1. An Efficient Service Propagation Scheme for Large-Scale MANETs Choonhwa Lee, Sungshick Yoon, Eensam Kim, and Sumi Helal Nov 28, 2006 College of Information and Communications Hanyang University South Korea

2. MPAC2006 2 Outline Service Discovery Ad Hoc Service Discovery Protocols Bloom Filter-Based Service Discovery Fisheye Bloom Filter Simulation Study

3. MPAC2006 3 Service Discovery Service discovery protocols –are network protocols which allow automatic detection of devices and services offered by these devices on a computer network [Wikipedia]. Everything is a service on the network –Devices, network resources, software modules, a piece of information, and even users of the system

4. MPAC2006 4 Ad Hoc Service Discovery Ad hoc service discovery –Service discovery is an integral part of the ad hoc collaboration networking to achieve stand-alone and self-configurable communication networks [8]. –Far more dynamic and heterogeneous networks A wide range of wireless, mobile devices Resource constraint (no fixed infrastructure support) Topology dynamics (instability caused by node mobility and failures) –Challenges and requirements Decentralized operation Very low or zero configuration and administration Energy awareness (minimal network transmission and lightweight) Adaptive to highly changing environments A range of different application needs

5. MPAC2006 5 MANET SDPs - Taxonomy By supporting layer –Network layer Coupling service discovery with routing protocols –Service query and response messages are often piggybacked onto ad hoc routing protocols. AODV-SD, ODMRP-SD, M-ZRP, LSD, and DSD –Application layer Service discovery functionality is supported above the routing layer. –Usually, services belong to the application level. DEAPspace, Konark, GSD, SSD, and SANDMAN

6. MPAC2006 6 Bloom Filter h1h2h3h4 000000000000000 0 w = printerw = faxq = printer Match! ( hit ) 1111111 q = movies Not match! ( miss ) q = music Match! ( false positive) v hihi A vector v of m bits initially set to 0. k independent hash function h 1, h 2, … h k. For each service description w, v[h i (w)] = 1 where i = 1, 2,.., k.

7. MPAC2006 7 Bloom Filter-Based Discovery Trade-off between time and space Lossy aggregation of service information in favor of service advertisement traffic [18] [12] The filter can easily be filled up in a densely populated network –False positives are more likely. –Longer query routing time.

8. MPAC2006 8 Bloom Filter-Based Discovery A B C D F v = 1101 E FilterNext Node 1101B v = 1101 v = 1100 q = printer v = 1100 G ValueNext Node 0001local 1101D FilterNext Node 1100local (printer) 1001F 1010local FilterNext Node 1000local 1101E C FilterNext Node 1001local 1100F FilterNext Node 1100local (printer) 0100G 4 hops F

9. MPAC2006 9 Fisheye Bloom Filter The filter is now extended into two dimensional arrays to embed distance information to service instances. Algorithms fillin (v 0, 0) // v 0 initialized to zero for every hash function, h i, and every service attribute w of services in a local area { v 0 [h i (w)] = 1 } for each element position p from 1 to m { if (v 0 [p] == 0) { v 0 [p] = MIN(MIN(v 1 [p], v 2 [p], …, v n [p])+1, 2 d ) }

10. MPAC2006 10 Fisheye Bloom Filter Query routing –Based on the distance information embedded in the filter –Services in question may be located at MAX(v[h 1 (w)], v[h 2 (w)],, …, v[h k (w)]) hops or further. –Similar, in spirit, to FSR (Fisheye State Routing). Hence the name of Fisheye Bloom Filter v 0 [p] = MIN(MIN(v 1 [p], v 2 [p], …, v n [p])+1, 2 d ) –MIN(v 1 [p], v 2 [p], …, v n [p]) Closest service information –MIN(v 1 [p], v 2 [p], …, v n [p])+1 Hop count from the source node A B C 11845 v1v1 14317 v2v2 12426 v0v0

11. MPAC2006 11 Fisheye Bloom Filter-Based Discovery A B C D F v = 1201 v = 1302 E v = 2201 FilterNext Node 3302B v = 1101 v = 1100 q = printer v = 1100 G FilterNext Node 0001local 2403D FilterNext Node 1100local (printer) 1001F 1010local FilterNext Node 1000local 2302E 2202 C FilterNext Node 1001local 2200F FilterNext Node 1100local (printer) 0100G 2 hops F

