Profile-Cast: Behavior-Aware Mobile Networking

Hsu, Wei-jen; Dutta, Debojyoti; Helmy, Ahmed

doi:10.1109/wcnc.2008.530

Cited by 45 publications

(59 citation statements)

References 11 publications

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“…Simulations recreated a large variety of practical conditions (see, sec.IV-A) where sources either belong to the set of recipients or are outside of it, and recipients either belong to a given community of interests or are unequally dispersed among them. This enables us to test the algorithm behavior under far more general conditions than those assumed in previous works, such as [12] and [6]. We assume that every 30 minutes, each node generates a message labeled with interest I.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Interest-driven forwarding for delay-tolerant mobile ad hoc networks

Pagani

Rossi

2013

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)

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Abstract-In the era of pervasive mobile computing, human encounters can be leveraged to enable new forms of social interactions mediated by the personal devices of individuals. In this framework, emerging needs, such as content dissemination, social discovery and question&answering, advocate the raising of novel communication paradigms where the binding contentrecipients is not provided by the sender (in the classical IP addressing style), but directly executed by specific recipients with interest on it. This allows tagged contents to be freely advertised on the network according to a content-driven approach; human encounters drive the information towards potential recipients that extract it from the stream when content type and personal interest match. This very active research area has recently produced a few preliminary solutions to this networking problem; they inherently confine message delivery inside a specific location and/or community. This covers only a part of users needs, as emerging from everyday life experience and recent studies in human sciences. This paper proposes a novel communication protocol, named InterestCast, or ICast, solving the problem for a wide range of social scenarios and applying to a delay tolerant ad hoc network whose nodes are the personal device of moving individuals, possibly interacting with fixed road-side devices. The protocol is able to chase users interests decoupling content tags from locations and social communities. The main advantages the proposal achieves are: it ensures remarkable performance results; it is simple and, thus, it is feasible and keeps computational and networking costs low; it preserves users privacy.

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Section: Performance Evaluationmentioning

confidence: 99%

“…Some preliminary results are obtained by starting from the basic assumption that the user's interests and customarily frequented locations are closely related to one another. ProfileCast [12] belongs to this category. A content generated by a node is addressed to ("is of interest for") nodes used to visit the same locations as the source.…”

Section: Introductionmentioning

confidence: 99%

Interest-driven forwarding for delay-tolerant mobile ad hoc networks

Pagani

Rossi

2013

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)

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“…At the network layer, the multicast entity, multicastrelay, receives messages from encounters, when a contact event occurs, stores them locally, takes forwarding decisions and may deliver a message to the upper application entity, (end-node), according to membership conditions. End-nodes agree on a super-set of metadata that can represent locations (e.g., [4]), resources, interests or social attitudes of users. 1 The behavior-aware multicast model we consider is different from classical IP-multicast adopted, e.g., in [6].…”

Section: Multicast Architecture and Modelmentioning

confidence: 99%

“…By browsing the literature, Mobility ProfileCast, [4], is probably the only approach fitting with the described multicast scenario and trying to deal with membership and contact knowledge. It assumes that relays know a common set of locations (that defines K C ), coincident with the common set of interests (that define K M ) shared by the nodes.…”

Section: Role Of Knowledge In On Multicastmentioning

confidence: 99%

“…The huge of proposals on unicast and broadcast communications in ON is compensated by very few papers coping with the multicast issues. At the best of our knowledge, multicast is explored in detail only in [3], where new semantic models are defined taking into account timing aspects related with the long delays involved in ONs, and in [4], where a similar multicast semantics (ProfileCast) has been adopted. Other few provide multicast protocols under specific ON conditions.…”

Section: Introductionmentioning

confidence: 99%

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Reasoning about multicast in Opportunistic Networks

Pagani

Rossi

2011

2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

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Abstract-Mobile computing services are becoming highly personalized and influenced by user location, social attitudes and interests. This trend is often synthesized with the term behavior-awareness. Behavioral services characterize most of the applications of Opportunistic Networks and advocate a single communication paradigm: multicast. Despite that, multicast is less studied than unicast and broadcast. We believe that there is not correspondence between problem relevance and research attention. This paper focuses on the multicast best effort service in opportunistic networks with the aim of better understanding the problem and defining the guidelines for the design of a novel multicast protocol.

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Community Detection in Biological Networks

Thakur

2011

Applied Statistics for Network Biology

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Networks are everywhere and evolving in various forms, following certain characteristic patterns like small-world and scale-free characteristics. The basis of these forms is not the random addition of nodes and vertices to the system under study, but a systematic process of mutual evolution. This makes many aspects of such systems worth studying. Researchers have invested many years interpreting such systems, for instance how biological networks evolve and why humans form societies to name a few. Thus, seminal to the behavior to these systems are the way internal components are connected to each other. These connections breed structures that exemplify the nature and the behavior of the overall system. Advances in genomics have generated enormous amounts of data, currently gathered by biologists and now available to the scientific community at large. The categorization of this data naturally falls into various classes, ranging from protein sequences to complex molecules. Thus, challenges lie in understanding the properties and dynamics of such complex systems rather than gaining insight into the individual components. Fortunately, promises shown in statistical physics and network theory that offer a host of methods and tools to analyze large data can invariably be used in biology. Modeling interactions between proteins, cells, and other genomic data as network graphs and applying statistical techniques on them can greatly benefit us in scrutinizing the intricacies that exist in these datasets. These methods not only simplify the representations, but also aid in searching for general principles of organization and evolution of biological structures. For example, smallworld analysis is used to explain protein-protein interaction networks and scale-free invariance properties exist for in silico evolution of metabolic networks, which encode an increasing amount of information about their environment. The questions often arises as to which properties of such systems are worth studying and how are the emergent behaviors actually affecting the overall functionality of such systems. Visualization is a potential tool that was used as a first step to display the connectivity patterns between various components and describe the structure as a whole. Another

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Profile-Cast: Behavior-Aware Mobile Networking

Cited by 45 publications

References 11 publications

Interest-driven forwarding for delay-tolerant mobile ad hoc networks

Interest-driven forwarding for delay-tolerant mobile ad hoc networks

Reasoning about multicast in Opportunistic Networks

Community Detection in Biological Networks

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