68,098 research outputs found
Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks
Mobile social networks (MSNs) enable people with similar interests to
interact without Internet access. By forming a temporary group, users can
disseminate their data to other interested users in proximity with short-range
communication technologies. However, due to user mobility, airtime available
for users in the same group to disseminate data is limited. In addition, for
practical consideration, a star network topology among users in the group is
expected. For the former, unfair airtime allocation among the users will
undermine their willingness to participate in MSNs. For the latter, a group
head is required to connect other users. These two problems have to be properly
addressed to enable real implementation and adoption of MSNs. To this aim, we
propose a Nash bargaining-based joint head selection and airtime allocation
scheme for data dissemination within the group. Specifically, the bargaining
game of joint head selection and airtime allocation is first formulated. Then,
Nash bargaining solution (NBS) based optimization problems are proposed for a
homogeneous case and a more general heterogeneous case. For both cases, the
existence of solution to the optimization problem is proved, which guarantees
Pareto optimality and proportional fairness. Next, an algorithm, allowing
distributed implementation, for join head selection and airtime allocation is
introduced. Finally, numerical results are presented to evaluate the
performance, validate intuitions and derive insights of the proposed scheme
Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles
Vehicular Ad-hoc Networks (VANET) enable efficient communication between
vehicles with the aim of improving road safety. However, the growing number of
vehicles in dense regions and obstacle shadowing regions like Manhattan and
other downtown areas leads to frequent disconnection problems resulting in
disrupted radio wave propagation between vehicles. To address this issue and to
transmit critical messages between vehicles and drones deployed from service
vehicles to overcome road incidents and obstacles, we proposed a hybrid
technique based on fog computing called Hybrid-Vehfog to disseminate messages
in obstacle shadowing regions, and multi-hop technique to disseminate messages
in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to
changes in an environment and benefits in efficiency with robust drone
deployment capability as needed. Performance of Hybrid-Vehfog is carried out in
Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators.
The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message
Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP),
PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data
Networking (NDN) with mobility, and flooding schemes at all vehicle densities
and simulation times
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
Nowadays, events usually burst and are propagated online through multiple
modern media like social networks and search engines. There exists various
research discussing the event dissemination trends on individual medium, while
few studies focus on event popularity analysis from a cross-platform
perspective. Challenges come from the vast diversity of events and media,
limited access to aligned datasets across different media and a great deal of
noise in the datasets. In this paper, we design DancingLines, an innovative
scheme that captures and quantitatively analyzes event popularity between
pairwise text media. It contains two models: TF-SW, a semantic-aware popularity
quantification model, based on an integrated weight coefficient leveraging
Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series
alignment model matching different event phases adapted from Dynamic Time
Warping. We also propose three metrics to interpret event popularity trends
between pairwise social platforms. Experimental results on eighteen real-world
event datasets from an influential social network and a popular search engine
validate the effectiveness and applicability of our scheme. DancingLines is
demonstrated to possess broad application potentials for discovering the
knowledge of various aspects related to events and different media
Delay Tolerant Networking over the Metropolitan Public Transportation
We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Many decentralized online social networks (DOSNs) have been proposed due to
an increase in awareness related to privacy and scalability issues in
centralized social networks. Such decentralized networks transfer processing
and storage functionalities from the service providers towards the end users.
DOSNs require individualistic implementation for services, (i.e., search,
information dissemination, storage, and publish/subscribe). However, many of
these services mostly perform social queries, where OSN users are interested in
accessing information of their friends. In our work, we design a socially-aware
distributed hash table (DHTs) for efficient implementation of DOSNs. In
particular, we propose a gossip-based algorithm to place users in a DHT, while
maximizing the social awareness among them. Through a set of experiments, we
show that our approach reduces the lookup latency by almost 30% and improves
the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc
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