607,055 research outputs found
Self-management of context-aware overlay ambient networks
Ambient Networks (ANs) are dynamically changing and heterogeneous as they consist of potentially large numbers of independent, heterogeneous mobile nodes, with spontaneous topologies that can logically interact with each other to share a common control space, known as the Ambient Control Space. ANs are also flexible i.e. they can compose and decompose dynamically and automatically, for supporting the deployment of cross-domain (new) services. Thus, the AN architecture must be sophisticatedly designed to support such high level of dynamicity, heterogeneity and flexibility. We advocate the use of service specific overlay networks in ANs, that are created on-demand according to specific service requirements, to deliver, and to automatically adapt services to the dynamically changing user and network context. This paper presents a self-management approach to create, configure, adapt, contextualise, and finally teardown service specific overlay networks
Context-Aware Self-Attention Networks
Self-attention model have shown its flexibility in parallel computation and
the effectiveness on modeling both long- and short-term dependencies. However,
it calculates the dependencies between representations without considering the
contextual information, which have proven useful for modeling dependencies
among neural representations in various natural language tasks. In this work,
we focus on improving self-attention networks through capturing the richness of
context. To maintain the simplicity and flexibility of the self-attention
networks, we propose to contextualize the transformations of the query and key
layers, which are used to calculates the relevance between elements.
Specifically, we leverage the internal representations that embed both global
and deep contexts, thus avoid relying on external resources. Experimental
results on WMT14 English-German and WMT17 Chinese-English translation tasks
demonstrate the effectiveness and universality of the proposed methods.
Furthermore, we conducted extensive analyses to quantity how the context
vectors participate in the self-attention model.Comment: AAAI 201
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Power aware routing algorithms (PARA) in wireless mesh networks for emergency management
Wireless Mesh Networks (WMNs) integrate the advantages of WLANs and mobile Ad Hoc networks, which have become the key techniques of next-generation wireless networks in the context of emergency recovery. Wireless Mesh Networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. These capabilities make WMNs a promising technology for incident and emergency communication. An incident area network (IAN) needs a reliable and lively routing path during disaster recovery and emergency response operations when infrastructure-based communications and power resources have been destroyed and no routes are available. Power aware routing plays a significant role in WMNs, in order to provide continuous efficient emergency services. The existing power aware routing algorithms used in wireless networks cannot fully fit the characteristics of WMNs, to be used for emergency recovery. This paper proposes a power aware routing algorithm (PARA) for WMNs, which selects optimal paths to send packets, mainly based on the power level of next node along the path. This algorithm was implemented and tested in a proven simulator. The analytic results show that the proposed power node-type aware routing algorithm metric can clearly improve the network performance by reducing the network overheads and maintaining a high delivery ratio with low latency
CAM04-1: Admission control in self aware networks
The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing
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