1. MobiMedia 2007, Nafpaktos, Greece International Mobile Multimedia Communications Conference 2007 An Epidemiological Model for Semantics Dissemination Christos Anagnostopoulos 1, Evangelos Zervas 2, Stathes Hadjiefthymiades 1 1 National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Pervasive Computing Research Group 2 TEI of Athens, Department of Electronics

2. MobiMedia 2007, Nafpaktos, Greece Collaborative Context-Aware Computing Context: the current values of specific ingredients that represent an activity of an entity, Awareness: understanding of the activities of an entity, Context-Awareness: the ability of user applications (system) to discover (sense and interpret) and react to changes in the environment they are situated in, Collaborative Context-Awareness: an understanding of the activities / conditions / environmental parameters of neighboring nodes that, consequently, provides a more enhanced context for an individual. Collaborative Context-Aware Applications: generate inferred knowledge needed by the rest of the group, adapt information dissemination algorithms, and, exploit the ways in which users’ behavior coincides with their interests.

3. MobiMedia 2007, Nafpaktos, Greece Contextual Information analogous to Epidemic Disseminated contextual information could match an epidemic: a mobile node carrying a temporal valid piece of information content becomes infectious; otherwise it is susceptible. Infectious node: disseminates information to its neighboring node according to mobile context (e.g., network connectivity) and interest (e.g., profile). Epidemiological Model: Susceptible-Infected-Susceptible (SIS)

4. MobiMedia 2007, Nafpaktos, Greece Semantics-based Dissemination Epidemics are semantically related. Pieces of context are hierarchically structured from the most abstract to the most specific context [e.g., Soul is-a Rhythm_and_Blues is-a Blues (music genres)] Temporally valid pieces of context (e.g., recently sensed context better interprets the depiction of a nodes' environment than least sensed or obsolete context)  the knowledge derived from the most specific context implies also the knowledge derived from the most abstract one A node autonomously deduces whether the incoming epidemic refers to context that adequately matches to a node’s interest or not.

5. MobiMedia 2007, Nafpaktos, Greece Epidemical Transmutation More specific context refers to a stronger epidemic. A node infers the context specificity through a semantic reasoning process: The strongest epidemic infects a large portion of the group. The weakest epidemic infects a small portion of the group. Semantically-dependent epidemics through semantic relations in conceptual hierarchies can transmute to stronger ones (metallaxis in Greek). Double-epidemical spreading: Portion of the population is infected either with epidemics or with transmutations. Corresponds to the heterogeneous need of each node, as required in the collaborative context-aware systems, i.e., not all nodes is interested in the same context. Proposed Epidemiological Model: Susceptible- a Infected-Susceptible (S a IS) A node can be re-infected with a more stronger epidemic thus a ggravating its condition A node can be fully or partially cured

6. MobiMedia 2007, Nafpaktos, Greece State Transitions in S a IS p0p0 p1p1 p2p2 full cure δ 20 full cure δ 10 partial cure δ 21 aggravation infection A node transits between: Infection state p 1, Infection state p 2 (a transmutation of p 1 ), Susceptible state p 0.

7. MobiMedia 2007, Nafpaktos, Greece Probability of a ggravation Given the status of the neighbors of node i at time instant t and the fact that node i may be infectious at state p k, at the next time instant t + 1, node i will be infectious at a higher state p l with probability Q kl The probability that all the neighboring nodes being in a state greater than l will not infect node i, The probability that one or more nodes will infect node i at infection level l, and, The node i will not recover.

8. MobiMedia 2007, Nafpaktos, Greece Analytical & Simulation Results 2D lattice – Homogeneous network, M = 10,000, β = 0.2, δ 10 = δ 21 = 0.1, δ 20 = 0.01 Since nodes reason about more specific knowledge then, they are re-infected with the strongest epidemic assuming that the latter matches better to their interests.

9. MobiMedia 2007, Nafpaktos, Greece Analytical & Simulation Results β = 0.2, δ 10 = δ 21 = 0.1, δ 20 = 0.6 If δ 20 is relatively larger than β, (e.g., a minor portion of nodes are capable of reasoning) the propagation process for the strongest epidemic decays. The weakest epidemic cannot transmute to a stronger epidemic due to the limited reasoning capability of the majority of nodes The infection of the strongest epidemic p 2 depends highly on the fact that at least one node is capable of inferring p 2 from p 1, or, at least a node is infected with p 2 at the beginning of the process.

10. MobiMedia 2007, Nafpaktos, Greece Thank you Christos Anagnostopoulos bleu@di.uoa.gr Pervasive Computing Research Group http://p-comp.di.uoa.gr