45,573 research outputs found
Social-Aware Forwarding Improves Routing Performance in Pocket Switched Networks
Several social-aware forwarding strategies have been recently introduced in
opportunistic networks, and proved effective in considerably in- creasing
routing performance through extensive simulation studies based on real-world
data. However, this performance improvement comes at the expense of storing a
considerable amount of state information (e.g, history of past encounters) at
the nodes. Hence, whether the benefits on routing performance comes directly
from the social-aware forwarding mechanism, or indirectly by the fact state
information is exploited is not clear. Thus, the question of whether
social-aware forwarding by itself is effective in improving opportunistic
network routing performance remained unaddressed so far. In this paper, we give
a first, positive answer to the above question, by investigating the expected
message delivery time as the size of the net- work grows larger
Architecture for Mobile Heterogeneous Multi Domain Networks
Multi domain networks can be used in several scenarios including military, enterprize networks, emergency networks and many other cases. In such networks, each domain might be under its own administration. Therefore, the cooperation among domains is conditioned by individual domain policies regarding sharing information, such as network topology, connectivity, mobility, security, various service availability and so on. We propose a new architecture for Heterogeneous Multi Domain (HMD) networks, in which one the operations are subject to specific domain policies. We propose a hierarchical architecture, with an infrastructure of gateways at highest-control level that enables policy based interconnection, mobility and other services among domains. Gateways are responsible for translation among different communication protocols, including routing, signalling, and security. Besides the architecture, we discuss in more details the mobility and adaptive capacity of services in HMD. We discuss the HMD scalability and other advantages compared to existing architectural and mobility solutions. Furthermore, we analyze the dynamic availability at the control level of the hierarchy
PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies
Many interactions between network users rely on trust, which is becoming
particularly important given the security breaches in the Internet today. These
problems are further exacerbated by the dynamics in wireless mobile networks.
In this paper we address the issue of trust advisory and establishment in
mobile networks, with application to ad hoc networks, including DTNs. We
utilize encounters in mobile societies in novel ways, noticing that mobility
provides opportunities to build proximity, location and similarity based trust.
Four new trust advisor filters are introduced - including encounter frequency,
duration, behavior vectors and behavior matrices - and evaluated over an
extensive set of real-world traces collected from a major university. Two sets
of statistical analyses are performed; the first examines the underlying
encounter relationships in mobile societies, and the second evaluates DTN
routing in mobile peer-to-peer networks using trust and selfishness models. We
find that for the analyzed trace, trust filters are stable in terms of growth
with time (3 filters have close to 90% overlap of users over a period of 9
weeks) and the results produced by different filters are noticeably different.
In our analysis for trust and selfishness model, our trust filters largely undo
the effect of selfishness on the unreachability in a network. Thus improving
the connectivity in a network with selfish nodes.
We hope that our initial promising results open the door for further research
on proximity-based trust
Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster
The widespread use of GPS-enabled smartphones along with the popularity of
micro-blogging and social networking applications, e.g., Twitter and Facebook,
has resulted in the generation of huge streams of geo-tagged textual data. Many
applications require real-time processing of these streams. For example,
location-based e-coupon and ad-targeting systems enable advertisers to register
millions of ads to millions of users. The number of users is typically very
high and they are continuously moving, and the ads change frequently as well.
Hence sending the right ad to the matching users is very challenging. Existing
streaming systems are either centralized or are not spatial-keyword aware, and
cannot efficiently support the processing of rapidly arriving spatial-keyword
data streams. This paper presents Tornado, a distributed spatial-keyword stream
processing system. Tornado features routing units to fairly distribute the
workload, and furthermore, co-locate the data objects and the corresponding
queries at the same processing units. The routing units use the Augmented-Grid,
a novel structure that is equipped with an efficient search algorithm for
distributing the data objects and queries. Tornado uses evaluators to process
the data objects against the queries. The routing units minimize the redundant
communication by not sending data updates for processing when these updates do
not match any query. By applying dynamically evaluated cost formulae that
continuously represent the processing overhead at each evaluator, Tornado is
adaptive to changes in the workload. Extensive experimental evaluation using
spatio-textual range queries over real Twitter data indicates that Tornado
outperforms the non-spatio-textually aware approaches by up to two orders of
magnitude in terms of the overall system throughput
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