Data Dissemination Protocols in Wireless Sensor Networks : Models, Security and Design

1. Data Dissemination Protocols in Wireless Sensor Networks : Models, Security and Design Candidacy Proposal Defense by : Pradip De Department of Computer Science and Engineering The University of Texas, Arlington Center for Research in Wireless Mobility and Networking Advisors : Sajal K. Das and Yonghe Liu Committee Members : Kalyan Basu, Mohan Kumar, Matthew Wright

2. Organization • Data Dissemination in Wireless Sensor Networks • Protocol Models and Performance Analysis • Security Analysis of Data Dissemination in Sensor Networks • Design of a Reprogramming Protocol for Mobile Sensor Networks • Ongoing and Future Work

3. Data Dissemination/Reprogramming in Wireless Sensor Networks

4. Motivation • Sensor Networks operate unattended for months or years • Little control once deployed • Environment changes over time • Reprogramming of sensors is essential • Evolving requirements • Bug fixes after deployment • Scale and embedded nature requires network code propagation • Data dissemination protocols

5. Protocol Design Objectives and Issues • Scalability • Size of network • Data/Code Size disseminated • Reliability • Robust against packet loss (wireless uncertainties) • Tolerate topology changes (node failures) • Reach all nodes staying uncorrupted • Efficiency • Rapid propagation required • Security • Authentication necessary

6. Protocols in the HorizonTrickle Levis et al, NSDI 2004 Deluge Hui and Culler, SenSys 2004 MNP Kulkarni and Wang, ICDCS 2005…

7. Deluge J. W. Hui and D. Culler. The dynamic behavior of a data dissemination protocol for network programming at scale. In Proceedings of the second International Conference on Embedded Networked Sensor Systems (SenSys 2004)

8. Deluge Protocol Overview • A General Protocol for Bulk Data Dissemination • State Machine with strictly local rules • Nodes advertise, request data, and broadcast • Object divided into contiguous pages, each consisting of N packets • Allows for spatial multiplexing

9. Objectives of Dissertation • Model based performance analysis of data propagation over dissemination protocols • Rate of information propagation • Model based security analysis of network-wide dissemination • Spread of node compromise in a sensor network with secure communication using pairwise keys • Malware propagation over data dissemination protocols • Design of a reprogramming protocol for mobilesensor networks • Performance analysis of protocol in mobile scenario • Modeling data/malware propagation over dissemination protocols in mobile sensor networks

10. Model Based Comparative Performance Analysis of Data Dissemination Protocols

11. Model Characteristics and Features • An epidemic theoretic model for analysis of data propagation over these protocols • Analytical tool for studying dissemination protocols • Measures rate of information propagation • Flexibility of model • accommodates different dissemination protocols • Mechanism for inter-protocol comparison • Propagation speed • Extent of coverage

12. Sensor Network Model • Modeled as an undirected geometric random graph • N nodes uniformly randomly distributed • Unit Disk Model with transmission radius • is the probability of edge existence between nodes u and v at distance • Node Density where A is the area of the terrain u v

13. Epidemic Theory : Overview • Epidemic Theory • Models an infection spread in a population of susceptibles • Broadly two kinds of modeling techniques • Random Graph based spatial model • Differential Equation based temporal model • Infection Spread Cases • Susceptible-Infected-Susceptible (SIS) • Susceptible-Infected-Recovered (SIR) • Homogeneously mixed population • Heterogeneously mixed population

14. Epidemic Theoretic Framework • Proposed Framework • Design the spread model using network characteristics • Adopt differential equation based approach • Data propagation conforms to “No Recovery” model • Local interactions based on transmission range • Estimate the rate of infection (β) based on • Rate of communication paradigm of the broadcast protocol • Infectivity potential (ρ) of the data

15. Infection Spread Model Source Node Susceptible S(t) Inoperative R(t) Infective I(t)

16. Model Derivation • No Recovery Based Infection Model • Infected nodes cannot be recovered and the infection ultimately reaches the whole network • Formulation of differential equations for I(t) and S(t) based on network parameters • At , I(t) = N where

