Human dynamic networks in opportunistic routing and epidemiology

Measuring human behavioral patterns has broad application across different sciences. An individual’s social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure. This thesis presents a data collection system using tiny sensors which is capable of recording humans’ proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants’ health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information. In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the model’s contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection

Efficient and adaptive congestion control for heterogeneous delay-tolerant networks

Detecting and dealing with congestion in delay-tolerant networks (DTNs) is an important and challenging problem. Current DTN forwarding algorithms typically direct traffic towards more central nodes in order to maximise delivery ratios and minimise delays, but as traffic demands increase these nodes may become saturated and unusable. We pro- pose CafRep, an adaptive congestion aware protocol that detects and reacts to congested nodes and congested parts of the network by using implicit hybrid contact and resources congestion heuristics. CafRep exploits localised relative utility based approach to offload the traffic from more to less congested parts of the network, and to replicate at adaptively lower rate in different parts of the network with non-uniform congestion levels. We extensively evaluate our work against benchmark and competitive protocols across a range of metrics over three real connectivity and GPS traces such as Sassy [44], San Francisco Cabs [45] and Infocom 2006 [33]. We show that CafRep performs well, independent of network connectivity and mobility patterns, and consistently outperforms the state-of-the-art DTN forwarding algorithms in the face of increasing rates of congestion. CafRep maintains higher availability and success ratios while keeping low delays, packet loss rates and delivery cost. We test CafRep in the presence of two application scenarios, with fixed rate traffic and with real world Facebook application traffic demands, showing that regardless of the type of traffic CafRep aims to deliver, it reduces congestion and improves forwarding performance

Delay tolerant networking in a shopping mall environment

The increasing popularity of computing devices with short-range wireless offers new communication service opportunities. These devices are small and may be mobile or embedded in almost any type of object imaginable, including cars, tools, appliances, clothing and various consumer goods. The majority of them can store data and transmit it when a wireless, or wired, transmitting medium is available. The mobility of the individuals carrying such short-range wireless devices is important because varying distances creates connection opportunities and disconnections. It is likely that successful forwarding algorithms will be based, at least in part, on the patterns of mobility that are seen in real settings. For this reason, studying human mobility in different environments for extended periods of time is essential. Thus we need to use measurements from realistic settings to drive the development and evaluation of appropriate forwarding algorithms. Recently, several significant efforts have been made to collect data reflecting human mobility. However, these traces are from specific scenarios and their validity is difficult to generalize. In this thesis we contribute to this effort by studying human mobility in shopping malls. We ran a field trial to collect real-world Bluetooth contact data from shop employees and clerks in a shopping mall over six days. This data will allow the informed design of forwarding policies and algorithms for such settings and scenarios, and determine the effects of users' mobility patterns on the prevalence of networking opportunities. Using this data set we have analysed human mobility and interaction patterns in this shopping mall environment. We present evidence of distinct classes of mobility in this situation and characterize them in terms of power law coefficients which approximate inter-contact time distributions. These results are quite different from previous studies in other environments. We have developed a software tool which implements a mobility model for "structured" scenarios such as shopping malls, trade fairs, music festivals, stadiums and museums. In this thesis we define as structured environment, a scenario having definite and highly organised structure, where people are organised by characteristic patterns of relationship and mobility. We analysed the contact traces collected on the field to guide the design of this mobility model. We show that our synthetic mobility model produces inter-contact time and contact duration distributions which approximate well to those of the real traces. Our scenario generator also implements several random mobility models. We compared our Shopping Mall mobility model to three other random mobility models by comparing the performances of two benchmark delay tolerant routing protocols, Epidemic and Prophet, when simulated with movement traces from each model. Thus, we demonstrate that the choice of a mobility model is a significant consideration when designing and evaluating delay-tolerant mobile ad-hoc network protocols. Finally, we have also conducted an initial study to evaluate the effect of delivering messages in shopping mall environments by exclusively forwarding them to customers or sellers, each of which has distinctive mobility patterns

Effective and Efficient Communication and Collaboration in Participatory Environments

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