scholarly journals Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Author(s):  
Tanwi Mallick ◽  
Mariam Kiran ◽  
Bashir Mohammed ◽  
Prasanna Balaprakash
2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.


Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Eljona Zanaj ◽  
Giuseppe Caso ◽  
Luca De Nardis ◽  
Alireza Mohammadpour ◽  
Özgü Alay ◽  
...  

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions.


Author(s):  
Rocco De Nicola ◽  
Michele Loreti

A new area of research, known as Global Computing, is by now well established. It aims at defining new models of computation based on code and data mobility over wide-area networks with highly dynamic topologies, and at providing infrastructures to support coordination and control of components originating from different, possibly untrusted, fault-prone, malicious or selfish sources. In this paper, we present our contribution to the field of Global Computing that is centred on Kernel Language for Agents Interaction and Mobility ( Klaim ). Klaim is an experimental language specifically designed to programme distributed systems consisting of several mobile components that interact through multiple distributed tuple spaces. We present some of the key notions of the language and discuss how its formal semantics can be exploited to reason about qualitative and quantitative aspects of the specified systems.


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