Internal Model Hop-by-hop Congestion Control for High-Speed Networks

This paper presents a hop-by-hop congestion control for highspeed networks. The control policy relies on the data exchange between adjacent nodes of the network (nearest-neighbour interaction). The novelty of this paper consists in the extensive use of Internal Model Control (IMC) to set the rates of the traffic flows. As a result, the proposed congestion control provides upper-bounds of the queue lengths in all the network buffers (overflow avoidance), avoids wasting the assigned capacity (full link utilisation) and guarantees the congestion recovery. Numerical simulations prove the effectiveness of the scheme

Wardrop Equilibrium in Discrete-Time Selfish Routing with Time-Varying Bounded Delays

This paper presents a multi-commodity, discrete- time, distributed and non-cooperative routing algorithm, which is proved to converge to an equilibrium in the presence of heterogeneous, unknown, time-varying but bounded delays. Under mild assumptions on the latency functions which describe the cost associated to the network paths, two algorithms are proposed: the former assumes that each commodity relies only on measurements of the latencies associated to its own paths; the latter assumes that each commodity has (at least indirectly) access to the measures of the latencies of all the network paths. Both algorithms are proven to drive the system state to an invariant set which approximates and contains the Wardrop equilibrium, defined as a network state in which no traffic flow over the network paths can improve its routing unilaterally, with the latter achieving a better reconstruction of the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach

Chance-Constrained Control with Lexicographic Deep Reinforcement Learning

This paper proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes, in which the controller seeks to ensure that the probability of satisfying the constraint is above a given threshold. Standard DeepRL approaches require i) the constraints to be included as additional weighted terms in the cost function, in a multi-objective fashion, and ii) the tuning of the introduced weights during the training phase of the Deep Neural Network (DNN) according to the probability thresholds. The proposed approach, instead, requires to separately train one constraint-free DNN and one DNN associated to each constraint and then, at each time-step, to select which DNN to use depending on the system observed state. The presented solution does not require any hyper-parameter tuning besides the standard DNN ones, even if the probability thresholds changes. A lexicographic version of the well-known DeepRL algorithm DQN is also proposed and validated via simulations

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

Robust Adaptive Congestion Control for Next Generation Networks

This paper deals with the problem of congestion control in a next-generation heterogeneous network scenario. The algorithm runs in the 'edge' routers (the routers collecting the traffic between two different networks) with the aim of avoiding congestion in both the network and the edge routers. The proposed algorithm extends congestion control algorithms based on the Smith's principle: i) the controller, by exploiting on-line estimates via probe packets, adapts to the delay and rate variations; ii) the controller assures robust stability in the presence of time-varying delays

Control-Based Resource Management Procedures for Satellite Networks

This paper describes the resource management of a DVBRCS geostationary satellite network. The functional modules of the access layer aim at efficiently exploiting the link resources while assuring the contracted Quality of Service (QoS) to the traffic entering the satellite network. The main novelty is the integration between the Connection Admission Control and the Congestion Control procedures. Both them exploit the estimation of the traffic load, performed by a Kalman filter. The proposed solution has been analysed via computer simulations, which confirmed their effectiveness

¿Los sujetos con obesidad subestiman su tamaño corporal? Una revisión narrativa de los métodos de estimación y teorías explicativas

The widespread of overweight and obesity in the developed countries is a real societal issue, nevertheless a considerable amount of subjects with obesity do not recognize their condition. Researchers used different methods to assess body size perception by obese subjects and the results show that while some subjects with obesity estimate accurately or overestimate their body size, others underestimate their weight and their body size measures. A failure to identify overweight or obesity has serious consequences on the subject's health, as it is widely recognised that self-awareness is the first step to engage in a rehabilitation program. The spread of obesity underestimation and its implications make the case for a new hypothetical body image disorder, which has been called Fatorexia (TM). It consists in the significant underestimation of body size by subjects with obesity, as they are unable or unwilling to acknowledge their condition. Some researchers proposed a social explanation to the underestimation phenomenon, but here an alternative hypothesis, the Allocentric Lock Theory (ALT), is outlined to describe the mechanisms behind the underestimation of body size by subjects with obesity

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Bellman's principle of optimality and deep reinforcement learning for time-varying tasks

This paper presents the first framework (up to the authors' knowledge) to address time-varying objectives in finite-horizon Deep Reinforcement Learning (DeepRL), based on a switching control solution developed on the ground of Bellman's principle of optimality. By augmenting the state space of the system with information on its visit time, the DeepRL agent is able to solve problems in which its task dynamically changes within the same episode. To address the scalability problems caused by the state space augmentation, we propose a procedure to partition the episode length to define separate sub-problems that are then solved by specialised DeepRL agents. Contrary to standard solutions, with the proposed approach the DeepRL agents correctly estimate the value function at each time-step and are hence able to solve time-varying tasks. Numerical simulations validate the approach in a classic RL environment

Oxygen measurement in interstitially perfused cellularized constructs cultured in a miniaturized bioreactor

Aims The possibility of developing engineered tissue in vitro and maintaining the cell viability and functionality is primarily related to the possibility of controlling key culture parameters such as oxygen concentration and cell-specific oxygen consumption. We measured these parameters in a three-dimensional (3D) cellularized construct maintained under interstitially perfused culture in a miniaturized bioreactor. Methods MG63 osteosarcoma cells were seeded at high density on a 3D polystyrene scaffold. The 3D scaffolds were sensorized with sensor foils made of a polymer, which fluoresce with intensity proportional to the local oxygen tension. Images of the sensor foil in contact with the cellularized construct were acquired with a video camera every four hours for six culture days and were elaborated with analytical imaging software to obtain oxygen concentration maps. Results The data collected indicate a globally decreasing oxygen concentration profile, with a total drop of 28% after six days of culture and an average drop of 10.5% between the inlet and outlet of the perfused construct. Moreover, by importing the measured oxygen concentration data and the cell counts in a model of mass transport, we calculated the cell-specific oxygen consumption over the whole culture period. The consumption increased with oxygen availability and ranged from 0.1 to 0.7 µmol/h/106 cells. Conclusions The sensors used here allowed a non-invasive, contamination-free and non-destructive oxygen measurement over the whole culture period. This study is the basis for optimization of the culture parameters involved in oxygen supply, in order to guarantee maintenance of cell viability in our system. </jats:sec