Enzo Baccarelli

Sapienza University of Rome | la sapienza · Department of Information Engineering, Electronics and Telecommunications

The emerging paradigm of Pervasive Mobile Cloud Computing aims at combining mobile networking, cloud computing and thin mobile device technology, in order to implement the vision of "hiding computers into the background environment". Consequently, energy-saving adaptive joint self-management of distributed (possibly, virtual) communication and comp...

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination and location-based real-time services to connected VC...

This paper focuses on the QoS-constrained jointly optimal adaptive distributed source coding, channel coding, network coding and power control for Co-Channel Interference (CCI)-limited wireless multiple class multicast networks, such as, for example, Wireless Sensor Networks (WSNs). The goal is to allocate the available system-wide resources by joi...

In this paper, we design, analyze the convergence properties and address the implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), no...

We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate...

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belo...

Gomoku, also known as five in a row, is a classical board game, ideally suited for quickly testing novel Artificial Intelligence (AI) techniques. With the aim of facilitating a developer willing to write a new Gomoku player, in this report we present an analysis of the main game concepts and strategies, which is wider and deeper than existing ones....

The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have...

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-time. Motivated by this conside...

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that...

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance...

Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack. Recently, some contributions have proposed techniques to endow the networks with early exits, allowing to obtain predictions at intermediate points of the stack. These multi-output networks have a num...

Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack. Recently, some contributions have proposed techniques to endow the networks with early exits, allowing to obtain predictions at intermediate points of the stack. These multi-output networks have a num...

The incoming IoT big data era requires efficient and resource-constrained mining of large sets of distributed data. This paper explores a possible approach to this end, combining the two emerging paradigms of Conditional Neural Networks with early exits and Fog Computing. Apart from describing the general framework, we provide four specific contrib...

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog...

The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be...

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands t...

The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be...

The incoming era of the Fifth-Generation Fog Computing-supported Radio Access Networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of Virtual Machines (VMs) towards nearby Fog data centers. For this purpose, t...

SIoT and FC are two stand-alone technological paradigms under the realm of the Future Internet. SIoT relies on the self-establishment and self-management of inter-thing social relationships, in order to guarantee scalability to large IoT networks composed of both human and non-human agents. FC extends cloud capabilities to the access network, in or...

With the incoming 5G access networks, it is forecasted that Fog computing (FC) and Internet of Things (IoT) will converge onto the Fog-of-IoT paradigm. Since the FC paradigm spreads, by design, networking and computing resources over the wireless access network, it would enable the support of computing-intensive and delay-sensitive streaming applic...

The incoming era of the Fifth-Generation Fog Computing-supported Radio Access Networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of Virtual Machines (VMs) towards nearby Fog data centers. For this purpose, t...

In this paper, we explore on a comparative basis the performance suitability of meta-heuristic, sometime denoted as random search algorithms, and greedy-type heuristics for the energy-saving joint dynamic scaling and consolidation of the network-plus-computing resources hosted by networked virtualized data centers when the target is the support of...

Due to the growing interest for multimedia contents by mobile users, designing bandwidth and delay-e�cient distributed algorithms for data searching over wireless (possibly, mobile) \ad hoc Peer-to-Peer (P2P) content Delivery Networks (CDNs) is a topic of current interest. This is mainly due to the limited computing-plus-communication resources fea...

In this paper we design and test a primary-secondary user resource-management controller in cognitive radio vehicular networks, under hard and soft collision constraints. We cast the resource-management problem into a stochastic network utility maximization problem and derive the optimal steadystate controllers, which adaptively allocate the access...

Fog Computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, up to date, have been considered standing-alone. However, due to their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the Future Intern...

Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node’s heterogeneity and/or node’s failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the...

Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live migration of VMs in wireless channel. In this paper we...

Nowadays, the Cloud is migrating to the edge of the network and the traditional Cloud Computing (CC) paradigm is not enough for the storage of Big Data (BD) produced by Internet of Things (IoT). To meet this requirement, a new platform is needed, namely " F og Computing ". Under Fog Computing, most of the functions of data processing are performed...

Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live mi- gration of VMs in wireless channel. In this paper...

One of the current key challenges in wireless sensor networks is the development of routing protocols that provide stable cluster-head election, while prolonging network lifetime by saving energy. In this contribution, a new Stable Election Protocol (SEP), named New-SEP (N-SEP), is presented to prolong the stable period of Fog-supported sensor netw...

In this paper, we propose a dynamic resource provisioning scheduler to maximize the application through-put and minimize the computing-plus-communication energy consumption in virtualized networked data centers. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on processing delay. The resulting optimal resource sch...

Big Data Stream Mobile Computing is proposed as a paradigm that relies on the convergence of Broadband Internet Mobile Networking and Real-time Mobile Cloud Computing. It aims at fostering the rise of novel self-configuring integrated computing-communication platforms for enabling in real-time the offloading and processing of big data streams acqui...

