42,629 research outputs found
Routing in Mobile Ad-Hoc Networks using Social Tie Strengths and Mobility Plans
We consider the problem of routing in a mobile ad-hoc network (MANET) for
which the planned mobilities of the nodes are partially known a priori and the
nodes travel in groups. This situation arises commonly in military and
emergency response scenarios. Optimal routes are computed using the most
reliable path principle in which the negative logarithm of a node pair's
adjacency probability is used as a link weight metric. This probability is
estimated using the mobility plan as well as dynamic information captured by
table exchanges, including a measure of the social tie strength between nodes.
The latter information is useful when nodes deviate from their plans or when
the plans are inaccurate. We compare the proposed routing algorithm with the
commonly-used optimized link state routing (OLSR) protocol in ns-3 simulations.
As the OLSR protocol does not exploit the mobility plans, it relies on link
state determination which suffers with increasing mobility. Our simulations
show considerably better throughput performance with the proposed approach as
compared with OLSR at the expense of increased overhead. However, in the
high-throughput regime, the proposed approach outperforms OLSR in terms of both
throughput and overhead
Adaptive Dynamics of Realistic Small-World Networks
Continuing in the steps of Jon Kleinberg's and others celebrated work on
decentralized search in small-world networks, we conduct an experimental
analysis of a dynamic algorithm that produces small-world networks. We find
that the algorithm adapts robustly to a wide variety of situations in realistic
geographic networks with synthetic test data and with real world data, even
when vertices are uneven and non-homogeneously distributed.
We investigate the same algorithm in the case where some vertices are more
popular destinations for searches than others, for example obeying power-laws.
We find that the algorithm adapts and adjusts the networks according to the
distributions, leading to improved performance. The ability of the dynamic
process to adapt and create small worlds in such diverse settings suggests a
possible mechanism by which such networks appear in nature
Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks
For improving the efficiency and the reliability of the opportunistic routing
algorithm, in this paper, we propose the cross-layer and reliable opportunistic
routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the
improved efficiency fuzzy logic and humoral regulation inspired topology
control into the opportunistic routing algorithm. In CBRT, the inputs of the
fuzzy logic system are the relative variance (rv) of the metrics rather than
the values of the metrics, which reduces the number of fuzzy rules
dramatically. Moreover, the number of fuzzy rules does not increase when the
number of inputs increases. For reducing the control cost, in CBRT, the node
degree in the candidate relays set is a range rather than a constant number.
The nodes are divided into different categories based on their node degree in
the candidate relays set. The nodes adjust their transmission range based on
which categories that they belong to. Additionally, for investigating the
effection of the node mobility on routing performance, we propose a link
lifetime prediction algorithm which takes both the moving speed and moving
direction into account. In CBRT, the source node determines the relaying
priorities of the relaying nodes based on their utilities. The relaying node
which the utility is large will have high priority to relay the data packet. By
these innovations, the network performance in CBRT is much better than that in
ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201
Collective navigation of complex networks: Participatory greedy routing
Many networks are used to transfer information or goods, in other words, they
are navigated. The larger the network, the more difficult it is to navigate
efficiently. Indeed, information routing in the Internet faces serious
scalability problems due to its rapid growth, recently accelerated by the rise
of the Internet of Things. Large networks like the Internet can be navigated
efficiently if nodes, or agents, actively forward information based on hidden
maps underlying these systems. However, in reality most agents will deny to
forward messages, which has a cost, and navigation is impossible. Can we design
appropriate incentives that lead to participation and global navigability?
Here, we present an evolutionary game where agents share the value generated by
successful delivery of information or goods. We show that global navigability
can emerge, but its complete breakdown is possible as well. Furthermore, we
show that the system tends to self-organize into local clusters of agents who
participate in the navigation. This organizational principle can be exploited
to favor the emergence of global navigability in the system.Comment: Supplementary Information and Videos:
https://koljakleineberg.wordpress.com/2016/11/14/collective-navigation-of-complex-networks-participatory-greedy-routing
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