arXiv subjects

Marco Levorato

Publications and source records attributed to Marco Levorato.

At least 55 records · Page 3Linked to original sources

arXiv2020

Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.

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arXiv2019

Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control

Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life.

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arXiv2019

On the Feasibility of Infrastructure Assistance to Autonomous UAV Systems

Infrastructure assistance has been proposed as a viable solution to improve the capabilities of commercial Unmanned Aerial Vehicles (UAV), especially toward fully autonomous operations. The airborne nature of these devices imposes constrains limiting the onboard available energy supply and computing power. The assistance of the surrounding communication and computing infrastructure can mitigate such limitations by extending the communication range and taking over the execution of compute-intense tasks. However, autonomous operations impose specific, and rather extreme in some cases, demands to the infrastructure. Focusing on flight assistance and task offloading to edge servers, this paper presents an in-depth evaluation of the ability of the communication infrastructure to support the necessary flow of information from the UAV to the infrastructure. The study is based on our recently proposed FlyNetSim, an open-source UAV-network simulator accurately modeling both UAV and network operations.

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arXiv2018

FlyNetSim: An Open Source Synchronized UAV Network Simulator based on ns-3 and Ardupilot

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to interconnect the UAVs with each other or with ground resources. Focusing either on the modeling of UAV operations or communication and network dynamics, available simulation tools fail to capture the complex interdependencies between these two aspects of the problem. The main contribution of this paper is a flexible and scalable open source simulator -- FlyNetSim -- bridging the two domains. The overall objective is to enable simulation and evaluation of UAV swarms operating within articulated multi-layered technological ecosystems, such as the Urban Internet of Things (IoT). To this aim, FlyNetSim interfaces two open source tools, ArduPilot and ns-3, creating individual data paths between the devices operating in the system using a publish and subscribe-based middleware. The capabilities of FlyNetSim are illustrated through several case-study scenarios including UAVs interconnecting with a multi-technology communication infrastructure and intra-swarm ad-hoc communications.

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arXiv2018

eBPF-based Content and Computation-aware Communication for Real-time Edge Computing

By placing computation resources within a one-hop wireless topology, the recent edge computing paradigm is a key enabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the data stream needs to be replicated. However, the transportation of parallel streams might not be feasible due to limitations in the capacity of the network transporting the data. To address this issue, a content and computation-aware communication control framework is proposed based on the Software Defined Network (SDN) paradigm. The framework supports multi-streaming using the extended Berkeley Packet Filter (eBPF), where the traffic flow and packet replication for each specific computation process is controlled by a program running inside an in-kernel Virtual Ma- chine (VM). The proposed framework is instantiated to address a case-study scenario where video streams from multiple cameras are transmitted to the edge processor for real-time analysis. Numerical results demonstrate the advantage of the proposed framework in terms of programmability, network bandwidth and system resource savings.

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arXiv2018

Robust Multi-Path Communications for UAVs in the Urban IoT

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of scenarios and applications. However, their deployment in urban areas poses important technical challenges. One of the most prominent concerns is the robustness of communications between the ground stations and the UAVs in a highly dynamic and crowded spectrum. Indeed, competing data streams may create local or temporary congestion impairing the ground stations to control the UAVs. The main contribution of this paper is a robust multi-path communication framework for UAV systems. The framework continuously probes the performance of multiple wireless multi-hop paths from the ground stations to each UAV, and dynamically selects the path providing the best performance to support timely control. Numerical results, based on a real-world implementation and extensive field experimentation, demonstrate the ability of the proposed framework to provide robust control against exogenous interference and network congestion.

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arXiv2017

Semantic Compression for Edge-Assisted Systems

A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within "wireless islands", where a set of sensing devices (sensors) are interconnected through one-hop wireless links to a computational resource via a local access point. The core of the proposed technique is a cooperative framework where local classifiers at the mobile nodes are dynamically crafted and updated based on the current state of the observed system, the global processing objective and the characteristics of the sensors and data streams. The edge processor plays a key role by establishing a link between content and operations within the distributed system. The local classifiers are designed to filter the data streams and provide only the needed information to the global classifier at the edge processor, thus minimizing bandwidth usage. However, the better the accuracy of these local classifiers, the larger the energy necessary to run them at the individual sensors. A formulation of the optimization problem for the dynamic construction of the classifiers under bandwidth and energy constraints is proposed and demonstrated on a synthetic example.

