Davide Zordan

On the Performance of Lossy Compression Schemes for Energy Constrained Sensor Networking

Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, 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 multihop 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.

Modeling and Generation of Space-Time Correlated Signals for Sensor Network Fields

In the past few years, a large number of networking protocols for data gathering through aggregation, compression and recovery in Wireless Sensor Networks (WSNs) have utilized the spatio-temporal statistics of real world signals in order to achieve good performance in terms of energy savings and improved signal reconstruction accuracy. However, very little has been said in terms of suitable spatio-temporal models of the signals of interest. These models are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering algorithms. In this paper, we address this gap by devising a mathematical model for real world signals that are correlated in space and time. We thus describe a method to reproduce synthetic signals with tunable correlation characteristics and we verify, through analysis and comparison against large data sets from real world testbeds, that our model is accurate in reproducing the signal statistics of interest.

SolarStat: Modeling photovoltaic sources through stochastic Markov processes

In this paper, we present a methodology and a tool to derive simple and accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems that include these devices.The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab™ and is available under the GPL license at [1].

On the Design of Temporal Compression Strategies for Energy Harvesting Sensor Networks

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.

WSN-Control: Signal reconstruction through Compressive Sensing in Wireless Sensor Networks

The main contribution of this paper is the implementation and experimental evaluation of a signal reconstruction framework for Wireless Sensor Networks (WSNs). We design WSN-Control, an architecture to control a WSN from an external server connected to the Internet. Within such architecture, we implement a compression and recovery technique that combines Principal Component Analysis (PCA) and Compressive Sensing (CS) to reconstruct signals with many components from a sensor field through the collection of a relatively small number of samples, i.e., through incomplete representations of the actual signal. Overall, our experimental results show that a careful use of CS recovery is effective and can lead to a fully automated system for data gathering and reconstruction of real world and non-stationary signals in WSNs. In detail, WSN-Control effectively recovers signals showing some temporal and/or spatial correlation, from a relatively small number of samples, even below 20%, keeping the relative reconstruction error smaller than 5 · 10−3. Signals with more irregular and quickly varying statistics are also recovered, even though the reconstruction error becomes highly dependent on the number of collected samples. CS minimization is obtained through the recently proposed NESTA optimization algorithm. Our implementation of CS recovery is available in [1].

When telecommunications networks meet energy grids: cellular networks with energy harvesting and trading capabilities

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.

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

The radio transceiver of an Internet of Things (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; and 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.

Modeling, Simulation and Experimentation of Droplet-Based Microfluidic Networks

The aim of this paper is to model the “macroscopic” functioning of droplet-based microfluidic networks, i.e., the speed and trajectory of droplets across a network of microfluidic elements. To this end, we first give a quick overview of microfluidic basics and main governing rules. Based on such principles, we derive mathematical models of the fundamental components of a microfluidic network and, then, we identify the set of variables that capture the dynamic state of the system. This allows us to define a simple way to simulate the “macroscopic” evolution of the microfluidic network, predicting the path followed by the droplets in the circuits. To validate the model, we compare the simulation results with the experimental outcomes we obtained from a simple but representative microfluidic circuit, which has been realized in our laboratory, and with other circuits tested in previous works. Finally, we apply our theoretical model to a more complex usecase, consisting in a microfluidic network with bus topology, and we draw some final considerations about the performance of such a network.

Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Modern wearable Internet of Things (IoT) devices enable the monitoring of vital parameters such as heart or respiratory (RESP) rates, 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 1-D 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.

Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks

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 signals spatio-temporal correlation structure through the Kronecker CS framework. CB-CSs 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).