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.

Apparatus and method for heat-run test on high-power PWM converters with low energy expenditure

Before installation, a voltage source converter is usually subjected to heat-run test to verify its thermal design and performance under load. For heat-run test, the converter needs to be operated at rated voltage and rated current for a substantial length of time. Hence, such tests consume huge amount of energy in case of high-power converters. Also, the capacities of the source and loads available in the research and development (R&D) centre or the production facility could be inadequate to conduct such tests. This paper proposes a method to conduct heat-run tests on high-power, pulse width modulated (PWM) converters with low energy consumption. The experimental set-up consists of the converter under test and another converter (of similar or higher rating), both connected in parallel on the ac side and open on the dc side. Vector-control or synchronous reference frame control is employed to control the converters such that one draws certain amount of reactive power and the other supplies the same; only the system losses are drawn from the mains. The performance of the controller is validated through simulation and experiments. Experimental results, pertaining to heat-run tests on a high-power PWM converter, are presented at power levels of 25 kVA to 150 kVA.

The effect of cell size and channel density on neuronal information encoding and energy efficiency

Identifying the determinants of neuronal energy consumption and their relationship to information coding is critical to understanding neuronal function and evolution. Three of the main determinants are cell size, ion channel density, and stimulus statistics. Here we investigate their impact on neuronal energy consumption and information coding by comparing single-compartment spiking neuron models of different sizes with different densities of stochastic voltage-gated Na+ and K+ channels and different statistics of synaptic inputs. The largest compartments have the highest information rates but the lowest energy efficiency for a given voltage-gated ion channel density, and the highest signaling efficiency (bits spike(-1)) for a given firing rate. For a given cell size, our models revealed that the ion channel density that maximizes energy efficiency is lower than that maximizing information rate. Low rates of small synaptic inputs improve energy efficiency but the highest information rates occur with higher rates and larger inputs. These relationships produce a Law of Diminishing Returns that penalizes costly excess information coding capacity, promoting the reduction of cell size, channel density, and input stimuli to the minimum possible, suggesting that the trade-off between energy and information has influenced all aspects of neuronal anatomy and physiology.

Multiple sub-row buffers in DRAM: unlocking performance and energy improvement opportunities

The twin demands of energy-efficiency and higher performance on DRAM are highly emphasized in multicore architectures. A variety of schemes have been proposed to address either the latency or the energy consumption of DRAMs. These schemes typically require non-trivial hardware changes and end up improving latency at the cost of energy or vice-versa. One specific DRAM performance problem in multicores is that interleaved accesses from different cores can potentially degrade row-buffer locality. In this paper, based on the temporal and spatial locality characteristics of memory accesses, we propose a reorganization of the existing single large row-buffer in a DRAM bank into multiple sub-row buffers (MSRB). This re-organization not only improves row hit rates, and hence the average memory latency, but also brings down the energy consumed by the DRAM. The first major contribution of this work is proposing such a reorganization without requiring any significant changes to the existing widely accepted DRAM specifications. Our proposed reorganization improves weighted speedup by 35.8%, 14.5% and 21.6% in quad, eight and sixteen core workloads along with a 42%, 28% and 31% reduction in DRAM energy. The proposed MSRB organization enables opportunities for the management of multiple row-buffers at the memory controller level. As the memory controller is aware of the behaviour of individual cores it allows us to implement coordinated buffer allocation schemes for different cores that take into account program behaviour. We demonstrate two such schemes, namely Fairness Oriented Allocation and Performance Oriented Allocation, which show the flexibility that memory controllers can now exploit in our MSRB organization to improve overall performance and/or fairness. Further, the MSRB organization enables additional opportunities for DRAM intra-bank parallelism and selective early precharging of the LRU row-buffer to further improve memory access latencies. These two optimizations together provide an additional 5.9% performance improvement.

Assessing impact of material transition and thermal comfort models on embodied and operational energy in vernacular dwellings (India)

Vernacular dwellings are well-suited climate-responsive designs that adopt local materials and skills to support comfortable indoor environments in response to local climatic conditions. These naturally-ventilated passive dwellings have enabled civilizations to sustain even in extreme climatic conditions. The design and physiological resilience of the inhabitants have coevolved to be attuned to local climatic and environmental conditions. Such adaptations have perplexed modern theories in human thermal-comfort that have evolved in the era of electricity and air-conditioned buildings. Vernacular local building elements like rubble walls and mud roofs are given way to burnt brick walls and reinforced cement concrete tin roofs. Over 60% of Indian population is rural, and implications of such transitions on thermal comfort and energy in buildings are crucial to understand. Types of energy use associated with a buildings life cycle include its embodied energy, operational and maintenance energy, demolition and disposal energy. Embodied Energy (EE) represents total energy consumption for construction of building, i.e., embodied energy of building materials, material transportation energy and building construction energy. Embodied energy of building materials forms major contribution to embodied energy in buildings. Operational energy (OE) in buildings mainly contributed by space conditioning and lighting requirements, depends on the climatic conditions of the region and comfort requirements of the building occupants. Less energy intensive natural materials are used for traditional buildings and the EE of traditional buildings is low. Transition in use of materials causes significant impact on embodied energy of vernacular dwellings. Use of manufactured, energy intensive materials like brick, cement, steel, glass etc. contributes to high embodied energy in these dwellings. This paper studies the increase in EE of the dwelling attributed to change in wall materials. Climatic location significantly influences operational energy in dwellings. Buildings located in regions experiencing extreme climatic conditions would require more operational energy to satisfy the heating and cooling energy demands throughout the year. Traditional buildings adopt passive techniques or non-mechanical methods for space conditioning to overcome the vagaries of extreme climatic variations and hence less operational energy. This study assesses operational energy in traditional dwelling with regard to change in wall material and climatic location. OE in the dwellings has been assessed for hot-dry, warm humid and moderate climatic zones. Choice of thermal comfort models is yet another factor which greatly influences operational energy assessment in buildings. The paper adopts two popular thermal-comfort models, viz., ASHRAE comfort standards and TSI by Sharma and Ali to investigate thermal comfort aspects and impact of these comfort models on OE assessment in traditional dwellings. A naturally ventilated vernacular dwelling in Sugganahalli, a village close to Bangalore (India), set in warm - humid climate is considered for present investigations on impact of transition in building materials, change in climatic location and choice of thermal comfort models on energy in buildings. The study includes a rigorous real time monitoring of the thermal performance of the dwelling. Dynamic simulation models validated by measured data have also been adopted to determine the impact of the transition from vernacular to modern material-configurations. Results of the study and appraisal for appropriate thermal comfort standards for computing operational energy has been presented and discussed in this paper. (c) 2014 K.I. Praseeda. Published by Elsevier Ltd.

Embodied energy assessment of building materials in India using process and input-output analysis

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input-output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. (C) 2014 Elsevier B.V. All rights reserved.

Energy-efficient, delay-constrained, QoS-aware broadcast for cooperative wireless sensor networks

The problem of delay-constrained, energy-efficient broadcast in cooperative wireless networks is NP-complete. While centralised setting allows some heuristic solutions, designing heuristics in distributed implementation poses significant challenges. This is more so in wireless sensor networks (WSNs) where nodes are deployed randomly and topology changes dynamically due to node failure/join and environment conditions. This paper demonstrates that careful design of network infrastructure can achieve guaranteed delay bounds and energy-efficiency, and even meet quality of service requirements during broadcast. The paper makes three prime contributions. First, we present an optimal lower bound on energy consumption for broadcast that is tighter than what has been previously proposed. Next, iSteiner, a lightweight, distributed and deterministic algorithm for creation of network infrastructure is discussed. iPercolate is the algorithm that exploits this structure to cooperatively broadcast information with guaranteed delivery and delay bounds, while allowing real-time traffic to pass undisturbed.

