Network Layer Support for Service Discovery in MANETs Ulas Kozat and Leandros Tassiulas University of Maryland, College Park Presented by Wei Gao

CSE 535 Mobile Computing 2/17 Background Service discovery Users search for their desired network services Examples of network services: remote printers, scanners, data sources, and webpages……

CSE 535 Mobile Computing 3/17 Background Components of a service discovery system: Service requesters Service providers Service repositories A service discovery process include: Dissemination of service discovery messages Matchmaking between the services requested and provided

CSE 535 Mobile Computing 4/17 Background Service discovery in MANETs MANETs are highly dynamic Prevalent node mobility Adverse channel conditions Unpredictable node failures Requirements for service discovery Scalable to search for matching services quickly and efficiently Robust against unpredictable network topology changes

CSE 535 Mobile Computing 5/17 Overall approach Efficient dissemination of service discovery messages among service providers and repositories Backbone management phase: Formulation of 1-hop clusters and a virtual backbone Distributed service discovery phase: Distribution of service discovery messages

CSE 535 Mobile Computing 6/17 Backbone Management (BBM) Phase Some notations regarding the virtual backbone Service Broker Node (SBN): the clusterheads connecting other non-backbone nodes Virtual Access Point (VAP): the non-backbone node acting as the access point for another node to the backbone Directory Agent (DA): software agent that store service registrations

CSE 535 Mobile Computing 7/17 Backbone Management (BBM) Phase Construct a virtual backbone for dissemination of service discovery messages Essentially, a clustering process Clusterheads consists of a dominating set Criterion of clusterhead selection Normalized link failure frequency (NLFF) Properties: Convergence of cluster formation: a dominating set can always be constructed (Lemma 1) Cluster completeness: every node has an associated VAP (Lemma 2)

CSE 535 Mobile Computing 8/17 Backbone Management (BBM) Phase An example

CSE 535 Mobile Computing 9/17 Distributed Service Discovery (DSD) Phase Service registration A node registers its service to the DA located on its VAP For a backbone node, just register at itself Multicast dissemination to other DAs located on other VAPs A multicast tree is rooted at every DA consisting all the other VAPs in the network Construction of multicast tree: broadcast + self-pruning

CSE 535 Mobile Computing 10/17 Distributed Service Discovery (DSD) Phase Example of multicast tree construction

CSE 535 Mobile Computing 11/17 Distributed Service Discovery (DSD) Phase An example of resource discovery

CSE 535 Mobile Computing 12/17 Simulation results Successful ratio Advantage is shown compared to plain network architecture Slightly affected by node mobility

CSE 535 Mobile Computing 13/17 Simulation results Service discovery delay Improved over anycast-DSR Slower response than DSDP and AODV

CSE 535 Mobile Computing 14/17 Simulation results Constructions of backbone and multicast trees cost extra communication overhead

CSE 535 Mobile Computing 15/17 Summary Pros: Propose an approach to constructing a clustered network architecture for service discovery The clustered network architecture is able to achieve high successful ratio of service discovery Distributed service discovery provides sound robustness against link failures

CSE 535 Mobile Computing 16/17 Summary Cons: The proposed approach leads to extra communication overhead, and thus may not be suitable for large-scale networks This is mainly because the 1-hop clustered architecture. There will be too many backbone nodes and VAPs in the network

CSE 535 Mobile Computing 17/17 Relation to course project Multi-hop extension based on the approach in this paper Service discovery on multi-hop clusters Clusterhead selection considering both efficiency and stability Solving possible inconsistency Distributed storage of service registrations Deployment of local DHTs on multi-hop clusters Local searching based on Hash results