Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away - Antoine de Saint-Exupéry

### Journals

1. Davide Cecchinato, Tomaso Erseghe, Michele Rossi, Elastic Allocation of Computing Tasks in Energy Harvesting and Distributed IoT Edge Networks. IEEE Transactions on Network Science and Engineering. Early access, 2021.
We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server. IoT nodes and servers harvest ambient energy through dedicated hardware and jobs have a completion deadline. For this setup, we are concerned with the temporal allocation of jobs, either at the IoT nodes or at the edge servers, that maximizes the minimum level among all energy buffers in the network while meeting all the deadlines, i.e., that makes the network as much as possible energy neutral. A dynamic setting is considered, where jobs continuously and asynchronously arrive at the IoT nodes, and computing resources are allocated at runtime, automatically adapting the processing load across nodes and servers. To achieve this, we present a Model Predictive Control based algorithm, where the job scheduler solves a sequence of low complexity convex problems over a finite horizon and exploits future job and energy arrival estimates. The proposed predictive job allocation technique is numerically evaluated, showing excellent adaptation capabilities to load and energy processes. Also, its energy and delay performance is very close to that of an offline optimal scheduler with perfect information of all processes.
2. Hoang Dui Trinh, Angel Fernandez Gambin, Lorenza Giupponi, Michele Rossi, Paolo Dini, Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approach. IEEE Transactions on Network and Service Management. Early access. 2021.
The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical control channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this paper, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in an online and unsupervised fashion.
3. Jacopo Pegoraro, Francesca Meneghello and Michele Rossi, Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures. IEEE Transactions on Geoscience and Remote Sensing. Vol. 59, No. 4, April 2021.
In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical towards achieving robustness and accuracy in multi-user settings. Our technique is tested both on a single-target public dataset, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.
4. Flavio Esposito, Maria Mushtaq, Michele Berno, Gianluca Davoli, Davide Borsatti, Walter Cerroni, Michele Rossi, Necklace: an Architecture for Distributed and Robust Service Function Chains with Guarantees. IEEE Transactions on Network and Service Management, special issue on "Novel Techniques for Managing Softwarized Networks", Vol. 18, No. 1, March 2021.
The service function chaining paradigm links ordered service functions via network virtualization, in support of applications with severe network constraints. To provide widearea (federated) virtual network services, a distributed architecture should orchestrate cooperating or competing processes to generate and maintain virtual paths hosting service function chains, while guaranteeing performance and fast asynchronous consensus even in the presence of failures. To this end, we propose a prototype of an architecture for robust service function chain instantiation with convergence and performance guarantees. To instantiate a service chain, our system uses a fully distributed asynchronous consensus mechanism that has bounds on convergence time and leads to a (1−1/e)-approximation ratio with respect to the Pareto optimal chain instantiation, even in the presence of (non-byzantine) failures. Moreover, we show that a better optimal chain approximation cannot exist. To establish the practicality of our approach, we evaluate the system performance, policy tradeoffs and overhead via simulations and through a prototype implementation. We then describe our extensible management object model and compare our asynchronous consensus’s overhead against Raft, a recent decentralized consensus protocol, showing superior performance. We furthermore discuss a new management object model for distributed service function chain instantiation.
5. Ibtissam Labriji, Francesca Meneghello, Davide Cecchinato, Stefania Sesia, Eric Perraud, Emilio Calvanese Strinati, Michele Rossi, Mobility Aware and Dynamic Migration of MEC Services for the Internet of Vehicles. IEEE Transactions on Network and Service Management, special issue on "Novel Techniques for Managing Softwarized Networks". Vol. 18, No. 1, March 2021.
Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs.
6. Muddassar Hussain, Maria Scalabrin, Michele Rossi and Nicolò Michelusi, Mobility and Blockage-aware Communications in Millimeter-Wave Vehicular Networks. IEEE Transactions on Vehicular Technology, Vol. 69, No. 11, November 2020.
Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent mis-alignment and blockages require repeated beam training and handover, and incur enormous overhead. Nevertheless, mobility induces temporal correlations in the communication beams and in blockage events. In this paper, an adaptive design of beam training, data transmission and handover is proposed, that learns and exploits these temporal correlations to reduce the beam training overhead and optimally trade-off throughput and power consumption. At each time-slot, the serving base station (BS) decides to perform either beam training, data communication, or handover when blockage is detected, under uncertainty in the system state. The decision problem is cast as a partially observable Markov decision process, and the goal is to maximize the throughput delivered to the UE, under an average power constraint. To address the high dimensional optimization, an approximate constrained point-based value iteration (C-PBVI) method is developed, which simultaneously optimizes the primal and dual functions to meet the power constraint. Numerical results demonstrate a good match between the analysis and a simulation based on 2D mobility and 3D analog beamforming via uniform planar arrays at both BSs and UE, and reveal that C-PBVI performs near-optimally, and outperforms a baseline scheme with periodic beam training by 38% in spectral efficiency. Motivated by the structure of the C-PBVI policy, two heuristics are proposed, that trade complexity with sub-optimality, and achieve only 4% and 15% loss in spectral efficiency.
7. Angel Fernandez Gambin and Michele Rossi, A Sharing Framework for Energy and Computing Resources in Multi-Operator Mobile Networks. IEEE Transactions on Network and Service Management, Vol. 17, No. 2, June 2020.
Energy Harvesting (EH) and Multi-access Edge Computing (MEC) are here combined to build energy-sustainable mobile networks. We consider an edge infrastructure shared among several mobile operators and equipped with a solar EH farm for energy efficiency purposes together with an edge MEC server for low-latency computation, where two main goals are pursued: (i) to maximally and fairly exploit the available resources at the edge, allotting them among Base Stations (BSs) belonging to different operators; and (ii) to decrease the monetary cost incurred by energy purchases from the power grid. To do so, we devise an online framework combining Artificial Neural Network (ANN)-based pattern forecasting that learns energy harvesting and traffic load profiles over time, and Model Predictive Control (MPC)-based adaptive algorithms. Numerical results, obtained with real-world harvested energy, traffic load, and energy price traces, show that our proposal effectively reduces the amount of purchased energy from the electrical grid by more than 50% with respect to the case where no EH is considered, and by about 30% with respect to the case where the optimization is performed disregarding future energy and traffic load forecasts. Moreover, it is capable of reducing the energy consumption related to edge computation by about 20% with respect to two benchmark policies.
8. Maria Scalabrin, Guillermo Bielsa, Adrian Loch, Michele Rossi, Joerg Widmer, Machine Learning Based Network Analysis using Millimeter-Wave Narrow-Band Energy Traces. IEEE Transactions on Mobile Computing, Vol. 19, No. 5, pp. 1138-1155, May 2020.
Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.
9. Francesca Meneghello, Michele Rossi, Nicola Bui Smartphone Identification via Passive Traffic Fingerprinting: a Sequence-to-Sequence Learning Approach. IEEE Network, Vol. 34, No. 2, March/April 2020.
Passive cyber-security attacks do not require any modification of the data stream generated by the victim, nor the creation of false statement; in particular, those attacks based on statistical analysis aim at acquiring sensible information by just analyzing traffic patterns. Our work sits on the conjecture that the physical downlink control channel (PDCCH), which is transmitted in clear text, may be effectively used to statistically characterize the traffic generated by a smartphone in standby mode. Through this statistical signature, the attacker may then infer whether an unknown traffic pattern is generated by the victim user's terminal, guessing if the victim is in a certain geographical area and, in turn, gaining the ability to track the victim's movements and/or to profile her/his habits. In this work, we propose a data collection and processing framework that successfully obtains such signatures. User data patterns (transport block sizes and communications direction) are retrieved by analyzing the mobile network scheduling. Hence, a sequence-to-sequence learning framework to extract smartphone signatures from passive traffic is put forward, and is experimentally validated using a dataset of 40 user traces, successfully identifying up to 90% of the users.
10. Michele Rossi, Marco Centenaro, Aly Ba, Salma Eleuch, Tomaso Erseghe, Michele Zorzi, Distributed Learning Algorithms for Optimal Data Routing in IoT Networks. IEEE Transactions on Signal and Information Processing over Networks, Vol. 6, No. 1, February 2020.
We consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a source-specific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multi-hop fashion until it reaches a data collector (the IoT gateway). The problem we address is to compute a suitable rate-distortion working point for the compression scheme at the source nodes, while jointly assessing the most energy efficient routing paths for the data they transmit, under channel access, distortion and capacity constraints. This is formulated as a multi-objective optimization problem that is solved through distributed learning algorithms, where source coding and routing configurations emerge as the result of local interactions among the network devices. Our final algorithm is based on the alternating direction method of multipliers (ADMM), which is accelerated using the most recent findings from the literature. As a result, it has faster convergence (up to three times) to the global optimum than standard ADMM. Numerical results are discussed for selected network scenarios, emphasizing the interrelations that exist between signal reconstruction quality at the IoT gateway and total transport energy in the network.
11. Matteo Gadaleta, Michele Rossi, Eric J. Topol, Steven R. Steinhubl, Giorgio Quer, On the Effectiveness of Deep Representation Learning: the Atrial Fibrillation Case, IEEE Computer, Vol. 52, No. 11, pp. 18-29, November 2019.
The automatic and unsupervised analysis of biomedical time series is of primary importance for diagnostic and preventive medicine, enabling fast and reliable data processing to reveal clinical insights without the need for human intervention. Representation learning (RL) methods perform an automatic extraction of meaningful features that can be used, e.g., for a subsequent classification of the measured data. The goal of this study is to explore and quantify the benefits of RL techniques of varying degrees of complexity, focusing on modern deep learning (DL) architectures. We focus on the automatic classification of atrial fibrillation (AF) events from noisy single-lead electrocardiographic signals (ECG) obtained from wireless sensors. This is an important task as it allows the detection of sub-clinical AF which is hard to diagnose with a short in-clinic 12-lead ECG. The effectiveness of the considered DL architectures for the AF detection task is quantified and discussed in terms of classification performance, memory/data efficiency and computational complexity.