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arXiv2017

Energy-Based Adaptive Multiple Access in LPWAN IoT Systems with Energy Harvesting

This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. The objective is to adapt their transmission strategy to the state of the network, including the energy available to the individual nodes. In order to reduce the complexity of control, an optimization framework is proposed where energy storage dynamics are replaced by dynamic average power constraints induced by the time correlated energy supply, thus enabling lightweight and flexible network control. Specifically, the BS adapts the packet transmission probability of the "active" nodes (those currently under a favorable energy harvesting state) so as to maximize the average long-term throughput, under these dynamic average power constraints. The resulting policy takes the form of the packet transmission probability as a function of the energy harvesting state and number of active nodes. The structure of the throughput-optimal genie-aided policy, in which the number of active nodes is known non-causally at the BS, is proved. Inspired by the genie-aided policy, a Bayesian estimation approach is presented to address the case where the BS estimates the number of active nodes based on the observed network transmission pattern. It is shown that the proposed scheme outperforms by 20% a scheme in which the nodes operate based on local state information only, and performs well even when energy storage dynamics are taken into account.

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arXiv2016

Content-based Cognitive Interference Control for City Monitoring Applications in the Urban IoT

In the Urban Internet of Things devices and systems are interconnected at the city scale to provide innovative services to the citizens.However, the traffic generated by the sensing and processing systems may overload local access networks. A coexistence problem arises where concurrent applications mutually interfere and compete for available resources. This effect is further aggravated by the multiple scales involved and heterogeneity of the networks supporting the urban IoT. One of the main contributions of this paper is the introduction of the notion of content-oriented cognitive interference control in heterogeneous local access networks supporting computing and data processing in the urban IoT. A network scenario where multiple communication technologies, such as Device-to-Device and Long Term Evolution (LTE), is considered. The focus of the present paper is on city monitoring applications, where a video data stream generated by a camera system is remotely processed to detect objects. The cognitive network paradigm is extended to dynamically shape the interference pattern generated by concurrent data streams and induce a packet loss trajectory compatible with video processing algorithms. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of interfering applications for a target accuracy of the monitoring application.

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arXiv2016

Content-Based Interference Management for Video Transmission in D2D Communications Underlaying LTE

A novel interference management approach is proposed for modern communication scenarios, where multiple applications and networks coexist on the same channel resource. The leading principle behind the proposed approach is that the interference level should be adapted to the content being transmitted by the data links to maximize the amount of delivered information. A network setting is considered where Device-to-Device (D2D) communications underlay a Long Term Evolution (LTE) link uploading video content to the network infrastructure. For this scenario, an optimization problem is formulated aiming at the maximization of the D2D throughput under a constraint on the Peak Signal-to-Noise-Ratio of the video data stream. The resulting optimal policy focuses interference on specific packets within the video stream, and significantly increases the throughput achieved by the D2D link compared to an undifferentiated interference strategy. The optimal strategy is applied to a real-world video streaming application to further demonstrate the performance gain.

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arXiv2015

Modeling And Control Battery Aging in Energy Harvesting Systems

Energy storage is a fundamental component for the development of sustainable and environment-aware technologies. One of the critical challenges that needs to be overcome is preserving the State of Health (SoH) in energy harvesting systems, where bursty arrival of energy and load may severely degrade the battery. Tools from Markov process and Dynamic Programming theory are becoming an increasingly popular choice to control dynamics of these systems due to their ability to seamlessly incorporate heterogeneous components and support a wide range of applications. Mapping aging rate measures to fit within the boundaries of these tools is non-trivial. In this paper, a framework for modeling and controlling the aging rate of batteries based on Markov process theory is presented. Numerical results illustrate the tradeoff between battery degradation and task completion delay enabled by the proposed framework.