In this chapter, the authors develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. The objective is to minimize the communication-plus-computing energy which is wasted by processing stre...

Abstract—The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communicati...

Live virtual machine (VM) migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized datacenters, so to lead to reduced energy consumption. However, the bandwidth consumption and latency of current state-of-the-art live VM migration techniques still reduce the experienced benefits to much less...

In this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows. We p...

In this paper, a primary-secondary resource-management controller on vehicular networks is designed and tested. We formulate the resource-management problem as a constrained stochastic network utility maximization problem and derive the optimal resource management controller, which dynamically allocates the access time-windows to the secondary-user...

In this research contribution, we develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. Our objective is to minimize the communication-plus-computing energy which is wasted by processing...

In this contribution, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of Cognitive Radio and soft-input/soft-output data fusion in Vehicular Access Networks. The ultimate goal is to allow energy and computing-limited car smartphones to utilize the available Vehic...

Performing real-time applications on top of virtualized cloud systems requires that the overall per-job delay due to the in-cloud processing is upper bounded in a hardway. This opens the question about the optimal dynamic joint allocation of both computing and networking resources hosted in the Cloud. This is the focus of this contribution, where w...

In this paper, we design and test a full distributed and scalable resource-management scheduler for Vehicular Real-Time applications. We dynamically allocate the access time window (at the RoadSide Units) and the access rate and traffic flows (at the Vehicular Clients) under hard reliability collision constraints. We provide the optimal memoryless...

In this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows at th...

In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in Virtualized Networked Data Centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC’s platform works at the Middleware layer of the und...

In this paper, we focus on the quality-of-service (QoS)-constrained jointly optimal congestion control, network coding, and adaptive distributed power control for connectionless wireless networks affected by multiple access interference (MAI). The goal is to manage the available network resources, so as to support multiple multicast sessions with Q...

In this work, we focus on the Stochastic Traffic Engineering (STE) problem arising from the support of QoS-demanding real-time media-streaming applications over fading and congestion affected TCP-friendly/IP multiantenna wireless pipes. Two main contributions are presented. First, we recast the afforded STE problem in the form of a suitable cross-l...

Ultra Wide Band (UWB) Radio transmission is an emerging technology and up to now a lot of works are devoted to increase capacity and QoS of UWB-based systems. At this regard, a critical point still unresolved deals with the performance degradation induced in UWB systems by narrow-band interference, possibly generated by concurrent services. In this...

A new procedure is proposed for the identification of data channels affected by randomly time-variant fading. It is based on a set of nonlinear equations employing a minimum number of lags of the observed autocorrelation function (acf), and its solution gives the desired channel fading parameter estimates. Better estimation accuracy is obtained in...

In this contribution, we consider the so-called "last-hop" of wireless mesh networks, where multiple (possibly, no- madic) clients require to download huge-size les from wire- less mesh router via a faded link composed by several or- thogonal sub-channels. Since the transmit mesh router is battery-powered, a still open basic problem deals with the...

Recent advancements in network coding have shown great potential for efficient information multi-casting in wireless packet networks in terms of both throughput and robustness. In this paper, we address the jointly optimal congestion control, network coding and self-adaptive distributed power control for DiffServ-based wireless networks. The target...

Emerging media overlay networks for wireless applications aim at delivering Variable Bit Rate (VBR) encoded media contents to nomadic end users by exploiting the (fading-impaired and time-varying) access capacity offered by the "last-hop” wireless channel. In this application scenario, a still open question concerns the closed-form design of contro...

In this work, the Stochastic Traffic Engineering (STE) problem arising from the support of QoS-demanding real-time (e.g., delay and delay-jitter sensitive) media-streaming applications over unreliable IP-over-wireless pipes is addressed. Two main contributions are presented. First, we develop an optimal resource-management policy that allows a join...

This paper focuses on the problem of optimal QoS Traffic Engineering (TE) in Co-Channel Interference (CCI)-affected power-limited wireless access networks that support connectionless services. By exploiting the analytical tool offered by nonlinear optimization and following the emerging “Decomposition as Optimization” paradigm [1], the approach pur...

Traffic Engineering application to the cross-layer design of Multiple Access Interference (MAI)-affected powerlimited wireless networks, when Quality of Service constraints are also present, leads to deal with nonconvex resource allocation problems. Although several manageable-complexity solutions have been proposed, they are based on specific capa...

The delivering of variable bit rate (VBR) encoded media contents to nomadic end-users through the 'last-hop' wireless connection, rises up several challenges related to the fading-impaired and time-varying nature of the wireless medium itself. In detail, a still open question concerns the closed-form design of cross-layer control policies to maximi...