2,767 research outputs found
A topology-oblivious routing protocol for NDN-VANETs
Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent
connectivity, which leads to failures of end-to-end paths between nodes. Named
Data Networking (NDN) is a network paradigm that deals with such problems,
since information is forwarded based on content and not on the location of the
hosts. In this work, we propose an enhanced routing protocol of our previous
topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs
(MMM-VNDN) routing strategy that exploits several paths to achieve more
efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN
(iMMM-VNDN), creates paths between a requester node and a provider by
broadcasting Interest messages. When a provider responds with a Data message to
a broadcast Interest message, we create unicast routes between nodes, by using
the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN
extracts and thus creates routes based on the MAC addresses from the strategy
layer of an NDN node. Simulation results show that our routing strategy
performs better than other state of the art strategies in terms of Interest
Satisfaction Rate, while keeping the latency and jitter of messages low
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?
With the current host-based Internet architecture, networking faces
limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm
mitigates such problems by releasing the need to have an end-to-end transport
session established during the life time of the data transfer. Moreover, the
ICN concept solves the mismatch between the Internet architecture and the way
users would like to use it: currently a user needs to know the topological
location of the hosts involved in the communication when he/she just wants to
get the data, independently of its location. Most of the research efforts aim
to come up with a stable ICN architecture in fixed networks, with few examples
in ad-hoc and vehicular networks. However, the Internet is becoming more
pervasive with powerful personal mobile devices that allow users to form
dynamic networks in which content may be exchanged at all times and with low
cost. Such pervasive wireless networks suffer with different levels of
disruption given user mobility, physical obstacles, lack of cooperation,
intermittent connectivity, among others. This paper discusses the combination
of content knowledge (e.g., type and interested parties) and social awareness
within opportunistic networking as to drive the deployment of ICN solutions in
disruptive networking scenarios. With this goal in mind, we go over few
examples of social-aware content-based opportunistic networking proposals that
consider social awareness to allow content dissemination independently of the
level of network disruption. To show how much content knowledge can improve
social-based solutions, we illustrate by means of simulation some
content-oblivious/oriented proposals in scenarios based on synthetic mobility
patterns and real human traces.Comment: 7 pages, 6 figure
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