On the Effects of Battery Imperfections in an Energy Harvesting Device

Energy Harvesting allows the devices in a Wireless Sensor Network to recharge their batteries through environmental energy sources. While in the literature the main focus is on devices with ideal batteries, in reality several inefficiencies have to be considered to correctly design the operating regimes of an Energy Harvesting Device (EHD). In this work we describe how the throughput optimization problem changes under \emph{real battery} constraints in an EHD. In particular, we consider imperfect knowledge of the state of charge of the battery and storage inefficiencies, \emph{i.e.}, part of the harvested energy is wasted in the battery recharging process. We formulate the problem as a Markov Decision Process, basing our model on some realistic observations about transmission, consumption and harvesting power. We find the performance upper bound with a real battery and numerically discuss the novelty introduced by the real battery effects. We show that using the \emph{old} policies obtained without considering the real battery effects is strongly sub-optimal and may even result in zero throughput.Comment: In Proc. IEEE International Conference on Computing, Networking and Communications, pp. 942-948, Feb. 201

A Bayesian Approach to Sparse plus Low rank Network Identification

We consider the problem of modeling multivariate time series with parsimonious dynamical models which can be represented as sparse dynamic Bayesian networks with few latent nodes. This structure translates into a sparse plus low rank model. In this paper, we propose a Gaussian regression approach to identify such a model

Energy Harvesting Communication System with SOC-Dependent Energy Storage Losses

The popularity of Energy Harvesting Devices (EHDs) has grown in the past few years, thanks to their capability of prolonging the network lifetime. In reality, EHDs are affected by several inefficiencies, e.g., energy leakage, battery degradation or storage losses. In this work we consider an energy harvesting transmitter with storage inefficiencies. In particular, we assume that when new energy has to be stored in the battery, part of this is wasted and the losses depend upon the current state of charge of the device. This is a practical realistic assumption, e.g., for a capacitor, that changes the structure of the optimal transmission policy. We analyze the throughput maximization problem with a dynamic programming approach and prove that, given the battery status and the channel gain, the optimal transmission policy is deterministic. We derive numerical results for the energy losses in a capacitor and show the presence of a \emph{loop effect} that degrades the system performance if the optimal policy is not considered.Comment: In Proc. IEEE Twelfth Int. Symposium on Wireless Communication Systems (ISWCS), pp. 406-410, Aug. 201

The Harmonic Analysis of Kernel Functions

Kernel-based methods have been recently introduced for linear system identification as an alternative to parametric prediction error methods. Adopting the Bayesian perspective, the impulse response is modeled as a non-stationary Gaussian process with zero mean and with a certain kernel (i.e. covariance) function. Choosing the kernel is one of the most challenging and important issues. In the present paper we introduce the harmonic analysis of this non-stationary process, and argue that this is an important tool which helps in designing such kernel. Furthermore, this analysis suggests also an effective way to approximate the kernel, which allows to reduce the computational burden of the identification procedure

Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices with Finite Batteries

One of the main concerns in traditional Wireless Sensor Networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is Ambient \emph{Energy Harvesting} (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is Wireless \emph{Energy Transfer} (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.Comment: 16 pages, 12 figure

Achievable Secrecy Rates of an Energy Harvesting Device

The secrecy rate represents the amount of information per unit time that can be securely sent on a communication link. In this work, we investigate the achievable secrecy rates in an energy harvesting communication system composed of a transmitter, a receiver and a malicious eavesdropper. In particular, because of the energy constraints and the channel conditions, it is important to understand when a device should transmit and to optimize how much power should be used in order to improve security. Both full knowledge and partial knowledge of the channel are considered under a Nakagami fading scenario. We show that high secrecy rates can be obtained only with power and coding rate adaptation. Moreover, we highlight the importance of optimally dividing the transmission power in the frequency domain, and note that the optimal scheme provides high gains in secrecy rate over the uniform power splitting case. Analytically, we explain how to find the optimal policy and prove some of its properties. In our numerical evaluation, we discuss how the maximum achievable secrecy rate changes according to the various system parameters. Furthermore, we discuss the effects of a finite battery on the system performance and note that, in order to achieve high secrecy rates, it is not necessary to use very large batteries.Comment: Accepted for publication in IEEE Journal on Selected Areas in Communications (Mar. 2016

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

Estimating effective connectivity in linear brain network models

Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance Imaging (fMRI), the so-called effective connectivity in brain networks, that is the existing interactions among neuronal populations. This inverse problem is complicated by the fact that the BOLD (Blood Oxygenation Level Dependent) signal measured by fMRI represent a dynamic and nonlinear transformation (the hemodynamic response) of neuronal activity. In this paper, we consider resting state (rs) fMRI data; building upon a linear population model of the BOLD signal and a stochastic linear DCM model, the model parameters are estimated through an EM-type iterative procedure, which alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel (RTS) smoother, updates the connections among neuronal states and refines the parameters of the hemodynamic model; sparsity in the interconnection structure is favoured using an iteratively reweighting scheme. Experimental results using rs-fMRI data are shown demonstrating the effectiveness of our approach and comparison with state of the art routines (SPM12 toolbox) is provided

Online semi-parametric learning for inverse dynamics modeling

This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equa- tion, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot. In doing so we also compare two different techniques, namely cross validation and marginal likelihood optimization, for estimating the hyperparameters of the kernel function