arXiv subjects

Andrea Zanella

Publications and source records attributed to Andrea Zanella.

At least 55 records · Page 3Linked to original sources

arXiv2022

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR's interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

cs.NI↗

arXiv2021

No Free Lunch: Balancing Learning and Exploitation at the Network Edge

Over the last few years, the DRL paradigm has been widely adopted for 5G and beyond network optimization because of its extreme adaptability to many different scenarios. However, collecting and processing learning data entail a significant cost in terms of communication and computational resources, which is often disregarded in the networking literature. In this work, we analyze the cost of learning in a resource-constrained system, defining an optimization problem in which training a DRL agent makes it possible to improve the resource allocation strategy but also reduces the number of available resources. Our simulation results show that the cost of learning can be critical when evaluating DRL schemes on the network edge and that assuming a cost-free learning model can lead to significantly overestimating performance.

cs.NI↗

arXiv2021

Energy-Efficient Design for RIS-assisted UAVcommunications in beyond-5G Networks

The usage of Reconfigurable Intelligent Surfaces (RIS) in conjunction with Unmanned Ariel Vehicles (UAVs) is being investigated as a way to provide energy-efficient communication to ground users in dense urban areas. In this paper, we devise an optimization scenario to reduce overall energy consumption in the network while guaranteeing certain Quality of Service (QoS) to the ground users in the area. Due to the complex nature of the optimization problem, we provide a joint UAV trajectory and RIS phase decision to minimize transmission power of the UAV and Base Station (BS) that yields good performance with lower complexity. So, the proposed method uses a Successive Convex Approximation (SCA) to iteratively determine a joint optimal solution for UAV Trajectory, RIS phase and BS and UAV Transmission Power. The approach has, therefore, been analytically evaluated under different sets of criterion.

eess.SP↗

arXiv2021

An Open Framework for Analyzing and Modeling XR Network Traffic

Thanks to recent advancements in the technology, eXtended Reality (XR) applications are gaining a lot of momentum, and they will surely become increasingly popular in the next decade. These new applications, however, require a step forward also in terms of models to simulate and analyze this type of traffic sources in modern communication networks, in order to guarantee to the users state of the art performance and Quality of Experience (QoE). Recognizing this need, in this work, we present a novel open-source traffic model, which researchers can use as a starting point both for improvements of the model itself and for the design of optimized algorithms for the transmission of these peculiar data flows. Along with the mathematical model and the code, we also share with the community the traces that we gathered for our study, collected from freely available applications such as Minecraft VR, Google Earth VR, and Virus Popper. Finally, we propose a roadmap for the construction of an end-to-end framework that fills this gap in the current state of the art.

cs.NI↗

arXiv2021

A Configurable Mathematical Model for Single-Gateway LoRaWAN Performance Analysis

LoRaWAN is a Low Power Wide Area Network technology featuring long transmission ranges and a simple MAC layer, which can support sensor data collection, control applications and reliable services thanks to the flexibility offered by a large set of configurable system parameters. However, the impact of such parameters settings on the system's performance is often difficult to predict, depending on several factors. To ease this task, in this paper, we provide a mathematical model to estimate the performance of a LoRaWAN gateway serving a set of devices that may or may not employ confirmed traffic. The model features a set of parameters that can be adjusted to investigate different gateway and end-device configurations, making it possible to carry out a systematic analysis of various trade-offs. The results given by the proposed model are validated through realistic ns-3 simulations that confirm the ability of the model to predict the system performance with high accuracy, and assess the impact of the assumptions made in the model for tractability.

cs.NI↗

arXiv2021

Proof of convergence of LoRaWAN model

In this document, we prove the convergence of the model proposed in [1], which aims at estimating the LoRaWAN network performance in a single-gateway scenario. First, we provide an analytical proof of the existence of a fixed point solution for such a system. Then, we report experimental results, showing that the system of the two inter-dependent equations provided by the model can be solved through fixed-point iterations, and that a limited number of iterations is enough to reach convergence.

cs.NI↗

arXiv2021

A Simulation Framework for Contention-Free Scheduling on WiGig

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum and, thanks to the wide bandwidth available at such frequencies, makes it possible to wirelessly approach several emergent use cases, such as virtual and augmented reality, telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service, that only contention-free scheduling algorithms can guarantee. In this paper, we propose an end-to-end framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3, a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and we evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the differences and trade-offs between contention-based and contention-free access strategies.

cs.NI↗

arXiv2021

Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario

The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slices' requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agents' training is carried out following the Advantage Actor Critic algorithm, which allows to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.

cs.MA↗

arXiv2021

An ns-3 Implementation of a Bursty Traffic Framework for Virtual Reality Sources

Next-generation wireless communication technologies will allow users to obtain unprecedented performance, paving the way to new and immersive applications. A prominent application requiring high data rates and low communication delay is Virtual Reality (VR), whose presence will become increasingly stronger in the years to come. To the best of our knowledge, we propose the first traffic model for VR applications based on traffic traces acquired from a commercial VR streaming software, allowing the community to further study and improve the technology to manage this type of traffic. This work implements ns-3 applications able to generate and process large bursts of packets, enabling the possibility of analyzing APP-level end-to-end metrics, making the source code as well as the acquired VR traffic traces publicly available and open-source.

cs.NI↗

arXiv2021

An ns-3 implementation of a battery-less node for energy-harvesting Internet of Things

