A dynamic approach to rebalancing bike-sharing systems

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule

Joint Optimization of Energy Efficiency and Data Compression in TDMA-Based Medium Access Control for the IoT - Extended Version

Energy efficiency is a key requirement for the Internet of Things, as many sensors are expected to be completely stand-alone and able to run for years without battery replacement. Data compression aims at saving some energy by reducing the volume of data sent over the network, but also affects the quality of the received information. In this work, we formulate an optimization problem to jointly design the source coding and transmission strategies for time-varying channels and sources, with the twofold goal of extending the network lifetime and granting low distortion levels. We propose a scalable offline optimal policy that allocates both energy and transmission parameters (i.e., times and powers) in a network with a dynamic Time Division Multiple Access (TDMA)-based access scheme.Comment: 8 pages, 4 figures, revised and extended version of a paper that was accepted for presentation at IEEE Int. Workshop on Low-Layer Implementation and Protocol Design for IoT Applications (IoT-LINK), GLOBECOM 201

Platforms and Protocols for the Internet of Things

Building a general architecture for the Internet of Things (IoT) is a very complex task, exacerbated by the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we identify the main blocks of a generic IoT architecture, describing their features and requirements, and analyze the most common approaches proposed in the literature for each block. In particular, we compare three of the most important communication technologies for IoT purposes, i.e., REST, MQTT, and AMQP, and we also analyze three IoT platforms: openHAB, Sentilo, and Parse. The analysis will prove the importance of adopting an integrated approach that jointly addresses several issues and is able to flexibly accommodate the requirements of the various elements of the system. We also discuss a use case which illustrates the design challenges and the choices to make when selecting which protocols and technologies to use

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

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

3D Chocolate Printer Dropper

The Mechanical Engineering Department at Washington University in St. Louis is working to stimulate interest in the fields of fluid dynamics and thermal sciences, as students are not typically exposed to these topics within the first two years of school. Dr. Okamoto, Jeff Krampf, and Dr. Weisensee of the Mechanical Engineering Department would like to remedy this situation by developing a laboratory experiment for first year students that utilizes a 3D chocolate printer to teach thermal-fluid concepts in a fun and engaging manner. The goal of this project is to build a chocolate droplet dispensing system, which is a part of the 3D chocolate printing machine. The device must be able to melt chocolate and generate droplets in consistent and adjustable time intervals. The dispensing height of the nozzle should be manually changeable so that the students can understand how height and frequency influence the droplet impact. While the primary function of this device is to help students learn thermal-fluids in a fun yet educational environment, it is also imperative that the device is safe for students to use

Channel Access in Wireless Networks: Protocol Design of Energy-Aware Schemes for the IoT and Analysis of Existing Technologies

The design of channel access policies has been an object of study since the deployment of the first wireless networks, as the Medium Access Control (MAC) layer is responsible for coordinating transmissions to a shared channel and plays a key role in the network performance. While the original target was the system throughput, over the years the focus switched to communication latency, Quality of Service (QoS) guarantees, energy consumption, spectrum efficiency, and any combination of such goals. The basic mechanisms to use a shared channel, such as ALOHA, TDMA- and FDMA-based policies, have been introduced decades ago. Nonetheless, the continuous evolution of wireless networks and the emergence of new communication paradigms demand the development of new strategies to adapt and optimize the standard approaches so as to satisfy the requirements of applications and devices. This thesis proposes several channel access schemes for novel wireless technologies, in particular Internet of Things (IoT) networks, the Long-Term Evolution (LTE) cellular standard, and mmWave communication with the IEEE802.11ad standard. The first part of the thesis concerns energy-aware channel access policies for IoT networks, which typically include several battery-powered sensors. In scenarios with energy restrictions, traditional protocols that do not consider the energy consumption may lead to the premature death of the network and unreliable performance expectations. The proposed schemes show the importance of accurately characterizing all the sources of energy consumption (and inflow, in the case of energy harvesting), which need to be included in the protocol design. In particular, the schemes presented in this thesis exploit data processing and compression techniques to trade off QoS for lifetime. We investigate contention-free and contention-based chanel access policies for different scenarios and application requirements. While the energy-aware schemes proposed for IoT networks are based on a clean-slate approach that is agnostic of the communication technology used, the second part of the thesis is focused on the LTE and IEEE802.11ad standards. As regards LTE, the study proposed in this thesis shows how to use machine-learning techniques to infer the collision multiplicity in the channel access phase, information that can be used to understand when the network is congested and improve the contention resolution mechanism. This is especially useful for massive access scenarios; in the last years, in fact, the research community has been investigating on the use of LTE for Machine-Type Communication (MTC). As regards the standard IEEE802.11ad, instead, it provides a hybrid MAC layer with contention-based and contention-free scheduled allocations, and a dynamic channel time allocation mechanism built on top of such schedule. Although this hybrid scheme is expected to meet heterogeneous requirements, it is still not clear how to develop a schedule based on the various traffic flows and their demands. A mathematical model is necessary to understand the performance and limits of the possible types of allocations and guide the scheduling process. In this thesis, we propose a model for the contention-based access periods which is aware of the interleaving of the available channel time with contention-free allocations

Enabling LTE RACH Collision Multiplicity Detection via Machine Learning

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of machine-type traffic. Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, thereby increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that outperforms the state-of-the-art schemes in preamble detection for the LTE RACH procedure. Most importantly, our scheme can also estimate the collision multiplicity, and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. The presented approach is applicable to novel 3GPP standards that target massive IoT, e.g., LTE-M and NB-IoT.Comment: Submitted to IEEE GLOBECOM 201

Phase précoce des troubles psychotiques :Etude de corrélation entre l'évaluation neurocognitive et les données métaboliques.

