Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing?

This paper advocates the use of the emerging distributed compressive sensing (DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor networks (WSN) with practical network lifetime and data gathering rates that are substantially higher than the state-of-the-art. In particular, we argue that there are two fundamental mechanisms in an EH WSN: i) the energy diversity associated with the EH process that entails that the harvested energy can vary from sensor node to sensor node, and ii) the sensing diversity associated with the DCS process that entails that the energy consumption can also vary across the sensor nodes without compromising data recovery. We also argue that such mechanisms offer the means to match closely the energy demand to the energy supply in order to unlock the possibility for energy-neutral WSNs that leverage EH capability. A number of analytic and simulation results are presented in order to illustrate the potential of the approach.Comment: 6 pages. This work will be presented at the 2013 IEEE Global Communications Conference (GLOBECOM), Atlanta, US, December 201

Prediction-based incremental refinement for binomially-factorized discrete wavelet transforms

It was proposed recently that quantized representations of the input source (e. g., images, video) can be used for the computation of the two-dimensional discrete wavelet transform (2D DWT) incrementally. The coarsely quantized input source is used for the initial computation of the forward or inverse DWT, and the result is successively refined with each new refinement of the source description via an embedded quantizer. This computation is based on the direct two-dimensional factorization of the DWT using the generalized spatial combinative lifting algorithm. In this correspondence, we investigate the use of prediction for the computation of the results, i.e., exploiting the correlation of neighboring input samples (or transform coefficients) in order to reduce the dynamic range of the required computations, and thereby reduce the circuit activity required for the arithmetic operations of the forward or inverse transform. We focus on binomial factorizations of DWTs that include (amongst others) the popular 9/7 filter pair. Based on an FPGA arithmetic co-processor testbed, we present energy-consumption results for the arithmetic operations of incremental refinement and prediction-based incremental refinement in comparison to the conventional (nonrefinable) computation. Our tests with combinations of intra and error frames of video sequences show that the former can be 70% more energy efficient than the latter for computing to half precision and remains 15% more efficient for full-precision computation

Energy consumption of visual sensor networks: impact of spatio-temporal coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PANs) under recently established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by a number of camera sensors deployed (spatial coverage), as well as a number of captured video frames of which each node processes and transmits data (temporal coverage). In this paper we explore this aspect for uniformly formed VSNs, that is, networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatiooral coverage parameters of such VSNs under a priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results are derived from a deployment of TelosB motes and reveal that our analytic results are always within 7%of the energy consumption measurements for a wide range of settings. In addition, results obtained via motion JPEG encoding and feature extraction on a multimedia subsystem (BeagleBone Linux Computer) show that the optimal spatiooral settings derived by our framework allow for substantial reduction of energy consumption in comparison with ad hoc settings

Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks

Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimumenergy harvestingrequirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment

Linear image processing operations with operational tight packing

Computer hardware with native support for large-bitwidth operations can be used for the concurrent calculation of multiple independent linear image processing operations when these operations map integers to integers. This is achieved by packing multiple input samples in one large-bitwidth number, performing a single operation with that number and unpacking the results. We propose an operational framework for tight packing, i.e., achieve the maximum packing possible by a certain implementation. We validate our framework on floating-point units natively supported in mainstream programmable processors. For image processing tasks where operational tight packing leads to increased packing in comparison to previously-known operational packing, the processing throughput is increased by up to 25%. © 2010 IEEE

jClust: a clustering and visualization toolbox

jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron–Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside–inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction

Large Scale Structures a Gradient Lines: the case of the Trkal Flow

A specific asymptotic expansion at large Reynolds numbers (R)for the long wavelength perturbation of a non stationary anisotropic helical solution of the force less Navier-Stokes equations (Trkal solutions) is effectively constructed of the Beltrami type terms through multi scaling analysis. The asymptotic procedure is proved to be valid for one specific value of the scaling parameter,namely for the square root of the Reynolds number (R).As a result large scale structures arise as gradient lines of the energy determined by the initial conditions for two anisotropic Beltrami flows of the same helicity.The same intitial conditions determine the boundaries of the vortex-velocity tubes, containing both streamlines and vortex linesComment: 27 pages, 2 figure