In the Internet of Things (IoT), thousands of devices can be deployed to acquire data from the environment and provide service to several applications in different fields. In many cases, it is desirable that devices are self-sustainable in terms of energy. Therefore,the research community is exploring the possibility of employing battery-less devices, where the energy is derived solely from external and/or environmental sources, such as solar panels. In this work, we propose an ns-3 model of a (super) capacitor, which can be used as the storage of the harvested energy in a battery-less IoT device, and add the support for the intermittent behavior of devices, turning off/on according to their energy level. To exemplify the use of the model, we apply it to a LoRaWAN node, and compare the simulation outcomes with results in the literature obtained with mathematical analysis, confirming the accuracy of the implementation. Then, we show the importance of analyzing the interaction between energy availability and communication performance, paving the way for more accurate and realistic simulations in the field. The implemented code is made available as open source.

cs.NI↗

arXiv2021

Exploiting Scheduled Access Features of mmWave WLANs for Periodic Traffic Sources

Many current and future multimedia and industrial applications, like video streaming, eXtended Reality or remote robot control, are characterized by periodic data transmissions with strict latency and reliability constraints. In an effort to meet the stringent demand of such traffic sources, the WiGig standards support a contention-free channel access mechanism, named Service Period, that makes it possible to allocate dedicated time intervals to certain wireless stations. However, the standard only covers the fundamental aspects that ensure interoperability, while the actual schedule logic is left to vendors. In this paper, we propose two algorithms for joint admission control and scheduling of periodic traffic streams with contrasting performance objectives, specifically a simple scheduler and a max-min fair scheduler. The schemes are compared in two different scenarios, in order to characterize and highlight some fundamental trade-offs. As expected from their design principles, the simple scheduler tends to trade acceptance rate for resource availability, contrary to the max-min fair scheduler, giving to implementers a clear performance trade-off, although performance cannot be balanced by means of a tunable parameter.

cs.NI↗

arXiv2021

Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm's actions. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments, and that can easily deal with non-uniform distributions of targets and obstacles. Moreover, when agents are trained in a specific scenario, they can adapt to a new one with minimal additional training. We also show that our approach achieves better performance compared to a computationally intensive look-ahead heuristic.

cs.LG↗

arXiv2020

QUIC-EST: A Transmission Scheme to Maximize VoI of Multi-Stream Correlated Data Flows

New advanced applications, such as autonomous driving and haptic communication, require to transmit multi-sensory data and require low latency and high reliability. These applications include. Existing implementations for such services have mostly relied on ad hoc scheduling and send rate adaptation mechanisms, implemented directly by the application and running over UDP. In this work, we propose a transmission scheme that relies on the features of the recently developed QUIC transport protocol, providing reliability where needed, and standardized congestion control, without compromising latency. Furthermore, we propose a scheduler for sensor data transmissions on the transport layer that can exploit the correlations over time and across sensors. This mechanism allows applications to maximize the Value of Information (VoI) of the transmitted data, as we demonstrate through simulations in two realistic application scenarios.

cs.NI↗

arXiv2020

The challenges of Scheduling and Resource Allocation in IEEE 802.11ad/ay

The IEEE 802.11ad WiFi amendment enables short-range multi-gigabit communications in the unlicensed 60~GHz spectrum, unlocking new interesting applications such as wireless Augmented and Virtual Reality. The characteristics of the mmWave band and directional communications allow increasing the system throughput by scheduling pairs of nodes with low cross-interfering channels in the same time-frequency slot. On the other hand, this requires significantly more signaling overhead. Furthermore, IEEE 802.11ad introduces a hybrid MAC characterized by two different channel access mechanisms: contention-based and contention-free access periods. The coexistence of both access period types and the directionality typical of mmWave increase the channel access and scheduling complexity in IEEE 802.11ad compared to previous WiFi versions. Hence, to provide the Quality of Service (QoS) performance required by demanding applications, a proper resource scheduling mechanism that takes into account both directional communications and the newly added features of this WiFi amendment is needed. In this paper, we present a brief but comprehensive review of the open problems and challenges associated with channel access in IEEE 802.11ad and propose a workflow to tackle them via both heuristic and learning-based methods.

cs.NI↗

arXiv2020

Internet of Things for Elderly and Fragile People

This paper discusses the potential of the Internet of Things (IoT) paradigm in the context of assisted living for elderly and fragile people, in the light of the peculiar requirements of such users, both from a functional and a technological perspective. We stress some aspects that are often disregarded by the technical community, such as technology acceptability and usability, and we describe the framework and the phases of the current co-design approaches that imply the active involvement of the final users in the system design process. Thereby, we identify a series of design practices to merge technical and fragile people's requirements. The discussion is backed up by the description of DOMHO, a prototypal IoT-based AAL system that embodies most of the concepts described in the paper, and that is being deployed and tested in a shelter house for elders, and in an apartment for the co-housing of individuals with disabilities. Finally, we discuss the potential and limits of the current approaches and present some open challenges and future research directions.

cs.CY↗

arXiv2020

Feature selection for gesture recognition in Internet-of-Things for healthcare

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance.

cs.CV↗

arXiv2020

Dissecting Energy Consumption of NB-IoT Devices Empirically

3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy efficient connectivity option to the rapidly expanding market of Internet of Things (IoT) devices. To unleash its full potential, end-devices are expected to work in a plug and play fashion, with zero or minimal parameters configuration, still exhibiting excellent energy efficiency. We perform the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our campaign proves that parameters setting does impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Further, we investigate which device reported metadata metrics better reflect performance and implement an algorithm to automatically identify device state in current time series logs. Then, we provide a measurement-driven analysis of the energy consumption and network performance of two popular NB-IoT boards under different parameter configurations and with two major western European operators. We observed that energy consumption is mostly affected by the paging interval in Connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters settings can yield great energy savings.

cs.NI↗

arXiv2020

Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches.

eess.SY↗