17. Fitting Broadcast Protocols • Deluge • In the maintenance algorithm, the probability of node i broadcasting metadata in each time interval is given by where kdenotes the advertisement threshold in the period and denotes the expected number of neighbors of a node i • The expected time for a node to receive metadata is calculated using • The expected time to transmit a page in a neighborhood is derived from and the infection rate and is given by where is the infectivity potential of the data

18. Deluge : Data Propagation Rate Simulation Analytical

19. Summary • Performance analysis of broadcast protocols • Speed of propagation of data • Reachability into network • Construction of an epidemic model for data propagation • Flexible tool to compare different broadcast protocols

20. Security Analysis of Network-Wide Data Dissemination in Sensor Networks • Model based security analysis of network-wide dissemination • Spread of node compromise in a sensor network with secure communication using pairwise keys • Malware propagation over data dissemination protocols

21. Propagation of Node Compromise in Sensor Networks • Construction of a model and analysis of the spread of node compromise on a sensor network based on Epidemic Theory • Identify point of outbreak of the process in the network • Observe the impact of infectivity duration of a compromised node on the process • Identify critical values of relevant parameters to prevent outbreaks

22. Network Model • Consider two deployment strategies • a basic uniform random deployment strategy • A realistic group based deployment strategy • Adopt the same model for the physical network • An overlay with key sharing probability q based on random pairwise key predistribution

23. Topology Model : Group Based Deployment • A set of 2-dimensional Gaussian Distribution of resident points about the deployment point • g(x,y|j) represents the probability of a node belonging to group j to reside within transmission range of point x,y

24. Topology Model : Group Based Deployment • The probability that a node at (x,y) has l neighbors is expressed as Nb(l,x,y) • Nb(l,x,y) is a function of g(x,y|j) and the gaussian distributed node location pdf of (x,y) • The degree distribution p(k) of a node is given by where

25. Analysis Overview • Two scenarios • No recovery once compromised • Nodes recover • When nodes do not recover transmissibility is expressed only in terms of the infection probability • Essence of node recovery is captured by expressing the transmissibility as a function of the average duration of infectivity

26. Primary Analysis Results • Average Cluster size as the epidemic attains outbreak proportions • Average Epidemic size after outbreak results • Results observed under both scenarios of without node recovery and with node recovery

27. Epidemic size with infection probability

28. Summary • Study of spread of node compromise in sensor networks • Uniform random network model • Group deployment based network model • The outbreak points for network-wide compromise propagation are affected by the deployment strategy

29. Vulnerability of Broadcast Protocols to Malware Propagation • Model based security analysis of network-wide dissemination • Spread of node compromise in a sensor network with secure communication using pairwise keys • Malware propagation over data dissemination protocols

30. Vulnerability of Broadcast Protocols • Construct model to estimate vulnerability to piggybacked malware spread • Compromise propagation after a single or few nodes compromised by adversary • No Recovery case • Use the same model for data propagation • Infection ultimately spreads to the entire network

31. Attack Model Malware spreads, piggybacked on the broadcast protocol, passing security verification at each stage since source was compromised Broadcast Protocol wavefront; pass authentication Deploy Malicious Code Compromised Src; Authentication keys captured

32. Model Analysis • Imposition of a simultaneous recovery process • Parameterized by mean recovery rate of each node • The infection rate is computed from the communication rate of the protocol • Construct differential equations to compute the sub-populations I(t), S(t), and R(t)

33. Deluge : Spreading Time Comparison Simulation Analytical With Recovery

34. Summary • Reprogramming protocols are essential for sensor networks • However, they could be carriers for rapid spread of malicious code in sensor network • Analytical tool proposed • Gain valuable insights into the propagation characteristics of malware over different broadcast protocols • Tool is flexible for comparative studies of different broadcast protocols

35. Design of Reprogramming Protocols for Mobile Sensor Networks • Design of a reprogramming protocol for mobile sensor networks • Performance analysis of protocol in mobile scenario • Modeling data/malware propagation over dissemination protocols in mobile sensor networks