Introduction : La schizophrénie est une maladie récurrente dans notre société et touche près d'1% de la population. Le premier accès de psychose survient en général entre 18 et 25 ans chez les hommes et entre 24 et 35 ans chez les femmes. Les symptômes sont classés en quatre sous- groupes, (1) les symptômes positifs comprenant les hallucinations, délires, troubles de perception, troubles de la pensée et (2) les symptômes négatifs qui sont les affects aplatis, l'apathie et le retrait social, (3) les symptômes de base qui consistent en troubles perceptifs, moteurs et des émotions et enfin (4) les symptômes cognitifs tels que des troubles de l'attention, de la mémoire et des fonctions exécutives, qui surviennent dans 60 à 80% des cas. La maladie est fréquemment accompagnée de co-morbidités (dépression, abus de substances, troubles obsessionnels compulsifs, anxiété). L'évolution à long terme diffère selon les patients, 35% évolueront de manière chronique et avec une aggravation progressive du déficit, 35% évolueront vers une chronicité de la maladie mais sans atteinte résiduelle, 8% évolueront de manière chronique avec la persistance de symptômes résiduels et enfin on observera une rémission complète après le premier épisode psychotique sans handicap résiduel chez 22% des patients. Les recherches concernant la schizophrénie ont fait une avancée considérable ces vingt dernières années, que cela soit par la définition plus précise des troubles cognitifs ou encore par la mise en évidence de certaines substances neurobiologiques, qui se trouvent dérégulées chez les patients atteints de la maladie. C'est le cas du glutathion (GSH) ainsi que des enzymes et protéines intervenant dans son métabolisme. Il persiste cependant encore beaucoup d'inconnues, et une meilleure connaissance des mécanismes biologiques opérant dans la phase précoce des psychoses contribuerait de façon certaine à une amélioration de l'identification et de la prise en charge des patients pendant la phase prodromique de la maladie et permettrait le développement de cibles pharmacologiques plus précises. Objectifs : Ce travail consiste en une analyse de données récoltées par deux axes de recherche de l'étude sur les bio-marqueurs dans la phase précoce des troubles psychotiques effectuée actuellement au Centre de Neurosciences Psychiatriques, à savoir d'une part l'identification de marqueurs neurocognitifs précoces sur la base d'une série de tests neurocognitifs évaluant (1) la vitesse de traitement de l'information, (2) l'attention et la vigilance, (3) la mémoire de travail, (4) l'apprentissage verbal, (5) l'apprentissage visuel et (6) le raisonnement et la résolution de problèmeet d'autre part l'identification de bio-marqueurs métaboliques associés aux phases précoces de la maladie. Dans cette étude, les patients sont comparés à un groupe d'individus contrôles et les questions suivantes sont posées : « « Dans une population de patients en premier épisode psychotique, les performances neurocognitives sont-elles significativement amoindries comparé à un groupe d'individus contrôles ? » et « Dans cette même population, les déficits neurocognitifs survenant dans la phase de psychose débutante sont-ils en corrélation avec des variations de biomarqueurs métaboliques ? ». Méthodes : Dans cette étude, nous comparons un échantillon de 30 patients provenant d'une cohorte de patients souffrant de psychose émergente (Programme TIPP, Lausanne) à un échantillon de 30 sujets contrôles. L'évaluation neurocognitive des patients et des sujets contrôles a été réalisée par des tests neuropsychologiques s'inspirant de la batterie cognitive MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia). Les données biologiques proviennent (1) de la culture de fibroblastes dérivés de biopsies de peau prélevées auprès de chaque patient et individu contrôle, dont le métabolisme cellulaire a été caractérisé dans des conditions basales, ou après l'ajout de tert-butylhydroquinone (t-BHQ) qui induit un stress oxydatif ; (2) de l'analyse métabolique de prélèvements sanguins également effectués auprès de chaque patient et contrôle et enfin (3) du taux de glutathion mesuré par imagerie par résonance magnétique spectroscopique (MRS). L'analyse et le croisement de ces bases de données ont été faite à l'aide du logiciel SPSS. Résultats et conclusion : Les performances neurocognitives de l'échantillon de patients sont significativement diminuées par rapport au groupe d'individus contrôles, et pour chacun des domaines neurocognitifs. La différence est la plus grande pour les tests HVLT-R (apprentissage verbal) et les tests BACS-SC et TMT-A (vitesse de traitement). Concernant la deuxième partie du travail, (1) un déficit dans les domaines neurocognitifs de l'attention/vigilance (CPT-IP) et la mémoire de travail verbale (WMS-III) est corrélé avec un taux de glutathion sanguin total élevé (p-value = 0.03 et 0.02) ; (2) un déficit dans la vitesse de traitement (TMT-A) est corrélé à un taux de GSH cérébral diminué (p-value=0.047) et (3) un déficit dans le domaine du raisonnement et de la résolution de problème (NAB lab) est corrélé avec une augmentation de l'ARN messager codant pour la protéine Nrf2 au niveau cellulaire (p=0.022). Selon ces résultats, le GSH sanguin total, le GSH cérébral et le Nrf2 pourraient être des bio-marqueurs permettant d'identifier les patients dans la phase précoce de la maladie et leurs mécanismes biologiques pourraient constituer des cibles spécifiques dans le développement de traitements futurs