12. Angel Fernandez Gambin, Maria Scalabrin, Michele Rossi, Online Power Management Strategies for Energy Harvesting Mobile Networks. IEEE Transactions on Green Communications and Networking, Vol. 3, No. 3, pp. 721-738, September 2019.
The design of self-sustainable Base Station (BS) deployments is addressed in this paper. We target deployments featuring small BSs with Energy Harvesting (EH) and storage capabilities. These BSs can use ambient energy to serve the local traffic or store it for later use. A dedicated power packet grid is utilized to transfer energy across them, compensating for imbalance in the harvested energy or in the traffic load. Some BSs are offgrid, i.e., they can only use the locally harvested energy and that transferred from other BSs, whereas others are ongrid, i.e., they can additionally purchase energy from the power grid. Within this setup, an optimization problem is formulated where: harvested energy and traffic processes are estimated (at runtime) at the BSs through Gaussian Processes (GPs), and a Model Predictive Control (MPC) framework is devised for the computation of energy allocation and transfer across base stations. The combination of prediction and optimization tools leads to an efficient and online solution that automatically adapts to energy harvesting and load dynamics. Numerical results, obtained using real energy harvesting and traffic profiles, show substantial improvements with respect to the case where the optimization is carried out without predicting future system dynamics. The main improvements are in the outage probability (zero in most cases), and in the amount of energy purchased from the power grid, that is more than halved for the same served load.
13. Thembelihle Dlamini, Angel Fernandez Gambin, Daniele Munaretto, Michele Rossi, Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities, Wireless Communications and Mobile Computing, Special Issue: "Energy Efficient Wireless Networks", vol. 2019, Article ID 8593808, 17 pages, February 2019.
The convergence of communication and computing has lead to the emergence of Multi-access Edge Computing (MEC), where computing resources (supported by Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN), i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called ENergy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and QoS provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.
14. Matteo Gadaleta, Enrico Grisan, Andrea Facchinetti, Michele Rossi, Prediction of Adverse Glycemic Events from Continuous Glucose Monitoring Signal, IEEE Journal of Biomedical and Health Informatics, Vol. 23, No. 2, pp. 650-659, March 2019.
The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern Continuous Glucose Monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches that have been proposed in recent years is still missing, and is thus unclear which one is to be preferred. The aim of this study is to fill this gap, by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.
15. Nicola Piovesan, Angel Fernandez Gambin, Marco Miozzo, Michele Rossi, Paolo Dini, Energy Sustainable Paradigms and Methods for Future Mobile Networks: a Survey. Elsevier Computer Communications, Volume 119, Pages 101-117, April 2018.
In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.
16. Matteo Gadaleta, Michele Rossi, IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks. Pattern Recognition, Volume 74, Pages 25–37, February 2018. [arXiv:1606.03238v2]
Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from their way of walking, using the accelerometer and gyroscope (inertial) signals provided by a commercial smartphone worn in the front pocket of the user’s trousers. IDNet features several innovations including: i) a robust and smartphone-orientation-independent walking cycle extrac- tion block, ii) a novel feature extractor based on convolutional neural networks, iii) a one-class support vector machine to classify walking cycles, and the coherent integra- tion of these into iv) a multi-stage authentication technique. IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recog- nition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework. Experimental results show the superiority of our approach against state-of-the-art techniques, leading to misclassification rates (either false negatives or positives) smaller than 0.15% with fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the user authentication performance.
17. Thembelihle Dlamini, Michele Rossi, Daniele Munaretto, Softwarization of Mobile Network Functions Towards Agile and Energy-Efficient 5G Architectures: a Survey. Wireless Communications and Mobile Computing, Vol. 2017, Article ID 8618364, 20 November 2017.
Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting into green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN and NFV enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures.
18. Matteo Gadaleta, Federico Chiariotti, Michele Rossi, Andrea Zanella, D-DASH: a Deep Q-learning Framework for DASH Video Streaming. IEEE Transactions on Cognitive Communications and Networking, Vol. 3, No. 4, December 2017.
The ever-increasing demand for seamless high-definition video streaming, along with the widespread adoption of the Dynamic Adaptive Streaming over HTTP (DASH) standard, has been a major driver of the large amount of research on bitrate adaptation algorithms. The complexity and variability of the video content and of the mobile wireless channel make this an ideal application for learning approaches. Here, we present D-DASH, a framework that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH. Different learning architectures are proposed and assessed, combining feed-forward and recurrent deep neural networks with advanced strategies. D-DASH designs are thoroughly evaluated against prominent algorithms from the state-of-the-art, both heuristic and learning-based, evaluating performance indicators such as image quality across video segments and freezing/rebuffering events. Our numerical results are obtained on real and simulated channel traces and show the superiority of D-DASH in nearly all the considered quality metrics. Besides yielding a considerably higher QoE, the D-DASH framework exhibits faster convergence to the rate-selection strategy than the other learning algorithms considered in the study. This makes it possible to shorten the training phase, making D-DASH a good candidate for client-side runtime learning.
19. Riccardo Bonetto, Michele Rossi, Stefano Tomasin, Carlo Fischione, Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids. IEEE Access, Vol. 5, pp. 19782-19798, 8 September 2017.
In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from Distributed Energy Resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately, or the trading strategy is tailored to a specific electrical optimization objective, in this work we consider their joint optimization. We also allow for a modular approach, where the market model can support any smart grid optimization goal. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy, 2) the loads try to minimize their expense, and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non-convex, and an equivalent convex formulation is derived. Centralized solutions are discussed and a procedure to distribute the solution is proposed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are finally presented, showing the proposed model results in economic benefits for all the users (generators and loads) and in an increased electrical efficiency for the grid.
20. Mohsen Hooshmand, Davide Zordan, Tommaso Melodia, Michele Rossi, SURF: subject-adaptive unsupervised ECG signal compression for wearable fitness monitors. IEEE Access, Vol. 5, pp. 19517 - 19535, 7 September 2017.
Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers is expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on size- and resource-constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this article proposes SURF, a subject-adpative unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a {\it subject-adaptive} dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of codewords according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including (i) compression ratios up to 90-times, (ii) reconstruction errors (RMSE) between 2% and 7% of the signal's range (depending on the amount of compression sought), and (iii) reduction in energy consumption of up to two-orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range [40,50], which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60~bit/s and 75~bit/s for CE=40 and CE=50, respectively.
21. Mohsen Hooshmand, Davide Zordan, Davide Del Testa, Enrico Grisan, Michele Rossi, Boosting the Battery Life of Wearables for Health Monitoring through the Compression of Biosignals. IEEE IoT Journal, Vol. 4, No. 5, pp. 1647-1662, October 2017.
Modern wearable IoT devices enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed.
22. Alessandro Biason, Chiara Pielli, Michele Rossi, Andrea Zanella, Davide Zordan, Mark Kelly, and Michele Zorzi, An Energy- and Context-Centric Perspective on IoT Architecture and Protocol Design, IEEE Access, Vol. 5, pp. 6894-6908, 10 April 2017.
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application.
23. Lyes Khelladi, Djamel Djenouri, Michele Rossi and Nadjib Badache, Efficient on-demand multi-node charging techniques for wireless sensor networks, Elsevier Computer Communications, Vol. 101, March 2017.
This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-node power transfer through a mobile charger (MC).The proposed solution, called On-demand Multi-node Charg- ing (OMC), features an original threshold-based tour launching (TTL) strategy, using request grouping, and a path plan- ning algorithm based on minimizing the number of stopping points in the charging tour. Contrary to existing solutions, which focus on shortening the charging delays, OMC groups incoming charging requests and optimizes the charging tour and the mobile charger energy consumption. Although slightly increasing the waiting time before nodes are charged, this allows taking advantage of multiple simultaneous charges and also reduces node failures. At the tour planning level, a new modeling approach is used. It leverages simultaneous energy transfer to multiple nodes by maximizing the number of sensors that are charged at each stop. Given its NP-hardness, tour planning is approximated through a clique partition- ing problem, which is solved using a lightweight heuristic approach. The proposed schemes are evaluated in offline and on-demand scenarios and compared against relevant state-of-the-art protocols. The results in the offline scenario show that the path planning strategy reduces the number of stops and the energy consumed by the mobile charger, compared to existing offline solutions. This is with further reduction in time complexity, due to the simple heuristics that are used. The results in the on-demand scenario confirm the effectiveness of the path planning strategy. More importantly, they show the impact of path planning, TTL and multi-node charging on the efficiency of handling the requests, in a way that reduces node failures and the mobile charger energy expenditure.
24. Leo Turi, Nicola Piovesan, Enrico Toigo, Borja Martinez and Michele Rossi, Data Analytics for Smart Parking Applications (open access), MDPI Sensors, Special Issue: "Smart City: Vision and Reality", September 2016.
We consider real-life smart parking systems where parking-lot occupancy data is collected from field sensor devices and sent to backend servers for further processing and usage from applications. Our objective is to make this data useful to end users, such as parking managers and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: 1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers, and 2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times, and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self organizing maps). These, are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely, expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all the outliers in the dataset.
25. Mohsen Hooshmand, Michele Rossi, Davide Zordan, Michele Zorzi, Covariogram-based Compressive Sensing for Environmental Wireless Sensor Networks. IEEE Sensors Journal, Vol. 16, No. 6, March 2016.
In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal's spatio-temporal correlation structure through the Kronecker CS framework. CB-CS's performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver).
