Eiko Yoneki

Bubble rap: social-based forwarding in delay tolerant networks

The increasing penetration of smart devices with networking capability form novel networks. Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner. The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs. To that end, social information is an essential metric for designing forwarding algorithms for such types of networks. Previous methods relied on building and updating routing tables to cope with dynamic network conditions. On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior. A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms. In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces. The contributions of this paper are two-fold. First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance. Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm.

BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks

The increasing penetration of smart devices with networking capability form novel networks. Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner. The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs. To that end, social information is an essential metric for designing forwarding algorithms for such types of networks. Previous methods relied on building and updating routing tables to cope with dynamic network conditions. On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior. A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms. In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces. The contributions of this paper are two-fold. First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance. Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm.

Distributed community detection in delay tolerant networks

Community is an important attribute of Pocket Switched Networks (PSN), because mobile devices are carried by people who tend to belong to communities. We analysed community structure from mobility traces and used for forwarding algorithms [12], which shows significant impact of community. Here, we propose and evaluate three novel distributed community detection approaches with great potential to detect both static and temporal communities. We find that with suitable configuration of the threshold values, the distributed community detection can approximate their corresponding centralised methods up to 90% accuracy.

A socio-aware overlay for publish/subscribe communication in delay tolerant networks

The emergence of Delay Tolerant Networks (DTNs) has culminated in a new generation of wireless networking. We focus on a type of human-to-human communication in DTNs, where human behaviour exhibits the characteristics of networks by forming a community. We show the characteristics of such networks from extensive study of real-world human connectivity traces. We exploit distributed community detection from the trace and propose a Socio-Aware Overlay over detected communities for publish/subscribe communication. Centrality nodes have the best visibility to the other nodes in the network. We create an overlay with such centrality nodes from communities. Distributed community detection operates when nodes (i.e. devices) are in contact by gossipping, and subscription propagation is performed along with this operation. We validate our message dissemination algorithms for publish/subscribe with connectivity traces.

Exploring social context with the wireless rope

The Wireless Rope is a framework to study the notion of social context and the detection of social situations by Bluetooth proximity detection with consumer devices and its effects on group dynamics Users can interact through a GUI with members of an existing group or form a new group Connection information is collected by stationary tracking devices and a connection map of all participants can be obtained via the web Besides interaction with familiar persons, the Wireless Rope also includes strange persons to provide a rich representation of the surrounding social situation This paper seeks to substantiate the notion of social context by an exploratory analysis of interpersonal proximity data collected during a computer conference Two feature functions are presented that indicate typical situations in this setting.

Unified semantics for event correlation over time and space in hybrid network environments

The recent evolution of ubiquitous computing has brought with it a dramatic increase of event monitoring capabilities by wireless devices and sensors. Such systems require new, more sophisticated, event correlation over time and space. This new paradigm implies composition of events in heterogeneous network environments, where network and resource conditions vary. Event Correlation will be a multi-step operation from event sources to final subscribers, combining information collected by wireless devices into higher level information or knowledge. Most extant approaches to define event correlation lack a formal mechanism for establishing complex temporal and spatial relationships among correlated events. Here, we will focus on two subjects. First, we define generic composite event semantics, which extend traditional event composition with data aggregation in wireless sensor networks (WSNs). This work bridges data aggregation in WSNs with event correlation services over distributed systems. Secondly, we introduce interval-based semantics for event detection, defining precisely complex timing constraints among correlated event instances.

An adaptive approach to content-based subscription in mobile ad hoc networks

We describe a novel approach for content-based publish/subscribe system in mobile ad-hoc networks and show preliminary experiment results. We extended ODMRP (on-demand multicast routing protocol) using aggregated summaries of content-based subscriptions in Bloom filters expression for the dynamic construction of an event dissemination structure. ODMRPs mesh topology gives extra reliability. We describe experiment results including comparison with regular ODMRP and mobility simulation.

Visualizing community detection in opportunistic networks

Community is an important attribute of Pocket Switched Networks (PSNs), since mobile devices are carried by people who tend to belong to communities in their social life. We discover the heterogeneity of human interactions such as community formation from real world human mobility traces. We have introduced novel distributed community detection approaches and evaluated with those traces [11]. This paper describes a series of visualizations to show characteristics of human mobility traces including community detection. We focus on extracting information related to levels of clustering, network transitivity, and strong community structure. The progression of the connection map along the community formation process is also visualized.

Understanding and measuring the urban pervasive infrastructure

The increasing popularity of mobile computing devices has allowed for new research and application areas. Specifically, urban areas exhibit an elevated concentration of such devices enabling potential ad-hoc co-operation and sharing of resources among citizens. Here, we argue that people, architecture and technology together provide the infrastructure for these applications and an understanding of this infrastructure is important for effective design and development. We focus on describing the metrics for describing this infrastructure and elaborate on a set of observation, analysis and simulation methods for capturing, deriving and utilising those metrics.

ML-SOR

Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.