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arXiv2015

On the Effect of Fronthaul Latency on ARQ in C-RAN Systems

In the Cloud Radio Access Network (C-RAN) architecture, a Control Unit (CU) implements the baseband processing functionalities of a cluster of Base Stations (BSs), which are connected to it through a fronthaul network. This architecture enables centralized processing at the CU, and hence the implementation of enhanced interference mitigation strategies, but it also entails an increased decoding latency due to the transport on the fronthaul network. The fronthaul latency may offset the benefits of centralized processing when considering the performance of protocols at layer 2 and above. This letter studies the impact of fronthaul latency on the performance of standard Automatic Retransmission reQuest (ARQ) protocols, namely Stop and Wait, Go-Back-N and Selective Repeat. The performance of the C-RAN architecture in terms of throughput and efficiency is compared to the that of a conventional cellular system with local processing, as well as with that of a proposed hybrid C-RAN system in which BSs can perform decoding. The dynamics of the system are modeled as a multi-dimensional Markov process that includes sub-chains to capture the temporal correlation of interference and channel gains. Numerical results yield insights into the impact of system parameters such as fronthaul latency and signal-to-interference ratio on different ARQ protocols.

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arXiv2013

Active Classification for POMDPs: a Kalman-like State Estimator

The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking, estimation of sparse signals and radar scheduling.

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arXiv2013

Cognitive Access Policies under a Primary ARQ process via Forward-Backward Interference Cancellation

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques.

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arXiv2012

An Algorithmic Solution for Computing Circle Intersection Areas and its Applications to Wireless Communications

A novel iterative algorithm for the efficient computation of the intersection areas of an arbitrary number of circles is presented. The algorithm, based on a trellis-structure, hinges on two geometric results which allow the existence-check and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. The presented algorithm is a powerful tool for the performance analysis of wireless networks, and finds many applications, ranging from sensor to cellular networks. As an example of practical application, an insightful study of the uplink outage probability of in a wireless network with cooperative access points as a function of the transmission power and access point density is presented.

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arXiv2012

Cooperation in Carrier Sense Based Wireless Ad Hoc Networks - Part I: Reactive Schemes

Cooperative techniques have been shown to significantly improve the performance of wireless systems. Despite being a mature technology in single communication link scenarios, their implementation in wider, and practical, networks poses several challenges which have not been fully identified and understood so far. In this two-part paper, the implementation of cooperative communications in non-centralized ad hoc networks with sensing-based channel access is extensively discussed. Both analysis and simulation are employed to provide a clear understanding of the mutual influence between the link layer contention mechanism and collaborative protocols. Part I of this work focuses on reactive cooperation, in which relaying is triggered by packet delivery failure events, while Part II addresses proactive approaches, preemptively initiated by the source based on channel state information. Results show that sensing-based channel access significantly hampers the effectiveness of cooperation by biasing the spatial distribution of available relays, and by inducing a level of spatial and temporal correlation of the interference that diminishes the diversity improvement on which cooperative gains are founded. Moreover, the efficiency reduction entailed by several practical protocol issues related to carrier sense multiple access which are typically neglected in the literature is thoroughly investigated.

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arXiv2012

Cooperation in Carrier Sense Based Wireless Ad Hoc Networks - Part II: Proactive Schemes

This work is the second of a two-part series of papers on the effectiveness of cooperative techniques in non-centralized carrier sense-based ad hoc wireless networks. While Part I extensively discussed reactive cooperation, characterized by relayed transmissions triggered by failure events at the intended receiver, Part II investigates in depth proactive solutions, in which the source of a packet exploits channel state information to preemptively coordinate with relays in order to achieve the optimal overall rate to the destination. In particular, this work shows by means of both analysis and simulation that the performance of reactive cooperation is reduced by the intrinsic nature of the considered medium access policy, which biases the distribution of the available relays, locating them in unfavorable positions for rate optimization. Moreover, the highly dynamic nature of interference that characterizes non-infrastructured ad hoc networks is proved to hamper the efficacy and the reliability of preemptively allocated cooperative links, as unpredicted births and deaths of surrounding transmissions may force relays to abort their support and/or change the maximum achievable rate at the intended receiver. As a general conclusion, our work extensively suggests that CSMA-based link layers are not apt to effectively support cooperative strategies in large-scale non-centralized ad hoc networks.

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arXiv2011

Cognitive Interference Management in Retransmission-Based Wireless Networks

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this work, the optimal transmission policy for the secondary user when the primary user adopts a retransmission based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: (i) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; (ii) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

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