26. Riccardo Bonetto, Stefano Tomasin, Michele Rossi, Michele Zorzi, On The Interplay of Distributed Power Loss Reduction and Communication in Low Voltage Microgrids. IEEE Transactions on Industrial Informatics, Vol. 12, No. 1, February 2016.
Distributed generators (DGs), coupled with suitable control and communication infrastructures, are expected to play a key role in improving the efficiency of electricity grids. In this paper, we focus on low-voltage and single-phase microgrids exploring the interplay of distributed power loss reduction and communication. We select representative power-loss reduction algorithms from the state of the art and provide design rules for the required networking strategies in the presence of lossy communication links, assessing the impact of communication as well as electrical grid features. Toward this end, we devise a novel statistical cosimulation (electricity grid, communication, and control) framework that faithfully mimics the characteristics of real-world microgrids in terms of communication and grid topologies, power demand, and distributed generation from solar sources. Our numerical results highlight the role of communication procedures and the differences among the selected optimization techniques for power loss reduction, assessing their convergence rate and quantifying the impact of communication failures, line impedance estimation error, communication and electricity grid topologies, network size, and number of DGs.
27. Davide Zordan, Tommaso Melodia and Michele Rossi, On the Design of Temporal Compression Strategies for Energy Harvesting Sensor Networks. IEEE Transactions on Wireless Communications, Vol. 15, No. 2, February 2016.
Recent advances in energy harvesting devices and low-power embedded systems are enabling energetically self-sustainable wireless sensing systems able to sense, process, and wirelessly transmit environmental data. In such systems, energy resources need to be judiciously allocated to processing and transmission tasks to guarantee high-fidelity reconstruction of the phenomenon under observation while keeping the system operational over extended periods of time. Within this context, this paper addresses the problem of designing efficient policies to control the task of lossy data compression for wireless transmission over fading channels in the presence of a stochastic energy input process and a replenishable energy buffer. As a first contribution, the transmission and energy dynamics of a sensor node implementing practical lossy compression methods are modeled as a constrained Markov decision problem (CMDP). Then, an algorithm is designed to derive optimal compression/transmission policies through a Lagrangian relaxation approach combined with a dichotomic search for the Lagrangian multiplier, while also obtaining theoretical results on the optimal policy structure. Furthermore, a thorough numerical evaluation of optimal and heuristic policies is conducted under different scenarios. Finally, the impact of practical operating conditions, including perfect versus delayed channel state information and power control, is evaluated.
28. Davide Del Testa, Michele Rossi, Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders. IEEE Signal Processing Letters, Vol. 22, No. 12, September 2015.
Wearable Internet of Things (IoT) devices permit the massive collection of biosignals (e.g., heart-rate, oxygen level, respiration, blood pressure, photo-plethysmographic signal, etc.) at low cost. These, can be used to help address the individual fitness needs of the users and could be exploited within personalized healthcare plans. In this letter, we are concerned with the design of lightweight and efficient algorithms for the lossy compression of these signals. In fact, we underline that compression is a key functionality to improve the lifetime of IoT devices, which are often energy constrained, allowing the optimization of their internal memory space and the efficient transmission of data over their wireless interface. To this end, we advocate the use of autoencoders as an efficient and computationally lightweight means to compress biometric signals. While the presented techniques can be used with any signal showing a certain degree of periodicity, in this letter we apply them to ECG traces, showing quantitative results in terms of compression ratio, reconstruction error and computational complexity. State of the art solutions are also compared with our approach.
29. Davide Zordan, Marco Miozzo, Paolo Dini and Michele Rossi, When Telecommunication Networks Meet Energy Grids: Cellular Networks with Energy Harvesting and Trading Capabilities, IEEE Communications Magazine - Special Issue - Energy Harvesting Communications, Vol. 53, No. 6, June 2015. [Slides]
In this article, we cover eco-friendly cellular networks, discussing the benefits that ambient energy harvesting offers in terms of energy consumption and profit. We advocate for future networks where energy harvesting will be massively employed to power network elements; even further, communication networks will seamlessly blend with future power grids. This vision entails the fact that future base stations may trade some of the excess energy they harvest so as to make a profit and provide ancillary services to the electricity grid. We start by discussing recent developments in the energy harvesting field, and then deliberate on the way future energy markets are expected to evolve and the new fundamental trade-offs that arise when energy can be traded. Performance estimates are given throughout to support our arguments, and open research issues in this emerging field are discussed.
30. Nicola Bui and Michele Rossi, Staying Alive: System Design for Self-Sufficient Sensor Networks, ACM Transactions on Sensor Networks, Vol. 11, No. 3, March 2015. [Slides]
Self-sustainability is a crucial step for modern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies that make the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources. Our framework sheds new light into the design of pragmatic schemes for the control of energy-harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity, the harvester size, the information transmission rate, and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state-of-the-art solutions, and describing the impact of relevant design choices in terms of achievable network throughput and battery-level dynamics.
31. Cristiano Tapparello, Osvaldo Simeone and Michele Rossi, Dynamic Compression-Transmission for Energy-Harvesting Multihop Networks with Correlated Sources, [technical report: arXiv:1203.3143v1]. IEEE/ACM Transactions on Networking, Vol. 22, No. 6, December 2014.
Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost.
32. Davide Zordan, Borja Martinez, Ignasi Villajosana and Michele Rossi, On the Performance of Lossy Compression Schemes for Energy Constrained Sensor Networking, [early version: arXiv:1206.2129]. ACM Transactions on Sensor Networks, Vol. 11, No. 1, August 2014.
Lossy temporal compression is key for energy constrained wireless sensor networks (WSN), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this paper, we evaluate a number of selected lossy compression methods from the literature, and extensively analyze their performance in terms of compression efficiency, computational complexity and energy consumption. Specifically, we first carry out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and Wavelet-based models, by looking at their performance as a function of relevant signal statistics. Second, we obtain formulas through numerical fittings, to gauge their overall energy consumption and signal representation accuracy. Third, we evaluate the benefits that lossy compression methods bring about in interference-limited multi-hop networks, where the channel access is a source of inefficiency due to collisions and transmission scheduling. Our results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay and increased reliability performance.
33. Angelo P. Castellani, Michele Rossi, Michele Zorzi, Back Pressure Congestion Control for CoAP/6LoWPAN Networks. Elsevier Ad Hoc Networks - Special Issue - From M2M communications to the Internet of Things: Opportunities and challenges, Vol. 18, July 2014, pp: 71–84.
In this paper we address the design of network architectures for the Internet of Things by proposing practical algorithms to augment IETF CoAP/6LoWPAN protocol stacks with congestion control functionalities. Our design is inspired by previous theoretical work on back pressure routing and is targeted toward Web-based architectures featuring bidirectional data flows made up of CoAP request/response pairs. Here, we present three different cross-layer and fully decentralized congestion control schemes and compare them against ideal back pressure and current UDP-based protocol stacks. Hence, we discuss results obtained using ns-3 through an extensive simulation campaign for two different scenarios: unidirectional and upstream flows and bidirectional Web-based CoAP flows. Our results confirm that the proposed congestion control algorithms perform satisfactorily in both scenarios for a wide range of values of their configuration parameters, and are amenable to the implementation onto existing protocol stacks for embedded sensor devices.
34. Alessandro Camillò, Michele Nati, Chiara Petrioli, Michele Rossi and Michele Zorzi, IRIS: Integrated Data Gathering and Interest Dissemination System for Wireless Sensor Networks, Elsevier Ad Hoc Networks - Special Issue - Cross-Layer Design in Ad Hoc and Sensor Networks. Vol. 11, No. 2, March 2013, pp: 654–671.
This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP.
35. Giorgio Quer, Riccardo Masiero, Gianluigi Pillonetto, Michele Rossi and Michele Zorzi, Sensing, Compression and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework, IEEE Transactions on Wireless Communications Vol. 11, No. 10, October 2012, pp: 3447-3461.
We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.
36. ##### Additional material:
37. Michele Rossi, Cristiano Tapparello, Stefano Tomasin, On Optimal Cooperator Selection Policies for Multi-Hop Ad Hoc Networks, IEEE Transactions on Wireless Communications. Vol. 10, No. 2, February 2011, pp: 506-518. You can also download the Source Code.
In this paper we consider wireless cooperative multihop networks, where nodes that have decoded the message at the previous hop cooperate in the transmission toward the next hop, realizing a distributed space-time coding scheme. Our objective is finding optimal cooperator selection policies for arbitrary topologies with links affected by path loss and multipath fading. To this end, we model the network behavior through a suitable Markov chain and we formulate the cooperator selection process as a stochastic shortest path problem (SSP). Further, we reduce the complexity of the SSP through a novel pruning technique that, starting from the original problem, obtains a reduced Markov chain which is finally embedded into a solver based on focused real time dynamic programming (FRTDP). Our algorithm can find cooperator selection policies for large state spaces and has a bounded (and small) additional cost with respect to that of optimal solutions. Finally, for selected network topologies, we show results which are relevant to the design of practical network protocols and discuss the impact of the set of nodes that are allowed to cooperate at each hop, the optimization criterion and the maximum number of cooperating nodes.
38. Michele Rossi, Nicola Bui, Giovanni Zanca, Luca Stabellini, Riccardo Crepaldi and Michele Zorzi, SYNAPSE++: Code Dissemination in Wireless Sensor Networks using Fountain Codes, IEEE Transactions on Mobile Computing, Vol. 9, No. 12, December 2010, pp: 1749-1765.
This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Furthermore, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of fountain codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language. At the end of this paper, the effectiveness of SYNAPSE++ is demonstrated through experimental results over actual multihop deployments, and its performance is compared with that of Deluge, the de facto standard protocol for code dissemination in WSNs. The TinyOS 2 code of SYNAPSE++ is available at http://dgt.dei.unipd.it/download.
39. Nicola Baldo, Marco Miozzo, Federico Guerra, Michele Rossi and Michele Zorzi, Miracle: the Multi-Interface Cross-layer Extension of ns2, EURASIP Journal of Wireless Communications and Networking, Special Issue on Simulators and Experimental Testbeds Design and Development for Wireless Networks, Volume 2010 (2010), Article ID 761792, 16 pages.

40. Alfred Asterjadhi, Elena Fasolo, Michele Rossi, Joerg Widmer and Michele Zorzi, Toward Network Coding-Based Protocols for Data Broadcasting in Wireless Ad Hoc Networks, IEEE Transactions on Wireless Communications, Vol. 9, No. 2, February 2010, pp: 662-673.

41. Paolo Casari, Angelo P. Castellani, Angelo Cenedese, Claudio Lora, Michele Rossi, Luca Schenato and Michele Zorzi, The "Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI)” Project, Sensors, Vol. 9, No. 6, May 2009, pp: 4056-4082. Published online at: http://www.mdpi.com/1424-8220/9/6/4056

42. Michele Rossi, Nicola Bui and Michele Zorzi, Cost and Collision Minimizing Forwarding Schemes for Wireless Sensor Networks: Design, Analysis and Experimental Validation, IEEE Transactions on Mobile Computing, Vol. 8, No. 3, March 2009, pp: 322-337.

43. Leonardo Badia, Nicola Bui, Marco Miozzo, Michele Rossi and Michele Zorzi, Improved Resource Management through User Aggregation in Heterogeneous Multiple Access Wireless Networks, IEEE Transactions on Wireless Communications, Vol. 7, No. 9, Sept. 2008, pp: 3329-3334.

44. Michele Rossi, Leonardo Badia, Paolo Giacon and Michele Zorzi, Energy and Connectivity Performance of Routing Groups in Multi-radio Multi-hop Networks, Wireless Communications and Mobile Computing Journal, John Wiley & Sons. Vol. 8, No. 3, Mar. 2008, pp. 327-342.

45. Michele Rossi, Ramesh R. Rao and Michele Zorzi, Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks, Springer Wireless Networks Journal, Vol. 14, No. 1, Feb. 2008, pp: 55-70.

46. [2008 best tutorial paper award] Elena Fasolo, Michele Rossi, Jörg Widmer and Michele Zorzi, In-Network Aggregation Techniques for Wireless Sensor Networks: A Survey, IEEE Wireless Communications Magazine, Apr. 2007, pp: 70-87. Best Tutorial Paper Award
In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area.
47. Michele Rossi and Michele Zorzi, Integrated Cost-Based MAC and Routing Techniques for Hop Count Forwarding in Wireless Sensor Networks, IEEE Transactions on Mobile Computing, Vol. 6, No. 4, Apr. 2007, pp: 434-448.

48. Leonardo Badia, Marco Miozzo, Michele Rossi and Michele Zorzi, Routing Schemes in Heterogeneous Wireless Networks Based on Access Advertisement and Backward Utilities for QoS Support, IEEE Communications Magazine, Vol. 45, No. 2, Feb. 2007, pp: 67-73.

