Sector-Based Routing with Destination Location Prediction for Underwater Mobile Networks

Chirdchoo, Nitthita; Soh, Wee-Seng; Chua, Kee Chaing

doi:10.1109/waina.2009.105

Cited by 75 publications

(47 citation statements)

References 12 publications

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“…This protocol consumes less energy and significantly improves the robustness of packet delivery in sparse networks. Chirdchoo et al (2009) propose a Sector-Based Routing with Destination Location Prediction (SBR-DLP). They assume that each sensor node knows its own location, and predicts locations of destination nodes, therefore, relaxing the need for precise knowledge of the destinations' locations.…”

Section: Geographic Protocolsmentioning

confidence: 99%

Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks

Jiang

Han

Guo

et al. 2016

Journal of Network and Computer Applications

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Section: Geographic Protocolsmentioning

confidence: 99%

Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks

Jiang

Han

Guo

et al. 2016

Journal of Network and Computer Applications

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“…Machine learning was used in variety of network related applications [5]- [8]. For an effective network support, classifiers was used for network application identification [9], end-to-end latency prediction [10], and network route determination [11], [12] in wired, ad hoc [13], as well as underwater networks [14].…”

Section: B Resource and Service Management For Cpssmentioning

confidence: 99%

Multivariate Data Fusion-Based Learning of Video Content and Service Distribution for Cyber Physical Social Systems

Misra

Goswami

Taneja

2016

IEEE Trans. Comput. Soc. Syst.

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Integration of physical processes with the computing world is driving newer challenges for networking frameworks. Cyber physical social systems (CPSSs) are another upcoming paradigm that encompasses the ever-growing interaction between the physical, social, and cyber worlds. As communication networks form the basis of these interactions, a cognitive evaluation of networks is called for. This CPSS driven network evolution was a direction motivating this paper. With the implementation of the next generation networks, traffic from real-time interactive services, such as video conferencing, is surpassing those of conventional transactional services. As such multimedia data transportation over IP networks has stringent quality constraints in terms of required bandwidth, latency, and jitter, legacy networks with no quality of service face challenges in terms of performance. We attempt to perform a multivariate analysis of video call record data collected from a wide area organizational network over a period of time. Learning-based prediction is attempted by training four classifiers: naïve Bayes, k-nearest neighbor, decision tree, and support vector machine. Two independent set of experiments were conducted with oversights of bandwidth and destination prediction. Both the discrete and continuous valued predictors were involved in the training. Performance evaluation of the generated hypothesis in both the cases was conducted using tenfold cross validation. Combined analysis using the assorted combinations of attributes was conducted, and thereafter, the effect of each feature was evaluated through singular attribute portioning. This paper presents observations, which exhibit deviations from the conventional machine learning paradigms. An attempt to increase the prediction accuracy of the classifiers was made through the boosting ensemble methodology. However, miniscule addition in performance was achieved. A maximum prediction accuracy of 81% for bandwidth and 60% for destination was obtained. Reasons of low accuracy of conventionally better performing algorithm were reasoned with a mathematical comprehension. Divergence of the obtained results from the accepted patterns poses an open research problem, particularly with respect to the nature and peculiarities of the data set. The proposed learning technique can have potential applications in social, tactical, and strategic spheres.Index Terms-Classifier, cyber physical social system (CPSS), data fusion, decision tree, k-nearest neighbor (k-NN), machine learning, naïve Bayes, service distribution, stylometrics, support vector machine (SVM), video conferencing.

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“…The SBR-DLP [23] protocol routes data packets in a fully mobile underwater acoustic network. It is a location-based protocol where the nodes do not need to carry neighbor or topology information.…”

Section: B Routing For Underwater Acoustic Networkmentioning

confidence: 99%

Routing challenges and solutions for underwater networks

Rahman

Benson

Frater

2012

2012 Military Communications and Information Systems Conference (MilCIS)

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Routing is one of the fundamental concerns in any communication network. Over the last two decades, a significant amount of attention has been given to the design of terrestrial routing protocols. However, the distinctive properties of the underwater communication channel -such as distancedependent bandwidth, large propagation delay and time-varying mulitpath -render the existing terrestrial routing protocols implausible for underwater networks. In this paper we explore the challenges in developing a suitable routing protocol for underwater networks. We also look at existing routing protocols designed for both terrestrial and underwater networks, identify their shortcomings and finally lay out some guidelines for the design of routing protocols suitable for next generation underwater networks/applications.

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Sector-Based Routing with Destination Location Prediction for Underwater Mobile Networks

Cited by 75 publications

References 12 publications

Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks

Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks

Multivariate Data Fusion-Based Learning of Video Content and Service Distribution for Cyber Physical Social Systems

Routing challenges and solutions for underwater networks

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