With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal