Queuing theoretic and information theoretic capacity of energy harvesting sensor nodes

Energy harvesting sensor networks provide near perpetual operation and reduce carbon emissions thereby supporting `green communication'. We study such a sensor node powered with an energy harvesting source. We obtain energy management policies that are throughput optimal. We also obtain delay-optimal policies. Next we obtain the Shannon capacity of such a system. Further we combine the information theoretic and queuing theoretic approaches to obtain the Shannon capacity of an energy harvesting sensor node with a data queue. Then we generalize these results to models with fading and energy consumption in activities other than transmission.

Information capacity of energy harvesting sensor nodes

Sensor nodes with energy harvesting sources are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting `green communication'. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over an AWGN channel and show that the capacity achieving energy management policies are related to the throughput optimal policies. We also obtain the capacity when energy conserving sleep-wake modes are supported and an achievable rate for the system with inefficiencies in energy storage.

Adiabatic Eigenfunction Based Approach to Coherent Transfer: Application to the Fenna-Matthews-Olson (FMO) Complex and the Role of Correlations in the Efficiency of Energy Transfer

We have recently suggested a method (Pallavi Bhattacharyya and K. L. Sebastian, Physical Review E 2013, 87, 062712) for the analysis of coherence in finite-level systems that are coupled to the surroundings and used it to study the process of energy transfer in the Fenna-Matthews-Olson (FMO) complex. The method makes use of adiabatic eigenstates of the Hamiltonian, with a subsequent transformation of the Hamiltonian into a form where the terms responsible for decoherence and population relaxation could be separated out at the lowest order. Thus one can account for decoherence nonperturbatively, and a Markovian type of master equation could be used for evaluating the population relaxation. In this paper, we apply this method to a two-level system as well as to a seven-level system. Comparisons with exact numerical results show that the method works quite well and is in good agreement with numerical calculations. The technique can be applied with ease to systems with larger numbers of levels as well. We also investigate how the presence of correlations among the bath degrees of freedom of the different bacteriochlorophyll a molecules of the FMO Complex affect the rate of energy transfer. Surprisingly, in the cases that we studied, our calculations suggest that the presence of anticorrelations, in contrast to correlations, make the excitation transfer more facile.

Strain-Induced Hierarchy of Energy Levels in CdS/ZnS Nanocrystals

Aside of size and shape, the strain induced by the mismatch of lattice parameters between core and shell in the nanocrystalline regime is an additional degree of freedom to engineer the electron energy levels. Herein, CdS/ZnS core/shell nanocrystals (NCs) with shell thickness up to four monolayers are studied. As a manifestation of strain, the low temperature radiative lifetime measurements indicate a reduction in Stokes shift from 36 meV for CdS to 5 meV for CdS/ZnS with four monolayers of overcoating. Concomitant crossover of S- and P-symmetric hole levels is observed which can be understood in the framework of theoretical calculations predicting flipping the hierarchy of ground hole state by the strain in CdS NCs. Furthermore, a nonmonotonic variation of higher energy levels in strained CdS NCs is discussed.

Error exponent analysis of energy-based Bayesian decentralized spectrum sensing under fading

This paper considers decentralized spectrum sensing, i.e., detection of occupancy of the primary users' spectrum by a set of Cognitive Radio (CR) nodes, under a Bayesian set-up. The nodes use energy detection to make their individual decisions, which are combined at a Fusion Center (FC) using the K-out-of-N fusion rule. The channel from the primary transmitter to the CR nodes is assumed to undergo fading, while that from the nodes to the FC is assumed to be error-free. In this scenario, a novel concept termed as the Error Exponent with a Confidence Level (EECL) is introduced to evaluate and compare the performance of different detection schemes. Expressions for the EECL under general fading conditions are derived. As a special case, it is shown that the conventional error exponent both at individual sensors, and at the FC is zero. Further, closed-form lower bounds on the EECL are derived under Rayleigh fading and lognormal shadowing. As an example application, it answers the question of whether to use pilot-signal based narrowband sensing, where the signal undergoes Rayleigh fading, or to sense over the entire bandwidth of a wideband signal, where the signal undergoes lognormal shadowing. Theoretical results are validated using Monte Carlo simulations. (C) 2015 Elsevier B.V. All rights reserved.

Life-cycle analysis of energy and greenhouse gas emissions of automotive fuels in India: Part 2-Well-to-wheels analysis

In this second of the two-part study, the results of the Tank-to-Wheels study reported in the first part are combined with Well-to-Tank results in this paper to provide a comprehensive Well-to-Wheels energy consumption and greenhouse gas emissions evaluation of automotive fuels in India. The results indicate that liquid fuels derived from petroleum have Well-to-Tank efficiencies in the range of 75-85% with liquefied petroleum gas being the most efficient fuel in the Well-to-Tank stage with 85% efficiency. Electricity has the lowest efficiency of 20% which is mainly attributed due to its dependence on coal and 25.4% losses during transmission and distribution. The complete Well-to-Wheels results show diesel vehicles to be the most efficient among all configurations, specifically the diesel-powered split hybrid electric vehicle. Hydrogen engine configurations are the least efficient due to low efficiency of production of hydrogen from natural gas. Hybridizing electric vehicles reduces the Well-to-Wheels greenhouse gas emissions substantially with split hybrid configuration being the most efficient. Electric vehicles do not offer any significant improvement over gasoline-powered configurations; however a shift towards renewable sources for power generation and reduction in losses during transmission and distribution can make it a feasible option in the future. (C) 2015 Elsevier Ltd. All rights reserved.

Damage Localization, Sensitivity of Energy Release and the Catastrophe Transition

Large earthquakes can be viewed as catastrophic ruptures in the earth’s crust. There are two common features prior to the catastrophe transition in heterogeneous media. One is damage localization and the other is critical sensitivity; both of which are related to a cascade of damage coalescence. In this paper, in an attempt to reveal the physics underlying the catastrophe transition, analytic analysis based on mean-field approximation of a heterogeneous medium as well as numerical simulations using a network model are presented. Both the emergence of damage localization and the sensitivity of energy release are examined to explore the inherent statistical precursors prior to the eventual catastrophic rupture. Emergence of damage localization, as predicted by the mean-field analysis, is consistent with observations of the evolution of damage patterns. It is confirmed that precursors can be extracted from the time-series of energy release according to its sensitivity to increasing crustal stress. As a major result, present research indicates that the catastrophe transition and the critical point hypothesis (CPH) of earthquakes are interrelated. The results suggest there may be two cross-checking precursors of large earthquakes: damage localization and critical sensitivity.

Characteristics Of Energy Dissipation In Hyperconcentrated Flows

An equilibrium equation for the turbulence energy in sediment-laden flows was derived on the basis of solid-liquid two-phase flow theory. The equation was simplified for two-dimensional, uniform, steady and fully developed turbulent hyperconcentrated flows. An energy efficiency coefficient of suspended-load motion was obtained from the turbulence energy equation, which is defined as the ratio of the sediment suspension energy to the turbulence energy of the sediment-laden flows. Laboratory experiments were conducted to investigate the characteristics of energy dissipation in hyperconcentrated flows. A total of 115 experimental runs were carried out, comprising 70 runs with natural sediments and 45 runs with cinder powder. Effects of sediment concentration on sediment suspension energy and flow resistance were analyzed and the relation between the energy efficiency coefficient of suspended-load motion and sediment concentration was established on the basis of experimental data. Furthermore, the characteristics of energy dissipation in hyperconcentrated flows were identified and described. It was found that the high sediment concentration does not increase the energy dissipation; on the contrary, it decreases flow resistance.

The mechanism of energy storage by shearing the solar magnetic field

In this paper, a complete set of MHD equations have been solved by numerical calculations in an attempt to study the dynamical evolutionary processes of the initial equilibrium configuration and to discuss the energy storage mechanism of the solar atmosphere by shearing the magnetic field. The initial equilibrium configuration with an arch bipolar potential field obtained from the numerical solution is similar to the configuration in the vicinity of typical solar flare before its eruption. From the magnetic induction equation in the set of MHD equations and dealing with the non-linear coupling effects between the flow field and magnetic field, the quantitative relationship has been derived for their dynamical evolution. Results show that plasma shear motion at the bottom of the solar atmosphere causes the magnetic field to shear; meanwhile the magnetic field energy is stored in local regions. With the increase of time the local magnetic energy increases and it may reach an order of 4×10^25 J during a day. Thus the local storage of magnetic energy is large enough to trigger a big solar flare and can be considered as the energy source of solar flares. The energy storage mechanism by shearing the magnetic field can well explain the slow changes in solar active regions.

Regulation of Energy Metabolism by the Extracytoplasmic Function (ECF) σ Factors of Arcobacter butzleri

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Numerical Partition of Energy Absorption of Foam-filled Top-Hat and Double-Hat Sections

The interaction effect, i.e., the contribution of each component to the total energy absorption of an axially crushed foam-filled hat section was investigated quantitatively via numerical simulation. The FE results were first verified by experimental work of aluminum foam-filled top-hat and double-hat sections, then the contribution of foam-fillers and that of hat sections to the overall energy absorption were quantitatively obtained, respectively. When foam-filled, increase in energy absorption was found both in hat section component and foam-filler component, whereas the latter contributes predominantly to the interaction effect.

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed.