Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments.
There are three main contributions in this dissertation:
1. A novel scheme for predicting contact volume during an opportunistic contact (PCV);
2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and
3. An algorithm for mobility and orientation estimation in mobile environments (MOEME).
The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes.
The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost.
We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System.
Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets
