Search results for " Computer"
showing 10 items of 6910 documents
Designing the 5G network infrastructure: a flexible and reconfigurable architecture based on context and content information
2018
5G networks will have to offer extremely high volumes of content, compared to those of today’s. Moreover, they will have to support heterogeneous traffics, including machine-to-machine, generated by a massive volume of Internet-of-Things devices. Traffic demands will be variable in time and space. In this work, we argue that all this can be achieved in a cost-effective way if the network is flexible and reconfigurable. We present the Flex5Gware network architecture, designed to meet the above requirements. Moreover, we discuss the links between flexibility and reconfigurability, on the one side, and context awareness and content awareness, on the other; we show how two of the building…
On the stability of some controlled Markov chains and its applications to stochastic approximation with Markovian dynamic
2015
We develop a practical approach to establish the stability, that is, the recurrence in a given set, of a large class of controlled Markov chains. These processes arise in various areas of applied science and encompass important numerical methods. We show in particular how individual Lyapunov functions and associated drift conditions for the parametrized family of Markov transition probabilities and the parameter update can be combined to form Lyapunov functions for the joint process, leading to the proof of the desired stability property. Of particular interest is the fact that the approach applies even in situations where the two components of the process present a time-scale separation, w…
Coupled conditional backward sampling particle filter
2020
The conditional particle filter (CPF) is a promising algorithm for general hidden Markov model smoothing. Empirical evidence suggests that the variant of CPF with backward sampling (CBPF) performs well even with long time series. Previous theoretical results have not been able to demonstrate the improvement brought by backward sampling, whereas we provide rates showing that CBPF can remain effective with a fixed number of particles independent of the time horizon. Our result is based on analysis of a new coupling of two CBPFs, the coupled conditional backward sampling particle filter (CCBPF). We show that CCBPF has good stability properties in the sense that with fixed number of particles, …
Learning automata based energy-efficient AI hardware design for IoT applications
2020
Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low-energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit for pervasive applications. Fundamental to this circuit is systematic encodin…
Formulations and exact algorithms for the distance-constrained generalized directed rural postman problem
2017
[EN] The generalized directed rural postman problem is an arc routing problem with many interesting real-life applications, such as routing for meter reading. In this application, a vehicle with a receiver travels through a series of neighborhoods. If the vehicle gets closer than a certain distance to a meter, the receiver is able to record the gas, water, or electricity consumption. Therefore, the vehicle does not need to traverse every street, but only a few, to get close enough to each meter. We study an extension of this problem in which a fleet of vehicles is available. Given the characteristics of the mentioned application, the vehicles have no capacities but there is a maximum distan…
Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning
2022
Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across thes…
A low complexity distributed cluster based algorithm for spatial prediction
2017
Los mapas del entorno radioeléctrico (REM) pueden ser una herramienta esencial para numerosas aplicaciones en las futuras redes inalámbricas 5G. En este trabajo, empleamos un popular método geoestadístico llamado kriging ordinario para estimar el REM de un área cubierta por un eNodeB equipado con múltiples antenas. Los sensores inalámbricos se distribuyen por el área de interés y se organizan clústeres adaptativos de sensores para mejorar la calidad de la estimación del canal. En este trabajo, modificamos el algoritmo de clustering distribuido propuesto en un trabajo anterior para reducir la complejidad de la predicción de kriging. Se realizan simulaciones para detallar la técnica de formac…