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. Nicola Piovesan, Angel Fernandez Gambin, Marco Miozzo, Michele Rossi, Paolo Dini, Energy Sustainable Paradigms and Methods for Future Mobile Networks: a Survey. Submitted.

  2. Mohsen Hooshmand, Davide Zordan, Tommaso Melodia, Michele Rossi, SURF: Subject-adaptive Unsupervised signal compression for weaRable Fitness monitors. Submitted.

  3. Matteo Gadaleta, Federico chiariotti, Michele Rossi, Andrea Zanella, D-DASH: a Deep Q-learning Framework for DASH Video Streaming. Submitted.

  4. Matteo Gadaleta, Michele Rossi, IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks. Submitted. [arXiv:1606.03238v2]

  5. Riccardo Bonetto, Michele Rossi, Stefano Tomasin, Carlo Fischione, Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids. Submitted.

  6. 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. PP, No. 99, 23 May 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.
  7. 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. PP, No. 99, 29 March 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.
  8. 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.
  9. 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.
  10. 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).
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. Angelo P. Castellani, Michele Rossi, Michele Zorzi, Back Pressure Congestion Control for CoAP/6LoWPAN Networks. Elsevier Ad Hoc Networks - Sprecial 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.
  19. 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.
  20. 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.
  21. Additional material:
  22. 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.
  23. 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.
  24. WORLDSENSING
    SYNAPSE++ has been integrated into WSN products from WORLDSENSING for the fast deployment and over the air software update of their WSN solutions!
  25. 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.

  26. 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.

  27. 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

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. [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, Field of Communications Systems: http://www.comsoc.org/~awards/best.html).
    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.
  33. 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.

  34. 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.

  35. 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.

  36. 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.

  37. 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.

  38. 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.

  39. 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.

  40. 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.

  41. 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.

  42. 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.

  43. 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.

  44. 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.

  45. 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. 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.

  2. 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.

  3. 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.

  4. 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. Maria Scalabrin, Matteo Gadaleta, Riccardo Bonetto, Michele Rossi, A Bayesian Forecasting and Anomaly Detection Framework for Vehicular Monitoring Networks, Submitted, 2017.

  2. Riccardo Bonetto, Alberto Lanaro, Simone Milani, Michele Rossi, Unsupervised and Multi-Modal Gait Analysis through Growing Neural Gas Networks, Submitted, 2017.

  3. Angel Fernandez Gambin, Michele Rossi, Energy Cooperation for Sustainable Base Station Deployments: Principles and Algorithms, Submitted, 2017.

  4. Davide Zordan, Raul Parada Medina, Michele Rossi, Michele Zorzi, Automatic Rate-Distortion Classification for the IoT: Towards Signal-Adaptive Network Protocols, Submitted, 2017.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.)

  21. 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.

  22. 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]

  23. 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.

  24. 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.

  25. 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.

  26. 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.

  27. 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. [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.

  33. 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.

  34. 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.

  35. 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.

  36. 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.

  37. 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.

  38. [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.

  39. 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.

  40. 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.

  41. 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.

  42. 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)

  43. [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.

  44. 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.

  45. [best paper award] 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.

  46. [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.

  47. 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.

  48. 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.

  49. 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.

  50. 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.

  51. 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.

  52. 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.

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

  54. [best paper award] 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.

  55. [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.

  56. 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.

  57. 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.

  58. 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.

  59. 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.

  60. 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.

  61. 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).

  62. [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.))

  63. [best paper award] 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.

  64. 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.

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. [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.

  73. 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.

  74. 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.

  75. 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.

  76. 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)

  77. 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)

  78. 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.

  79. 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.

  80. 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)

  81. 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.

  82. 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.

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