A 16mm computer chip armored in a stainless steel can. • Up-to-date information can travel with a person or object.

30. Correlated Information Gathering • Problem Definition • Correlation Modeling • Distributed Source Coding • Asymmetric Communication Channels • Our Approach

31. 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. • Focus: information theory • Number of bits sent. • Ignore network topology. Server

32. Modeling Correlation x1 • 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. x2 x3 x4 Server x5 x6 xk

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

34. (Distributed) Source Coding • Source coding is the process of encoding information using fewer bits than an unencoded representation, also called data compression. • DSC refers to the compression of the outputs of two or more physically separated sources. The sources do not communicate with each other. These sources send their compressed outputs to a central point for joint decoding. DSC is part of network information theory. • DSC has recently become a very active research area – more than 30 years after Slepian and Wolf laid its theoretical foundation.

35. 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. Server

36. Slepian-Wolf Theorem

37. Self-Coding & Foreign Coding

38. Network Coding • Network coding is a field of information theory and coding theory. • A method of attaining maximum information flow in a network. • The core notion of network coding is to allow mixing of data at intermediate network nodes. • A receiver sees these data packets and deduces from them the messages that were originally intended for that data sink.

39. Butterfly Network

40. New approach x1 • Allow interactive communication! • Nodes receive “feedback” from server. • Server at least as powerful as nodes. • Power utilization: • Central issue for sensor networks. • Node sending is power intensive. • Node receiving requires less power. . x2 x3 x4 Server x5 xk

41. New approach x1 • Communication model: • Synchronous rounds: • Nodes send bits to server. • Server sends bits back to nodes. • Nothing directly between nodes. • Asymmetric Communication Channels • Objectives: • Minimize bits sent by nodes. • Ideally O(H(D)+k). • Minimize bits sent by server. • Minimize rounds of communication. x2 x3 x4 Server x5 xk

42. X1 X2 X3 X4 X5 X6 X7 X8 , D , D , D , D , D , D , D , D Who knows what? Server D • 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.

43. Fingerprint Function • A class of functions that map an n-bit string to a single bit such that if f is chosen uniformly at random from this class, then for any y1≠y2 Pr[f(y1) = f(y2)] ≤ p, for some p › ½ • An n3 x n matrix with each item chosen uniformly at random from {0, 1}.

44. Correlation • Correlation: Multivariate Normal Distribution. • Put sensor nodes uniformly at random in a unit square. • Covariance matrix. • Diagonal items are all 1 • Cov(x, y) = d(x, y) r • Discretization of this continuous distribution. • Joint entropy estimation H(D).

45. Sample Set Generation • Determine the size of sample set. • Compute the Cholesky decomposition (matrix square root) of Σ, that is, find the unique lower triangular matrix A such that AAT = Σ. • Let Z = (z1, …, zn)T be a vector whose components are n independent standard normal variates (which can be generated, for example, by using the Box-Muller transform). • Let X be μ+ AZ (here, μ= 0).

46. The Algorithm • The algorithm can figure out the sensor node string with high probability in log2(H(D)) phases. • Each phase has two rounds. • First round: sensor nodes send fingerprint bits to the server. • Second round: server sends feedback to the sensor nodes.

47. Phase i – First Round • Node sends some number of fingerprint bits to the server, each specified by a fingerprint function chosen randomly. • When each fingerprint bit is processed, the inputs in the sample set that do not agree with that bit are discarded from consideration. • Before processing each fingerprint bit sent by the node, the server checks to see if it is an unbalanced bit: if some string x agrees with more than half of the elements of the sample set. • The string is called heavy string.

48. Phase i – Second Round • The server sends the heavy string to the node. • Node responses with a single indicator bit. • The server keeps track of the total number of balanced bits that it has received. • The server continues this process until it has received some predetermined number of balanced bits or the sample set is empty.

49. Conclusions • Lots of interesting problems in wireless sensor networks. • New technique for collecting distributed and correlated information (ongoing project). • Allows for arbitrary distributions and correlations. • Single sample sufficient. • Open problems: • Lower bound on rounds. • Incorporating network topology.

50. Reference • Top conference: MobiCom, MobiHoc, Sensys, IPSN, Infocom, SECON, SODA, STOC, FOCS. • I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless Sensor Networks: a Survey, Computer Networks 38 (2002) 393—422. • Z. Xiong, A. Liveris, and S. Cheng, Distributed Source Coding for Sensor Networks, IEEE Signal Processing Magazine 21 (2004) 80—94. • A. Ramamoorthy, K. Jain, P. Chou, and M. Effros, Separating Distributed Source Coding from Network Coding, IEEE/ACM Transactions on Networking 14 (2006) 2785—2795. • J. Liu, M. Adler, D. Towsley, and C. Zhang, On Optimal Communication Cost for Gathering Correlated Data through Wireless Sensor Networks, In Proceedings of MobiCom 2006. • T. Batu, S. Dasgupta, R. Kumar, and R. Rubinfeld, The Complexity of Approximating Entropy, In Proceedings of STOC 2002. • M. Adler, Collecting Correlated Information from a Sensor Network, In Proceedings of SODA 2005.