83,298 research outputs found
Improving Link Prediction in Intermittently Connected Wireless Networks by Considering Link and Proximity Stabilities
Several works have outlined the fact that the mobility in intermittently
connected wireless networks is strongly governed by human behaviors as they are
basically human-centered. It has been shown that the users' moves can be
correlated and that the social ties shared by the users highly impact their
mobility patterns and hence the network structure. Tracking these correlations
and measuring the strength of social ties have led us to propose an efficient
distributed tensor-based link prediction technique. In fact, we are convinced
that the feedback provided by such a prediction mechanism can enhance
communication protocols such as opportunistic routing protocols. In this paper,
we aim to bring out that measuring the stabilities of the link and the
proximity at two hops can improve the efficiency of the proposed link
prediction technique. To quantify these two parameters, we propose an entropy
estimator in order to measure the two stability aspects over successive time
periods. Then, we join these entropy estimations to the tensor-based link
prediction framework by designing new prediction metrics. To assess the
contribution of these entropy estimations in the enhancement of tensor-based
link prediction efficiency, we perform prediction on two real traces. Our
simulation results show that by exploiting the information corresponding to the
link stability and/or to the proximity stability, the performance of the
tensor-based link prediction technique is improved. Moreover, the results
attest that our proposal's ability to outperform other well-known prediction
metrics.Comment: Published in the proceedings of the 13th IEEE International Symposium
on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), San
Francisco, United States, 201
Predicting topology propagation messages in mobile ad hoc networks: The value of history
This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version
Correlations Between Human Mobility and Social Interaction Reveal General Activity Patterns
A day in the life of a person involves a broad range of activities which are
common across many people. Going beyond diurnal cycles, a central question is:
to what extent do individuals act according to patterns shared across an entire
population? Here we investigate the interplay between different activity types,
namely communication, motion, and physical proximity by analyzing data
collected from smartphones distributed among 638 individuals. We explore two
central questions: Which underlying principles govern the formation of the
activity patterns? Are the patterns specific to each individual or shared
across the entire population? We find that statistics of the entire population
allows us to successfully predict 71\% of the activity and 85\% of the
inactivity involved in communication, mobility, and physical proximity.
Surprisingly, individual level statistics only result in marginally better
predictions, indicating that a majority of activity patterns are shared across
{our sample population}. Finally, we predict short-term activity patterns using
a generalized linear model, which suggests that a simple linear description
might be sufficient to explain a wide range of actions, whether they be of
social or of physical character
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
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