12. MPAC2006 12 Simulation Study Simulation setup –ns-2.26 simulator + MAODV patch [19] –ns-2 UDP agent programming for Bloom filter- and Fisheye Bloom filter-based protocols Base case –Bloom filter-based discovery scheme [12] Performance metrics while varying network size & mobility, service density, and Fisheye filter depth (d) –Query routing path length –Service discovery traffic –Service advertisement traffic

13. MPAC2006 13 Simulation Study Workload –English-English dictionary A word (as service names) - its definition (as descriptive service attributes) Manet – French painter and forerunner of impressionist whose works were highly controversial in his day Simulation under way

14. MPAC2006 14 References [1] F. Zhu, M. Mutka, and L. Ni, Service Discovery in Pervasive Computing Environments, IEEE Pervasive Computing, 2005. [2] G. Coulouris, J. Dollimore, and T. Kindberg, Distributed Systems: Concepts and Design, Addison Wesley, 2005. [3] E. Guttman, C. Perkins, J. Veizades, and M. Day, Service Location Protocol Version 2, IETF RFC 2608, 1999. [4] R. Koodli and C. E. Perkins, Service Discovery in On-Demand Ad Hoc Networks, IETF Internet Draft - work in progress, 2002. [5] L. Cheng, Service Advertisement and Discovery in Mobile Ad Hoc Networks, Proc. of the ACM 2002 Conf. on Computer Supported Cooperative Work Workshops, 2002. [6] C. Ververidis and G. Polyzos, Routing Layer Support for Service Discovery in Mobile Ad Hoc Networks, Proc. of the 3rd Intl Conf. on Pervasive Computing and Communications Workshops, 2005. [7] L. Li and L. Lamont, A Lightweight Service Discovery Mechanism for Mobile Ad Hoc Pervasive Environment Using Cross-Layer Design, Proc. of the 3rd Intl Conf. on Pervasive Computing and Communications Workshops, 2005. [8] U. Kozat and L. Tassiulas, Network Layer Support for Service Discovery in Mobile Ad Hoc Networks, Proc. of IEEE INFOCOM, 2003. [9] M. Nidd, Service Discovery in DEAPspace, IEEE Personal Communications, 2001. [10] D. Chakraborty, A. Joshi, Y. Yesha, and T. Finin, GSD: A Novel Group-Based Service Discovery Protocol for MANETs, Proc. of the 4th IEEE Conf. on Mobile and Wireless Communication Networks, 2002. [11] S. Helal, N. Desai, V. Verma, and C. Lee, Konark - Service Discovery and Delivery Protocol for Ad Hoc Networks, Proc. of IEEE Wireless Communications and Networking Conf., 2003. [12] F. Saihan and V. Issarny, Scalable Service Discovery for MANET, Proc. of the 3rd Intl Conf. on Pervasive Computing and Communications, 2005. [13] G. Schiele, C. Becker, and K. Rothermel, Energy-Efficient Cluster-Based Service Discovery for Ubiquitous Computing, Proc. of the 11th ACM SIGOPS European Workshop, 2004. [14] D. Bucur, Activity-Based Sensor Network – Service Discovery: Overview and Related Work, Aarhus University, Denmark, 2006. [15] Alina Kopp, Service Discovery, Saarland University, Germany, 2004. [16] Zoltan Juhasz, Jini Technology – Technical Overview, University of Veszprem, Hungary, 2005. [17] W. K. Edwards, Discovery Systems in Ubiquitous Computing, IEEE Pervasive Computing, April-June 2006. [18] S. Czerwinski, B. Zhao, T. Hodes, A. Joseph, and R. Katz, An Architecture for a Secure Service Discovery Service, Proc. of the 5th Intl Conf. on Mobile Computing and Networks, 1999. [19] Y. Zhu and T. Kunz, MAODV Implementation for ns-2.26, Technical Report SCE-04-1, Systems and Computing Engineering, Carleton University, Canada, January 2004.

15. MPAC2006 15 Thank you!