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Presence of a logically centralized controller in software-defined networks enables smart and fine-grained management of network traffic. Generally, traffic management includes measurement, analysis and control of traffic in order to…
The goal of this work is to describe a self-management system that correlates data sensed by different Wireless Sensor Networks (WSNs) and adjusts the number of active nodes in each network to provide an appropriate amount of measurements.…
Wireless sensor networks (WSNs) have been adopted as merely data producers for years. However, the data collected by WSNs can also be used to manage their operation and avoid unnecessary measurements that do not provide any new knowledge…
This article introduces a control-oriented low-altitude wireless network (LAWN) that integrates near-ground communications and remote estimation of the internal system state. This integration supports reliable networked control in dynamic…
This paper aims to propose a significant way of remote access and real time monitoring of a particular geographic area by integrating wireless sensor clouds with existing Telecom infrastructure and applications built around them through a…
The recent apparition of mobile wireless sensor aware to their physical environment and able to process information must allow proposing applications able to take into account their physical context and to react according to the changes of…
Software Defined Networks has seen tremendous growth and deployment in different types of networks. Compared to traditional networks it decouples the control logic from network layer devices, and centralizes it for efficient traffic…
Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in…
Wireless Local Area Networks (WLANs), known as Wi-Fi, have become an essential service in university environments that helps staff, students and guests to access connectivity to the Internet from their mobile devices. Apart from the…
In wireless communications, transforming network into graphs and processing them using deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream network optimization approaches. While effective, the generative AI…
Software Defined Networking (SDN) has gained huge popularity in replacing traditional network by offering flexible and dynamic network management. It has drawn significant attention of the researchers from both academia and industries.…
The exponential growth in mobile broadband usage [1] has catalyzed the need for high data rate communication systems. In this regard, activities for standardizing the next generation mobile broadband system, known as the Fifth…
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about…
This paper presents an approach to integrate wireless sensor networks (WSN) with the LabVIEW graphical development environment through a dedicated software driver. As personal constribution, a system architecture and concept implementation…
In highly-dense IEEE 802.11 deployments, areas covered by multiple Wireless Local Area Networks (WLANs) will be common. This opens the door for stations equipped with multiple IEEE 802.11 interfaces to use several WLANs simultaneously,…
This paper aims to propose a significant way of remote access and real time monitoring of a particular geographic area by integrating wireless sensor clouds with existing Telecom infrastructure and applications built around them through a…
The dramatic advances in wireless communications and electronics have enabled the development of Wireless Sensor Networks (WSNs). WSNs consist of many affordable and portable sensor nodes for collecting data from the environment. In this…
Advances in the price, performance, and power consumption of Wi-Fi (IEEE 802.11) technology have led to the adoption of wireless functionality in diverse consumer electronics. These trends have enabled an exciting vision of rich wireless…
In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well…
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of…