Data-Mining-Based Link Failure Detection for Wireless Mesh Networks

Lindhorst, Timo; Lukas, Georg; Nett, Edgar; Mock, Michael

doi:10.1109/srds.2010.51

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…This system will incorporate other ongoing research works within our working group [30,31] developing concepts for end-to-end quality of service guarantees (throughput, packet loss, latency) in Wireless Mesh Networks. Another aspect of our future work is to integrate the developed concepts in components for industrial wireless communication in cooperation with german product manufacturers.…”

Section: Resultsmentioning

confidence: 99%

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Achieving Fault-Tolerant Network Topology in Wireless Mesh Networks

Ivanov¹,

Nett²

2012

Wireless Mesh Networks - Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

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Additional information is available at the end of the chapter http://dx.doi.org/10.5772/50173 IntroductionWireless Mesh Network (WMN) is an ad-hoc network with a fixed network infrastructure (see an example in figure 1). The physical structure of a WMN includes base stations, a backbone and mobile stations. The base stations (also known as mesh routers or mesh points) are static wireless nodes, forming the network infrastructure and providing wireless network access to the mobile stations. The backbone is a wireless ad-hoc network among the base stations. The fixed network infrastructure provides wireless network access to the mobile stations in a service area. Service area is a finite three-dimensional space. The mobile stations are wireless We consider a wireless mesh network which supports a business process and is under the administration of an organization. This is not a MANET (Mobile Ad-hoc Network) consisting of self-dependent mobile nodes, like it is often in the literature. The organization has control over the network infrastructure and aims at providing radio coverage and connectivity in a clearly defined service area. The management appliance is a central instance for basic configuration and diagnosis of the WMN, including topology monitoring, protocol settings, traffic management, etc.Radio coverage and connectivity are basic services of a wireless mesh network which are required for communication. Radio coverage ensures that the mobile stations can access the network infrastructure (backbone) while they are located or moving in the service area. Connectivity ensures that the topology of the backbone is connected. Radio coverageThe service radio coverage is correct, if the service area is covered by the base stations. The service area is covered, if the unification of radio cells of all base stations contains the whole service area. The radio cell of a base station is a part of the space around it, in which a mobile station observes the base station with a radio signal strength sufficient for communication.The sufficient radio signal strength in the service area is a basic requirement for the mobile stations to be able to access the WMN. The radio coverage service ensures this sufficient signal strength in the service area. Service location is a point of the service area, specified by its coordinates. A service location is covered, if the unification of radio cells of all base stations contains the service location. ConnectivityThe service connectivity is correct, if the backbone graph is connected. The backbone graph is a graph with the base stations as vertices and the routing layer links among them as edges. A link exists if two wireless devices can communicate through the wireless medium obeying some qualitative parameters (see section 4.3 for more information). The backbone graph represents the network topology at the routing layer. This graph is connected, if a path (a sequence of edges) exists between every two vertices. A connected backbone graph means a connected routing layer topology whic...

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Section: Resultsmentioning

confidence: 99%

“…For the routing protocol and the topology discovery we base on the research within our working group (e.g. [15,29,32]). …”

Section: Connectivity and Base Station Planningmentioning

confidence: 99%

Achieving Fault-Tolerant Network Topology in Wireless Mesh Networks

Ivanov¹,

Nett²

2012

Wireless Mesh Networks - Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

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“…The significance of detecting the destination was felt in the work reported by Sarigiannidis et al [2], wherein they devised a rule-based system to detect Sybil attack, where the detection paradigm was to locate two or more nodes in the same area of a wireless sensor network. Lindhorst et al [3] attempted to accurately predict the failure of links in wireless mesh networks using the data mining techniques. They attempted to reduce the prediction latency by monitoring the parameters on the wireless local area network's media access control layer.…”

Section: A Fault Tolerance Link Quality and Reliabilitymentioning

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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“…In [32], the authors analyzed the possibility of using "hello messages" to quickly detect failure in a mesh network. The paper [33] proposed how to accelerate the failure detection in MESH network nodes using data-mining-based link failure detection and using the MAC layer to predict connection failures. The literature also includes the use of a number of other mechanisms for detecting MESH network damage, such as Cooperative Watchdog [34], Software Defined Networks (SDN) [35], and the use of redundant elements [28].…”

Section: Introductionmentioning

confidence: 99%

Phase Transitions in Wireless MESH Networks and Their Application in Early Detection of Network Coherence Loss

Paszkiewicz¹,

Bolanowski²,

Zapała³

2019

Applied Sciences

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The paper focuses on the phenomenon of phase transitions related to changes taking place directly in a network structure; i.e., in physical topology. Although these changes are related to the physical layer, they directly affect the functioning of higher layers, and thus the quality and efficiency of network transmission. The research presented herein was focused in particular on phase transformations in MESH type wireless topologies. The dependence between the values of radius (transmitter power) nodes and the coherence of the MESH network is presented. These considerations are important for understanding the dynamics of processes occurring in topological structures, and for optimizing the use of power sources for individual nodes. The paper also presents the use of phase transition analysis for early detection of network coherence loss at the physical layer level. A new mechanism for assessing the level of network communication and an active method of preventing its failure using the arbiter and a set of limited diagnostic information are described. The solution presented can be used both in Internet of Things (IoT) systems and Industry 4.0 to create a high level of reliability wireless connection infrastructure between mobile and fixed devices.

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Data-Mining-Based Link Failure Detection for Wireless Mesh Networks

Cited by 8 publications

References 9 publications

Achieving Fault-Tolerant Network Topology in Wireless Mesh Networks

Achieving Fault-Tolerant Network Topology in Wireless Mesh Networks

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

Phase Transitions in Wireless MESH Networks and Their Application in Early Detection of Network Coherence Loss

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