38,593 research outputs found
Online machine learning algorithms to predict link quality in community wireless mesh networks
Producción CientíficaAccurate link quality predictions are key in community wireless mesh networks (CWMNs) to improve the performance of routing protocols. Unlike other techniques, online machine learning algorithms can be used to build link quality predictors that are adaptive without requiring a predeployment effort. However, the use of these algorithms to make link quality predictions in a CWMN has not been previously explored. This paper analyses the performance of 4 well-known online machine learning algorithms for link quality prediction in a CWMN in terms of accuracy and computational load. Based on this study, a new hybrid online algorithm for link quality prediction is proposed. The evaluation of the proposed algorithm using data from a real large scale CWMN shows that it can achieve a high accuracy while generating a low computational load.Ministerio de Economía, Industria y Competitividad (Project TIN2014-53199-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16
Reliable Prediction of Channel Assignment Performance in Wireless Mesh Networks
The advancements in wireless mesh networks (WMN), and the surge in
multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of
network performance issues. These issues are intricately linked to the adverse
impact of endemic interference. Thus, interference mitigation is a primary
design objective in WMNs. Interference alleviation is often effected through
efficient channel allocation (CA) schemes which fully utilize the potential of
MRMC environment and also restrain the detrimental impact of interference.
However, numerous CA schemes have been proposed in research literature and
there is a lack of CA performance prediction techniques which could assist in
choosing a suitable CA for a given WMN. In this work, we propose a reliable
interference estimation and CA performance prediction approach. We demonstrate
its efficacy by substantiating the CA performance predictions for a given WMN
with experimental data obtained through rigorous simulations on an ns-3 802.11g
environment.Comment: Accepted in ICACCI-201
Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation
Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on
wired infrastructure especially with the advent of the multi-radio
multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer
viz., augmented network capacity, uninterrupted connectivity and reduced
latency, are depreciated by the detrimental effect of prevalent interference.
Interference mitigation is thus a prime objective in WMN deployments. It is
often accomplished through prudent channel allocation (CA) schemes which
minimize the adverse impact of interference and enhance the network
performance. However, a multitude of CA schemes have been proposed in research
literature and absence of a CA performance prediction metric, which could aid
in the selection of an efficient CA scheme for a given WMN, is often felt. In
this work, we offer a fresh characterization of the interference endemic in
wireless networks. We then propose a reliable CA performance prediction metric,
which employs a statistical interference estimation approach. We carry out a
rigorous quantitative assessment of the proposed metric by validating its CA
performance predictions with experimental results, recorded from extensive
simulations run on an ns-3 802.11g environment
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
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