Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded with a discussion of open challenges and areas for future investigation.

An Introduction to Deep Learning for the Physical Layer

https://iris.polito.it/bitstream/11583/2522888/1/SmartCity_Trends_%20paper.pdf

Current trends in Smart City initiatives: some stylised facts

The availability of cheap and easy-to-install sensors is bolstering the development of monitoring applications in IoT scenarios. Although there is a need for periodical measurements of the tracked signals to guarantee an accurate representation at the receiver, choosing an appropriate duration for the reporting window is not trivial. In fact, the energy restrictions of many devices and the interference caused by other users call for longer reporting windows. However, this causes a higher reconstruction error due to the lower sampling resolution, which may be unacceptable in some applications. We propose a probabilistic random channel access scheme for battery-powered devices which monitor time-correlated phenomena and report their measurements to a fusion center. Our goal is to minimize the energy consumption of the sensors, while guaranteeing that the error in the signal estimate at the receiver does not exceed a chosen threshold. We exploit Markov chains and stochastic geometry to characterize the interference caused by the other devices. The numerical evaluation proves that our scheme is scalable and may be used in highly dense scenarios, and that it outperforms other state-of-the-art approaches, which do not consider the impact of interference on both the energy consumption and the accuracy of data representation.

An Interference-Aware Channel Access Strategy for WSNs Exploiting Temporal Correlation

A wide variety of network applications require the use of reliable multicast protocols to disseminate data from one source to a potentially large number of receivers simultaneously. We focus on multicast in single channel multi-access wireless networks and consider Automatic Repeat reQuest (ARQ) based error control algorithms. We propose a simple NACK based method, called overlapped NACKs, that exploits the physical nature of the transmitted NACK signals in order to achieve a high Signal to Noise Ratio (SNR) even in the presence of NACK collisions. As an example, we apply the proposed method to Bluetooth, quantify its reliability and compare the achievable throughput with respect to the multi-unicast case and to an ACK based protocol. Results show that the overlapped NACK method has the potential to substantially increase the throughput over the ACK based protocol and provides good reliability.

/pdf/overlapped-nacks-improving-multicast-performance-in-multi-3rrjhevmty.pdf

Overlapped NACKs: Improving multicast performance in multi-access wireless networks

In this paper, we focus on the average performance experienced by a mobile user while crossing a pico/femtocell as a function of context parameters, such as user speed, pico/femto cell radius, transmit power of main and subsidiary base stations. The analysis is based on a mathematical model that provides an approximate expression of the average Shannon capacity experienced by a mobile user when crossing the femtocell. The optimal handover policy obtained from such a model is then validated through simulations against other policies, showing that a context-aware handover policy may achieve better performance than handover policies based on a more limited set of context parameters.

Context-aware handover in HetNets

In a Flying Ad-Hoc Network, Unmanned Aerial Vehicles (UAVs), (i.e. drones or quadcopters), use wireless communication to exchange data, status updates, and commands between each other and with the control center. However, due to the movement of UAVs, maintaining communication is difficult, particularly when multiple hops are needed to reach the destination. In this work, we propose the Stochastic Multipath UAV Routing for FANETs (SMURF) protocol, which exploits trajectory tracking information from the drones to compute the routes with the highest reliability. SMURF is a centralized protocol, as the control center gathers location updates and sends routing commands following the Software Defined Networking (SDN) paradigm over a separate long-range low bitrate technology such as LoRaWAN. Additionally, SMURF exploits multiple routes, to increase the probability that at least one of the routes is usable. Simulation results show a significant reliability improvement over purely distance-based routing, and that just 3 routes are enough to achieve performance very close to oracle-based routing with perfect information.

SMURF: Reliable Multipath Routing in Flying Ad-Hoc Networks

Recently, millimeter wave (mmWave) bands have been investigated as a means to enhance automated driving and address the challenging data rate and latency demands of emerging automotive applications. For the development of those systems to operate in bands above 6 GHz, there is a need to have accurate channel models able to predict the peculiarities of the vehicular propagation at these bands, especially as far as Vehicle-to-Vehicle (V2V) communications are concerned. In this paper, we validate the channel model that the 3GPP has proposed for NR-V2X systems, which (i) supports deployment scenarios for urban/highway propagation, and (ii) incorporates the effects of path loss, shadowing, line of sight probability, and static/dynamic blockage attenuation. We also exemplify the impact of several automotive-specific parameters on the overall network performance considering realistic system-level simulation assumptions for typical scenarios. Finally, we highlight potential inconsistencies of the model and provide recommendations for future measurement campaigns in vehicular environments.

Andrea Zanella

Papers

An Interference-Aware Channel Access Strategy for WSNs Exploiting Temporal Correlation

Overlapped NACKs: Improving multicast performance in multi-access wireless networks

Context-aware handover in HetNets

SMURF: Reliable Multipath Routing in Flying Ad-Hoc Networks

Path Loss Models for V2V mmWave Communication: Performance Evaluation and Open Challenges.