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Selection of EAP-authentication methods in WLANs
IEEE 802.1X is a key part of IEEE802.11i. By employing Extensible Authentication Protocol (EAP) it supports a variety of upper layer
authentication methods each with different benefits and drawbacks. Any one of these authentication methods can be the ideal choice for a specific networking environment. The fact that IEEE 802.11i leaves the selection of the most suitable authentication method to system implementers makes the authentication framework more flexible, but on the other hand leads to the
question of how to select the authentication method that suits an organisationâs requirements and specific networking environment. This paper gives an overview of EAP authentication methods and provides a table comparing their properties. It then identifies the crucial factors to be considered when employing EAP authentication methods in WLAN environments. The paper presents algorithms that guide the selection of an EAP-authentication method for a WLAN and demonstrates their application through three examples
An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking
Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform
The role of circumstance monitoring on the diagnostic interpretation of condition monitoring data
Circumstance monitoring, a recently coined termed defines the collection of data reflecting the real network working environment of in-service equipment. This ideally complete data set should reflect the elements of the electrical, mechanical, thermal, chemical and environmental stress factors present on the network. This must be distinguished from condition monitoring, which is the collection of data reflecting the status of in-service equipment. This contribution investigates the significance of considering circumstance monitoring on diagnostic interpretation of condition monitoring data. Electrical treeing partial discharge activity from various harmonic polluted waveforms have been recorded and subjected to a series of machine learning techniques. The outcome provides a platform for improved interpretation of the harmonic influenced partial discharge patterns. The main conclusion of this exercise suggests that any diagnostic interpretation is dependent on the immunity of condition monitoring measurements to the stress factors influencing the operational conditions. This enables the asset manager to have an improved holistic view of an asset's health
Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing
Theories of knowledge reuse posit two distinct processes: reuse for
replication and reuse for innovation. We identify another distinct process,
reuse for customization. Reuse for customization is a process in which
designers manipulate the parameters of metamodels to produce models that
fulfill their personal needs. We test hypotheses about reuse for customization
in Thingiverse, a community of designers that shares files for
three-dimensional printing. 3D metamodels are reused more often than the 3D
models they generate. The reuse of metamodels is amplified when the metamodels
are created by designers with greater community experience. Metamodels make the
community's design knowledge available for reuse for customization-or further
extension of the metamodels, a kind of reuse for innovation
Enhancements to Secure Bootstrapping of Smart Appliances
In recent times, there has been a proliferation of smart IoT devices that make our everyday life more convenient, both at home and at work environment. Most of these smart devices are connected to cloud-based online services, and they typically reuse the existing Wi-Fi network infrastructure for Internet connectivity. Hence, it is of paramount importance to ensure that these devices establish a robust security association with the Wi-Fi networks and cloud-based servers. The initial process by which a device establishes a robust security association with the network and servers is known as secure bootstrapping. The bootstrapping process results in the derivation of security keys and other connection parameters required by the security associations. Since the smart IoT devices often possess minimal user-interface, there is a need for bootstrapping methods with which the users can effortlessly connect their smart IoT devices to the networks and services. Nimble out-of-band authentication for Extensible Authentication Protocol (EAP-NOOB) is one such secure bootstrapping method. It is a new EAP authentication method for IEEE 802.1X/EAP authentication framework. The protocol does not assume or require any pre-configured authentication credentials such as symmetric keys or certificates. In lieu, the authentication credentials along with the userâs ownership of the device are established during the bootstrapping process.
The primary goal of this thesis is to study and implement the draft specification of the EAP-NOOB protocol in order to evaluate the working of EAP-NOOB in real-world scenarios. During our implementation and testing of the initial prototype for EAP-NOOB, we discovered several issues in the protocol. In this thesis, we propose a suitable solution for each of the problems identified and also, verify the solutions through implementation and testing. The main results of this thesis work are various enhancements and clarifications to the EAP-NOOB protocol specification. The results consequently aid the standardisation of the protocol at IETF. We also design and implement several additional features for EAP-NOOB to enhance the user experience
Change Point Methods on a Sequence of Graphs
Given a finite sequence of graphs, e.g., coming from technological,
biological, and social networks, the paper proposes a methodology to identify
possible changes in stationarity in the stochastic process generating the
graphs. In order to cover a large class of applications, we consider the
general family of attributed graphs where both topology (number of vertexes and
edge configuration) and related attributes are allowed to change also in the
stationary case. Novel Change Point Methods (CPMs) are proposed, that (i) map
graphs into a vector domain; (ii) apply a suitable statistical test in the
vector space; (iii) detect the change --if any-- according to a confidence
level and provide an estimate for its time occurrence. Two specific
multivariate CPMs have been designed: one that detects shifts in the
distribution mean, the other addressing generic changes affecting the
distribution. We ground our proposal with theoretical results showing how to
relate the inference attained in the numerical vector space to the graph
domain, and vice versa. We also show how to extend the methodology for handling
multiple change points in the same sequence. Finally, the proposed CPMs have
been validated on real data sets coming from epileptic-seizure detection
problems and on labeled data sets for graph classification. Results show the
effectiveness of what proposed in relevant application scenarios
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.Comment: Proceedings of the 27th International Joint Conference on Artificial
Intelligenc
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