36. Reprogramming Protocols for Mobile Sensor Networks • Numerous applications for mobile sensor networks • Drawbacks of the existing reprogramming protocols for mobile scenarios • Location uncertainty due to mobility • Inefficiency of page ordered download • Dynamic changes in neighborhood node density • Protocol should take advantage of mobility

37. ReMo • Salient features • Based on a periodic metadata broadcast paradigm • The probability of broadcast is dynamically adjusted based on neighborhood density • Regardless of order, pages are downloaded based on availability • Snoop on neighborhood to construct link quality metrics • Choose neighbors appropriately for requesting downloads based on not only best link quality but also high potential of code availability

38. Link Characterization

39. Metadata Broadcast • and are the counts of the metadata advertisements that are different and same as current node • The periodic metadata broadcast probability for each time slot t is adjusted based on the above counts • Proportional increase in probability on hearing different metadata • Probability decreased aggressively on hearing same metadata

40. Protocol Components and Operation • Page Download Potential (PDP) • Based on the pages a node can potentially download from a neighbor • Neighbor Link Profile (NLP) • Aware of the current link quality with each neighbor • Link Quality estimate is updated as a window mean exponentially weighted moving average • Node i selects a neighbor j to send a download request based on NLP and PDP of j

41. Comparison of Code Update Completion Time

42. Number of Message Transmissions

43. Number of Message Transmissions

44. Number of Message Transmissions

45. Ongoing and Future Work • Design of a reprogramming protocol for mobile sensor networks • Performance analysis of protocol in mobile scenario • Modeling data/malware propagation over dissemination protocols in mobile sensor networks

46. Performance Analysis of Data Dissemination Protocols in Mobile Sensor Networks • Markov Chain based model of the protocol operation • Borrow ideas from MAC protocol analysis • 802.11 MAC models for backoff schemes • Derive throughput of data delivery over these protocols

47. Modeling of Information Propagation in Mobile Sensor Networks • Analytical model for the data propagation rate in mobile sensor network • Vulnerability assessment in a mobile scenario • Model approach based on epidemic theory • Assumption of homogeneous mixing among nodes possible

48. Implementation of ReMo • Implementation of ReMo on a testbed of SunSPOTs • Test the efficacy of the design of ReMo for code download under different real world mobility conditions • Implementation in the Java ME Framework • Compilation, Deployment and Execution using Ant scripts

49. Thank You

50. Relevant Publications • P. De, Y. Liu, and S. K. Das, “Modeling Node Compromise Spread in Wireless Sensor Networks Using Epidemic Theory”. InIEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) 2006. • P. De, Y. Liu, and S. K. Das, “Deployment Aware Modeling of Node Compromise Spread in Wireless Sensor Networks ”, under review in ACM Transactions on Sensor Networks. • P. De, Y. Liu, and S. K. Das, “Evaluating Broadcast Protocols in Sensor Networks : An Epidemic Theoretic Framework”, poster paper in The 3rd IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) 2007. • P. De, Y. Liu, and S. K. Das, “An Epidemic Theoretic Framework for Evaluating Broadcast Protocols in Wireless Sensor Networks”, In the 4thIEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS) 2007 • P. De, Y. Liu, and S. K. Das, “An Epidemic Theoretic Framework for Vulnerability Analysis of Broadcast Protocols in Wireless Sensor Networks ”, under review in IEEE Transactions on Mobile Computing. • P. De, Y. Liu, and S. K. Das, “Harnessing Epidemic Theory to Model Malware Propagation in Wireless Sensor Networks”, under review in IEEE Communications Magazine: Special Edition on Security in Mobile Ad Hoc and Sensor Networks • P. De, Y. Liu, and S. K. Das, “ReMo : An Energy Efficient Reprogramming Protocol for Mobile sensor Networks”, accepted for publication at The 6th IEEE International Conference on Pervasive Computing and Communications (PerCom) 2008. • Work under preparation • P. De, Y. Liu, and S. K. Das, “An Analytical Model for the Performance Analysis of Data Dissemination Protocols in Mobile Sensor Networks”. • P. De, Y. Liu, and S. K. Das, “Analyzing Information Propagation over Data Dissemination Protocols in Mobile Sensor Networks”.