49. Stefan Dulman, Michele Rossi, Paul Havinga and Michele Zorzi, On the hop count statistics for randomly deployed wireless sensor networks, International Journal of Sensor Networks (IJSNET), Vol. 1, No. 1/2, 2006, pp: 89-102.

50. Leonardo Badia, Michele Rossi and Michele Zorzi, SR ARQ Packet Delay Statistics on Markov Channels in the Presence of Variable Arrival Rate, IEEE Transactions on Wireless Communications, Vol. 5, No. 7, July 2006, pp: 1639-1644.

51. Michele Rossi, Leonardo Badia and Michele Zorzi, SR ARQ Delay Statistics on N-State Markov Channels with Non-instantaneous feedback, IEEE Transactions on Wireless Communications, Vol. 5, No. 6, June 2006, pp:1526-1536.

52. Michele Rossi, Leonardo Badia and Michele Zorzi, On the Delay Statistics of SR ARQ over Markov Channels with Finite Round-Trip Delay, IEEE Transactions on Wireless Communications, Vol. 4, No. 4, July 2005, pp: 1858-1868.

53. Michele Rossi, Frank H.P. Fitzek and Michele Zorzi, Error Control Techniques for Efficient Multicast Streaming in UMTS Networks: Proposals and Performance Evaluation, Journal of Systemics, Cybernetics and Informatics, Vol. 2, No. 3, 2004.

54. Michele Rossi, Raffaella Vicenzi and Michele Zorzi, Accurate Analysis of TCP on Channels With Memory and Finite Round-Trip Delay, IEEE Transactions on Wireless Communications, Vol. 3, No. 2, Mar. 2004, pp: 627-640.

55. Carla F. Chiasserini, Francesca Cuomo, Leonardo Piacentini, Michele Rossi, Ilenia Tinnirello and Francesco Vacirca, Architecture and Protocols for Mobile Computing Applications: A Reconfigurable Approach, IEEE Computer Networks, Vol. 44, No. 4, Mar. 2004, pp: 545-567.

56. Mario Marchese, Michele Rossi and Giacomo Morabito, PETRA: Performance Enhancing Transport Architecture for Satellite Communications, IEEE Journal on Selected Areas in Communications (JSAC), Vol. 22, No. 2, Feb. 2004, pp: 320-332.

57. Michele Rossi and Michele Zorzi, Analysis and Heuristics for the Characterization of Selective Repeat ARQ Delay Statistics over Wireless Channels, IEEE Transactions on Vehicular Technology, Vol. 52, No. 5, Sept. 2003, pp: 1365-1377.

58. Michele Zorzi, Michele Rossi and Gianluca Mazzini, Throughput and Energy Performance of TCP on a Wideband CDMA Air Interface, Wireless Communications and Mobile Computing Journal, John Wiley & Sons. Vol. 2, No. 1, Feb. 2002, pp. 71-84.

59. Alessandra Giovanardi, Gianluca Mazzini, Michele Rossi and Michele Zorzi, Improved Header Compression for TCP/IP over Wireless Links, IEE Electronic Letters, Vol. 36, No. 23, Nov. 2000, pp. 1958-1960.

### Book Chapters

1. Paolo Dini, Michele Rossi, Machine Learning for 5G Mobile Networks: a Pragmatic Essay on Where, How and Why, Chapter 30 of the White Book: The 5G Italy Book 2019: a Multiperspective View of 5G, December 2019.

2. Michele Rossi and Riccardo Bonetto, Smart Grid for the Smart City book chapter in: Designing, Developing, and Facilitating Smart Cities, Ed. Angelakis, Tragos, Kapovits, Pöhls, and Bassi. Springer International Publishing, Switzerland, November 6, 2016.

3. Nicola Bui, Michele Rossi and Michele Zorzi, Networking Technologies for Smart Grid book chapter in: IEEE Vision for Smart Grid Communications: 2030 and Beyond, Ed. Sanjay Goel, Stephen F. Bushand Dave Bakken. IEEE Communications Society 2013. IEEE 3 Park Avenue New York, NY 10016-5997 USA.

4. Nicola Bui, Angelo P. Castellani, Paolo Casari, Michele Rossi, Lorenzo Vangelista and Michele Zorzi, Implementation of and Performance Evaluation of Wireless Sensor Networks for Smart Grid Bookchapter in E. Hossain, Z. Han, and H. V. Poor, Smart Grid Communications and Networking, (edited volume), Cambridge University Press, IBSN-13: 978-1107014138, June 30, 2012.

5. Michele Rossi, Data Link Layer book chapter in: Principles of Communications Networks and Systems. Ed. N. Benvenuto and M. Zorzi. John Wiley and Sons Ltd. ISBN-13: 978-0470744314. December 13, 2011. (105 pages)

### Conferences

1. M. Berno, M. Canil, N. Chiarello, L. Piazzon, F. Berti, F. Ferrari, A. Zaupa, N. Ferro, M. Rossi, G.A. Susto, A Machine Learning-based Approach for Advanced Monitoring of Automated Equipment for the Entertainment Industrys, IEEE International Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 and IoT), 7-9 June, Rome, Italy, 2021.

2. F. Meneghello, D. Cecchinato, M. Rossi, Mobility Prediction via Sequential Learning for 5G Mobile Networks, IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 12-14 October, Thessaloniki, Greece, 2020.

3. J. Pegoraro, D. Solimini, F. Matteo, E. Bashirov, F. Meneghello, M. Rossi, Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar, IEEE Radar Conference (RadarCon), Florence, Italy, 21-25 September, 2020.

4. Michele Berno, Flavio Esposito, Michele Rossi, Elastic Function Chain Control for Edge Networks under Reconfiguration Delay and QoS Requirements, IEEE Mobile Cloud, Oxford, UK, 3-6 August, 2020. Awarded the Mobile Cloud 2020 Best Paper Award.

5. Muddassar Hussain, Maria Scalabrin, Michele Rossi, Nicolò Michelusi, Adaptive Millimeter-Wave Communications Exploiting Mobility and Blockage Dynamics, IEEE International Conference on Communications (ICC), Dublin, Ireland, 7-11 June, 2020.

6. Davide Cecchinato, Michele Berno, Flavio Esposito, Michele Rossi, Allocation of Computing Tasks in Distributed MEC Servers co-powered by Renewable Sources and the Power Grid, IEEE ICASSP, Barcelona, Spain, 4-8 May, 2020.

7. Matteo Gadaleta, Giulia Cisotto, Michele Rossi, Rana Zia Ur Rehman, Lynn Rochester, Silvia Del Din, Deep Learning Techniques for Improving Digital Gait Segmentation, Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July, 2019.

8. Davide Talon, Luca Attanasio, Federico Chiariotti, Matteo Gadaleta, Andrea Zanella, Michele Rossi, Comparing DASH Adaptation Algorithms in a Real Network Environment, European Wireless, Aarhus, Denmark, 2-4 May, 2019.

9. Michele Berno, Juan José Alcaraz Espín, Michele Rossi, On the Allocation of Computing Tasks under QoS Constraints in Hierarchical MEC Architectures, IEEE International Conference on Fog and Mobile Edge Computing (FMEC 2019), June 10-13, Rome, Italy, 2019. Awarded the FMEC 2019 Best Paper Award.

10. Angel Fernandez Gambin, Michele Rossi, Smart Energy Policies for Sustainable Mobile Networks via Forecasting and Adaptive Control, IEEE GLOBECOM Workshop: "Workshops: Wireless Energy Harvesting Communication Networks", Abu Dhabi, United Arab Emirates, 9-13 December, 2018.

11. Maria Scalabrin, Nicolò Michelusi, Michele Rossi, Beam Training and Data Transmission Optimization in Millimeter-Wave Vehicular Networks, IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, 9-13 December, 2018.

12. Thembelihle Dlamini, Angel Fernandez Gambin, Daniele Munaretto, Michele Rossi, Online Resource Management in Energy Harvesting BS Sites Through Prediction and Soft-Scaling of Computing Resources, IEEE PIMRC 2018, Bologna, Italy, 9-12 September, 2018.

13. Angel Fernandez Gambin, Elvina Gindullina, Leonardo Badia, Michele Rossi, Energy Cooperation fo Sustainable IoT Services within Smart Cities, IEEE WCNC 2018, Barcelona, Spain, 15-18 April, 2018.

14. Angel Fernandez Gambin, Michele Rossi, Energy Cooperation for Sustainable Base Station Deployments: Principles and Algorithms, IEEE GLOBECOM 2017, Singapore, 4-8 December, 2017. Awarded best paper in the GCSN Symposium.

15. Davide Zordan, Raul Parada Medina, Michele Rossi, Michele Zorzi, Automatic Rate-Distortion Classification for the IoT: Towards Signal-Adaptive Network Protocols, IEEE GLOBECOM 2017, Singapore, 4-8 December, 2017.

16. Maria Scalabrin, Matteo Gadaleta, Riccardo Bonetto, Michele Rossi, A Bayesian Forecasting and Anomaly Detection Framework for Vehicular Monitoring Networks, IEEE Machine Learning for Signal Processing Workshop (MLSP 2017), Roppongi, Tokio, Japan, 22-25 September, 2017.

17. Maria Scalabrin, Michele Rossi, Guillermo Bielsa, Adrian Loch, Joerg Widmer, Millimetric Diagnosis: Machine Learning Based Network Analysis for mm-Wave Communication. IEEE WoWMoM (World of Wireless Mobile and Multimedia Networks), Macau, China, June 12-15, 2017.

18. Leonardo Bonati, Angel Fernandez Gambin, Michele Rossi, Charging Terminals amid Dense Cellular Networks. IEEE WoWMoM (World of Wireless Mobile and Multimedia Networks), Macau, China, June 12-15, 2017.

19. Davide Zordan, Michele Rossi, Michele Zorzi, Rate-Distortion Classification for Self-Tuning IoT Networks. IEEE ICC 2017 (IEEE ICC-WT04 5thIEEE International Workshop on Smart Communication Protocols and Algorithms), Paris, France, 21-25 May 2017.

20. Marco Miozzo, Lorenza Giupponi, Michele Rossi, Paolo Dini, Switch-On/Off Policies for Energy Harvesting Small Cells through Distributed Q-Learning. IEEE WCNC 2017 (IEEE WCNC Workshop on Green and Sustainable 5G Wireless Networks (GRASNET 2), San Francisco, CA US, 19-22 March 2017.

21. Marco Centenaro, Michele Rossi, Michele Zorzi, Joint Optimization of Lossy Compression and Transport in Wireless Sensor Networks. IEEE GLOBECOM 2016 (IEEE International Workshop on Low-Layer Implementation and Protocol Design for IoT Applications), Washington, DC US, 4-8 December 2016.

22. Riccardo Bonetto, Michele Rossi, Parallel Multi-Step Ahead Power Demand Forecasting through NAR Neural Networks. IEEE International Conference on Smart Grid Communications (SmartGridComm), November 6-9, Sydney, Australia, 2016.

23. Valentina Vadori, Enrico Grisan, Michele Rossi, Biomedical Signal Compression with Time- and Subject-adaptive Dictionary for Wearable Devices. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 13-16, Vietri sul Mare, Salerno, Italy, 2016.

24. Matteo Gadaleta, Luca Merelli, Michele Rossi, Human Authentication From Ankle Motion Data Using Convolutional Neural Networks. IEEE Statistical Signal Processing Workshop (SSP), June 26-29, Palma de Mallorca, Spain, 2016.

25. Enrico Grisan, Giorgia Cantisani, Giacomo Tarroni, Seung Keun Yoon, Michele Rossi, A supervised learning approach for robust detection of heart beat in plethysmographic data. IEEE Engineering in Medicine and Biology Society (EMBS), August 25-29, Milan, Italy, 2015.

26. Roberto Francescon, Mohsen Hooshmand, Matteo Gadaleta, Enrico Grisan, Seung Keun Yoon, Michele Rossi, Toward Lightweight Biometric Signal Processing for Wearable Devices. IEEE Engineering in Medicine and Biology Society (EMBS), August 25-29, Milan, Italy, 2015.

27. Marco Miozzo, Lorenza Giupponi, Michele Rossi, Paolo Dini, Distributed Q-Learning for Energy Harvesting Heterogeneous Networks. IEEE ICC Workshop on Green Communications and Networks with Energy Harvesting, Smart Grids, and Renewable Energies, June 8-12, London, UK, 2015.

28. Riccardo Bonetto, T. Caldognetto, Simone Buso, Michele Rossi, Stefano Tomasin, Paolo Tenti, Lightweight Energy Management of Islanded Operated Microgrids for Prosumer Communities. IEEE International Conference on Industrial Technology (ICIT), March 17-19, Seville, Spain, 2015.

29. Riccardo Bonetto, Stefano Tomasin, Michele Rossi, When Order Matters: Communication Scheduling for Current Injection Control in Micro Grids. IEEE Conference on Innovative Smart Grid Technologies (ISGT2015), sponsored by the IEEE Power & Energy Society (PES), February 17-20, Washington DC, US, 2015.

30. Michele Rossi, Mohsen Hooshmand, Davide Zordan, Michele Zorzi, Evaluating the Gap Between Compressive Sensing and Distributed Source Coding in WSN. IEEE International Conference on Computing, Networking and Communications (ICNC), February 16-19, Anaheim, California, US, 2015.

31. Marco Miozzo, Davide Zordan, Paolo Dini and Michele Rossi, SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes. IEEE ENERGYCON, IEEE Energy Conference, May 13-16, Dubrovnik, Croatia, 2014.

32. Nicola Bui and Michele Rossi, Dimensioning Self-sufficient Networks of Energy Harvesting Embedded Devices, International Workshop on Wireless Access Flexibility (WiFlex), September 4-6, Kaliningrad, Russia, 2013. (Also published in the Springer Lecture Notes in Computer Science (LNCS), Vol. 8072/2013, pp. 138-150.)

33. Diego Altolini, Vishwas Lakkundi, Nicola Bui, Cristiano Tapparello and Michele Rossi, Low Power Link Layer Security for IoT: Implementation and Performance Analysis, IEEE IWCMC, June 1-5, Cagliari, Sardinia, Italy, 2013.

34. Marco Mezzavilla, Marco Miozzo, Michele Rossi, Nicola Baldo and Michele Zorzi, A Lightweight and Accurate Link Abstraction Model for System-Level Simulation of LTE Networks in ns-3, ACM MSWIM 2012, October 21-25, Paphos, Cyprus Island, 2012. [a longer technical report]

35. Riccardo Bonetto, Nicola Bui, Vishwas Lakkundi, Alexis Olivereau, Alexandru Serbanati and Michele Rossi, Secure Communication for Smart IoT Objects: Protocol Stacks, Use Cases and Practical Examples, IEEE IoT-SoS Workshop, San Francisco, CA, US, 2012.

36. Riccardo Bonetto, Nicola Bui, Michele Rossi and Michele Zorzi, McMAC: a power efficient, short preamble Multi-Channel Medium Access Control protocol for wireless sensor networks, Workshop on NS3 (WNS3) 2012, Sirmione, Italy, 23 March 2012.

37. Cristiano Tapparello, Stefano Tomasin and Michele Rossi, Online Policies for Opportunistic Virtual MISO Routing in Wireless Ad Hoc Networks, IEEE WCNC 2012, Paris, France, 1-4 April 2012.

38. Nicola Bui, Apostolos Georgiadis, Michele Rossi, Ignasi Vilajosana, SWAP Project: Beyond the State of the Art on Harvested Energy-Powered Wireless Sensors Platform Design, IEEE IoTech 2011, Valencia, Spain, 17 October 2011.

39. Davide Zordan, Giorgio Quer, Michele Zorzi and Michele Rossi, Modeling and Generation of Space-Time Correlated Signals for Sensor Network Fields, IEEE GLOBECOM 2011, Houston, Texas, US, 5-9 December 2011.

40. Cristiano Tapparello, Davide Chiarotto, Michele Rossi, Osvaldo Simeone and Michele Zorzi, Spectrum Leasing via Cooperative Opportunistic Routing in Distributed Ad Hoc Networks: Optimal and Heuristic Policies, Asilomar Conference on Signals Systems and Computers, Pacific Grove, CA, US, 6-9 November 2011.

41. Angelo P. Castellani, Mattia Gheda, Nicola Bui, Michele Rossi and Michele Zorzi, Web Services for the Internet of Things through CoAP and EXI, IEEE ICC 2011 Workshop on Embedding the Real World into the Future Internet (RWFI-2011). Kyoto, Japan, 5-9 June, 2011.

42. Cristiano Tapparello, Stefano Tomasin and Michele Rossi, On Interference-Aware Cooperation Policies for Wireless Ad Hoc Networks, IEEE International Conference on Ultra Modern Telecommunications (ICUMT) 2010. Moscow, Russia, 18-20 October, 2010.

43. Giorgio Quer, Davide Zordan, Riccardo Masiero, Michele Zorzi and Michele Rossi, WSN-Control: Signal Reconstruction through Compressive Sensing in Wireless Sensor Networks, IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp) 2010. Denver, Colorado, 11-14 October, 2010.

44. [invited paper] Nicola Bui, Moreno Dissegna, Michele Rossi, Osman Ugus and Michele Zorzi, An Integrated System for Secure Code Distribution in Wireless Sensor Networks, Sixth IEEE PerCom Workshop on Pervasive Wireless Networking (PWN) 2010, Mannheim, Germany, April 2, 2010.

45. Angelo P. Castellani, Nicola Bui, Paolo Casari, Michele Rossi, Zach Shelby and Michele Zorzi, Architecture and Protocols for the Internet of Things: A Case Study, First International Workshop on the Web of Things (WoT) 2010, Mannheim, Germany, March 29-April 2, 2010.

46. Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Jörg Widmer and Michele Zorzi, Data Acquisition through joint Compressive Sensing and Principal Component Analysis, IEEE GLOBECOM 2009, Honolulu, Hawaii, US, Nov. 30-Dec. 4, 2009.

47. Riccardo Masiero, Giorgio Quer, Michele Rossi, Michele Zorzi, A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks, IEEE SASN 2009, Saint Petersburg, Russia, Oct. 12-14, 2009.

48. Marco Miozzo and Michele Rossi, Heterogeneous Routing and Composition in Ambient Networking, International Workshop on Cross-Layer Design, IEEE IWCLD 2009, Palma de Mallorca, Spain, June 11-12, 2009.

49. Riccardo Masiero, Daniele Munaretto, Michele Rossi, Jörg Widmer and Michele Zorzi, A Note on the Buffer Overlap Among Nodes Performing Random Network Coding in Wireless Ad Hoc Networks, IEEE VTC-Spring 2009, Barcelona, Spain, Apr. 26-29, 2009.

50. [invited paper] Giorgio Quer, Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer and Michele Zorzi, On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks, Workshop on Information Theory and Applications, Information Theory and Applications Workshop (ITA) 2009, San Diego, CA, US, Feb. 8-13, 2009.

51. Paolo Casari, Michele Rossi and Michele Zorzi, Fountain Codes and their Application to Broadcasting in Underwater Networks: Performance Modeling and Relevant Tradeoffs, ACM WUWNet 2008, San Francisco, CA, US, Sept. 5, 2008.

52. Michele Rossi, Giovanni Zanca, Luca Stabellini, Riccardo Crepaldi, Albert F. Harris III, and Michele Zorzi, SYNAPSE: A Network Reprogramming Protocol for Wireless Sensor Networks using Fountain Codes, IEEE SECON 2008, San Francisco, California, US. June 16-20, 2008.

53. Marco Miozzo, Michele Rossi and Michele Zorzi, Architectures for Seamless Handover Support in Heterogeneous Wireless Networks, IEEE WCNC 2008, Las Vegas, Nevada, US. Mar. 31-Apr. 3, 2008.

54. Daniele Munaretto, Jörg Widmer, Michele Rossi and Michele Zorzi, Resilient Coding Algorithms for Sensor Network Data Persistence, EWSN 2008, Bologna, Italy. Jan. 30-Feb. 1, 2008. (Also published in the Springer Lecture Notes in Computer Science (LNCS), Vol. 4913/2008)

55. [invited paper] Paolo Casari, Michele Rossi and Michele Zorzi, Towards Optimal Broadcasting Policies for HARQ based on Fountain Codes in Underwater Networks, IEEE WONS 2008, Garmisch-Partenkirchen, Germany. Jan. 23-25, 2008.

56. Elena Fasolo, Michele Rossi, Jörg Widmer and Michele Zorzi, A Proactive Network Coding Strategy for Pervasive Wireless Networking, IEEE GLOBECOM, Washington, DC, US. Nov. 26-30, 2007.

57. Leonardo Badia, Nicola Bui, Marco Miozzo, Michele Rossi and Michele Zorzi, Mobility Aided Routing in Multi-hop Heterogeneous Networks with Group Mobility, IEEE GLOBECOM, Washington, DC, US. Nov. 26-30, 2007. Best Paper Award

58. [invited paper] Albert F. Harris III, Marco Miozzo, Michele Rossi and Michele Zorzi, Performance Improvements in Ad Hoc Networks Through Mobility Groups and Channel Diversity, WICON 2007, Austin, Texas, US. Oct. 22-24, 2007.

59. Nicola Baldo, Federico Maguolo, Marco Miozzo, Michele Rossi and Michele Zorzi, ns2-MIRACLE: a Modular Framework for Multi-Technology and Cross-Layer Support in Network Simulator 2, ACM NSTools, Nantes, France. Oct. 22, 2007.

60. Elena Fasolo, Michele Rossi, Jörg Widmer and Michele Zorzi, On MAC Scheduling and Packet Combination Strategies for Practical Random Network Coding, IEEE ICC, Glasgow, Scotland, UK. June 24-28, 2007.

61. Riccardo Crepaldi, Simone Friso, Albert F. Harris III, Michele Mastrogiovanni, Chiara Petrioli, Michele Rossi, Andrea Zanella and Michele Zorzi, The Design, Deployment, and Analysis of SignetLab: A Sensor Network Testbed and Interactive Management Tool, IEEE Tridentcom , Orlando, Florida, US. May 21-23, 2007.

62. Michele Rossi, Nicola Bui and Michele Zorzi, Cost and Collision Minimizing Forwarding Schemes for Wireless Sensor Networks, IEEE INFOCOM, Anchorage, Alaska, US. May 6-12, 2007.

63. Daniele Munaretto, Jörg Widmer, Michele Rossi and Michele Zorzi, Network Coding Strategies for Data Persistence in Static and Mobile Sensor Networks, International Workshop on Wireless Networks: Communication, Cooperation and Competition (WNC^3), Limassol, Cyprus. Apr. 16, 2007.

64. Michele Mastrogiovanni, Chiara Petrioli, Michele Rossi, Andrea Vitaletti and Michele Zorzi, Integrated Data Delivery and Interest Dissemination Techniques for Wireless Sensor Networks, IEEE GLOBECOM, San Francisco, CA, US. Nov. 27-Dec. 1, 2006.

65. Marco Miozzo, Michele Rossi and Michele Zorzi, Routing Strategies for Coverage Extension in Heterogeneous Wireless Networks, IEEE PIMRC, Helsinki, Finland. Sept. 11-14, 2006.

66. Leonardo Badia, Nicola Bui, Marco Miozzo, Michele Rossi and Michele Zorzi, On the Exploitation of User Aggregation Strategies in Heterogeneous Wireless Networks, IEEE CAMAD, Trento, Italy, June 8-9, 2006. Best Paper Award

67. [invited paper] Elena Fasolo, Christian Prehofer, Michele Rossi, Qing Wei, Jörg Widmer, Andrea Zanella and Michele Zorzi, Challenges and new approaches for efficient data gathering and dissemination in pervasive wireless networks, InterSense, Nice, France. May 30-31, 2006.

68. Michele Rossi, Ramesh R. Rao and Michele Zorzi, Cost Efficient Routing Strategies over Virtual Coordinates for Wireless Sensor Networks, IEEE GLOBECOM, St. Louis, MO, US. Nov. 20-Dec. 2, 2005.

69. Leonardo Badia, Michele Rossi and Michele Zorzi, Queueing and Delivery Analysis of SR ARQ on Markov Channels with Non-instantaneous Feedback, IEEE GLOBECOM, St. Louis, MO, US. Nov. 20-Dec. 2, 2005.

70. Michele Rossi, Leonardo Badia, Nicola Bui and Michele Zorzi, On Group Mobility Patterns and their Exploitation to Logically Aggregate Terminals in Wireless Networks, IEEE VTC Fall, Dallas, Texas, US. Sept. 25-28, 2005.

71. Sebastiaan Blom, Carlo Bellettini, Anna Sinigalliesi, Luca Stabellini, Michele Rossi and Gianluca Mazzini, Transmission Power Measurements for Wireless Sensor Nodes and their Relationship to the Battery Level, IEEE ISWCS, Siena, Italy. Sept. 5-7, 2005.

72. Leonardo Badia, Michele Rossi and Michele Zorzi, On the Statistics of Delay Terms in SR ARQ on Markov Channels, IEEE ISWCS, Siena, Italy. Sept. 5-7, 2005.

73. Michele Rossi and Michele Zorzi, Probabilistic Algorithms for Cost-based Integrated MAC and Routing in Wireless Sensor Networks, Third International Workshop on Measurement, Modeling, and Performance Analysis of Wireless Sensor Networks (SenMetrics), San Diego, CA, US. July 21, 2005. (In conjunction with MobiQuitous 2005).

74. [invited paper] Michele Rossi and Michele Zorzi, Cost Efficient Localized Geographical Forwarding Strategies for Wireless Sensor Networks, Tyrrhenian International Workshop on Digital Communications (TIWDC) 2005, Sorrento, Italy. July 4-6, 2005. (Also published in the book: "Distributed Cooperative Laboratories: Networking, Instrumentation and Measurements", Springer 2006. F. Davoli, S. Palazzo, S. Zappatore (Eds.))

75. Michele Rossi, Leonardo Badia, Paolo Giacon and Michele Zorzi, On the Effectiveness of Logical Device Aggregation in Multi-radio Multi-hop Networks, IEEE MobiWac, Maui, Hawaii, US. June 13-16, 2005. Best Paper Award

76. Abigail Surtees, Ramon Aguero, Jari Tenhunen, Michele Rossi and Daniel Hollos, Routing Group Formation in Ambient Networks, 14th IST Mobile & Wireless Communications Summit, Dresden, Germany. June 19-23, 2005.

77. Nicola Baldo, Andrea Odorizzi and Michele Rossi, Buffer Control Strategies for the Transmission of TCP Flows over Geostationary Satellite Links Using Proxy-Based Architectures, IEEE VTC Spring. Stockholm, Sweden. May 30-June 1, 2005.

78. Michele Rossi, Paolo Casari, Marco Levorato and Michele Zorzi, Multicast Streaming over 3G Cellular Networks through Multi-Channel Transmissions: Proposals and Performance Evaluation, IEEE WCNC. New Orleans, Louisiana, US. Mar. 13-17, 2005.

79. Michele Rossi, Leonardo Badia and Michele Zorzi, SR-ARQ Delay Statistics on N-State Markov Channels with finite Round Trip Delay, IEEE GLOBECOM. Dallas, Texas, US. Nov. 29-Dec. 3, 2004.

80. Michele Rossi, Michele Zorzi and Frank H.P. Fitzek, Link Layer Algorithms for Efficient Multicast Service Provisioning in 3G Cellular Systems, IEEE GLOBECOM. Dallas, Texas, US. Nov. 29-Dec. 3, 2004.

81. Michele Rossi, Michele Zorzi and Frank H.P. Fitzek, Investigation of Link Layer Algorithms and Play-Out Buffer Requirements for Efficient Multicast Services in 3G Cellular Systems, IEEE PIMRC. Barcelona, Spain. Sept. 5-8, 2004.

82. Michele Rossi, Leonardo Badia and Michele Zorzi, Exact statistics of ARQ packet delivery delay over Markov channels with finite round-trip delay, IEEE GLOBECOM, San Francisco, CA, US. Dec. 1-5, 2003.

83. Michele Rossi, Lorenzo Scaranari and Michele Zorzi, On the UMTS RLC Parameters Setting and their Impact on Higher Layers Performance, VTC Fall, Orlando, Florida, US. Oct. 6-9, 2003.

84. [invited paper] Michele Rossi, Frank H.P. Fitzek and Michele Zorzi, Error Control Techniques for Efficient Multicast Streaming in UMTS Networks, SCI Conference, Orlando, Florida, US. July 27-30, 2003.

85. Michele Rossi and Michele Zorzi, An Accurate Heuristic Approach for UMTS RLC Delay Statistics Evaluation, IEEE VTC Spring 2003, Jeju, Korea. Apr. 22-25, 2003.

86. Michele Rossi, Leonardo Badia and Michele Zorzi, Accurate Approximation of ARQ Packet Delay Statistics over Markov Channels with Finite Round-Trip Delay, IEEE WCNC. Louisiana, New Orleans, US. Mar. 16-20, 2003.

87. Michele Rossi, Leonardo Badia and Michele Zorzi, On the Delay Statistics of an Aggregate of SR-ARQ Packets over Markov Channels with Finite Round-Trip Delay, IEEE WCNC. Louisiana, New Orleans, US. Mar. 16-20, 2003.

88. Aldo Roveri, Carla F. Chiasserini, Mauro Femminella, Tommaso Melodia, Giacomo Morabito, Michele Rossi and Ilenia Tinnirello, The RAMON Module: Architecture Framework and Performance Results, Proccedings of 2nd international workshop on QoS in Multiservice IP Networks (QoS-IP), Milano (Italy). Feb. 24-26, 2003. (Also published in the Springer Lecture Notes in Computer Science (LNCS), Vol. 2601/2003)

89. Giacomo Morabito, Sergio Palazzo, Michele Rossi and Michele Zorzi, Improving End-To-End Performance in Reconfigurable Networks through Dynamic Setting of TCP Parameters, Proceedings of 2nd international workshop on QoS in Multiservice IP Networks (QoS-IP), Milano (Italy). Feb. 24-26, 2003. (Also published in the Springer Lecture Notes in Computer Science (LNCS), Vol. 2601/2003)

90. Davide Adami, Mario Marchese, Giacomo Morabito, Michele Rossi and Luca Veltri, Transport Protocol and Resource Management for Satellite Networks: Framework of a Project, 5th European workshop on Mobile/Personal Satcoms (EMPS), Baveno-Stresa, Lake Maggiore, Italy. Sept. 25-26, 2002.

91. Alessandra Giovanardi, Gianluca Mazzini and Michele Rossi, Analysis and Optimization of a Transparent Multicast Mobility Support in Cellular Systems, IEEE ICC, New York, US. Apr. 28-May 2, 2002.

92. Michele Zorzi, Michele Rossi and Gianluca Mazzini, Performance of TCP on a Wideband CDMA Air Interface, Tyrrhenian International Workshop on Digital Communications (TIWDC), Taormina, Italy. Sept. 17-20, 2001. (Also published in the Springer Lecture Notes in Computer Science (LNCS), Vol. 2170/2001)

93. Michele Rossi, Alessandra Giovanardi, Michele Zorzi and Gianluca Mazzini, TCP/IP Header Compression: Proposal and Performance Investigation on a WCDMA Air Interface, IEEE PIMRC, San Diego, CA, US. Sept. 30-Oct. 3, 2001.

94. Alessandra Giovanardi, Gianluca Mazzini and Michele Rossi, An Agent-based Approach for Multicast Applications in Mobile Wireless Networks, IEEE GLOBECOM, San Francisco, CA, US. Nov. 27-Dec. 1, 2000.