Discrete-Rate Adaptation and Selection in Energy Harvesting Wireless Systems

In a system with energy harvesting (EH) nodes, the design focus shifts from minimizing energy consumption by infrequently transmitting less information to making the best use of available energy to efficiently deliver data while adhering to the fundamental energy neutrality constraint. We address the problem of maximizing the throughput of a system consisting of rate-adaptive EH nodes that transmit to a destination. Unlike related literature, we focus on the practically important discrete-rate adaptation model. First, for a single EH node, we propose a discrete-rate adaptation rule and prove its optimality for a general class of stationary and ergodic EH and fading processes. We then study a general system with multiple EH nodes in which one is opportunistically selected to transmit. We first derive a novel and throughput-optimal joint selection and rate adaptation rule (TOJSRA) when the nodes are subject to a weaker average power constraint. We then propose a novel rule for a multi-EH node system that is based on TOJSRA, and we prove its optimality for stationary and ergodic EH and fading processes. We also model the various energy overheads of the EH nodes and characterize their effect on the adaptation policy and the system throughput.

Quick, Decentralized, Energy-Efficient One-Shot Max Function Computation Using Timer-Based Selection

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. We propose a novel, decentralized, scalable, energy-efficient, timer-based, one-shot max function computation (TMC) algorithm. In it, the sensor nodes do not transmit their readings in a centrally pre-defined sequence. Instead, the nodes are grouped into clusters, and computation occurs over two contention stages. First, the nodes in each cluster contend with each other using the timer scheme to transmit their reading to their cluster-heads. Thereafter, the cluster-heads use the timer scheme to transmit the highest sensor reading in their cluster to the fusion node. One new challenge is that the use of the timer scheme leads to collisions, which can make the algorithm fail. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find the maximum. TMC significantly lowers average function computation time, average number of transmissions, and average energy consumption compared to approaches proposed in the literature.

All Digital Energy Sensing for Minimum Energy Tracking

Minimizing energy consumption is of utmost importance in an energy starved system with relaxed performance requirements. This brief presents a digital energy sensing method that requires neither a constant voltage reference nor a time reference. An energy minimizing loop uses this to find the minimum energy point and sets the supply voltage between 0.2 and 0.5 V. Energy savings up to 1275% over existing minimum energy tracking techniques in the literature is achieved.

A TDMA-based Energy Aware MAC (TEA-MAC) Protocol for Reliable Multicast in WSNs

Multicast in wireless sensor networks (WSNs) is an efficient way to spread the same data to multiple sensor nodes. It becomes more effective due to the broadcast nature of wireless link, where a message transmitted from one source is inherently received by all one-hop receivers, and therefore, there is no need to transmit the message one by one. Reliable multicast in WSNs is desirable for critical tasks like code updation and query based data collection. The erroneous nature of wireless medium coupled with limited resource of sensor nodes, makes the design of reliable multicast protocol a challenging task. In this work, we propose a time division multiple access (TDMA) based energy aware media access and control (TEA-MAC) protocol for reliable multicast in WSNs. The TDMA eliminates collisions, overhearing and idle listening, which are the main sources of reliability degradation and energy consumption. Furthermore, the proposed protocol is parametric in the sense that it can be used to trade-off reliability with energy and delay as per the requirement of the underlying applications. The performance of TEA-MAC has been evaluated by simulating it using Castalia network simulator. Simulation results show that TEA-MAC is able to considerably improve the performance of multicast communication in WSNs.

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.

Dealing with Energy-QoE Trade-Offs in Mobile Video

Scalable video coding allows an efficient provision of video services at different quality levels with different energy demands. According to the specific type of service and network scenario, end users and/or operators may decide to choose among different energy versus quality combinations. In order to deal with the resulting trade-off, in this paper we analyze the number of video layers that are worth to be received taking into account the energy constraints. A single-objective optimization is proposed based on dynamically selecting the number of layers, which is able to minimize the energy consumption with the constraint of a minimal quality threshold to be reached. However, this approach cannot reflect the fact that the same increment of energy consumption may result in different increments of visual quality. Thus, a multiobjective optimization is proposed and a utility function is defined in order to weight the energy consumption and the visual quality criteria. Finally, since the optimization solving mechanism is computationally expensive to be implemented in mobile devices, a heuristic algorithm is proposed. This way, significant energy consumption reduction will be achieved while keeping reasonable quality levels.

Energy and information in Hodgkin-Huxley neurons

[EN]The generation of spikes by neurons is energetically a costly process and the evaluation of the metabolic energy required to maintain the signaling activity of neurons a challenge of practical interest. Neuron models are frequently used to represent the dynamics of real neurons but hardly ever to evaluate the electrochemical energy required to maintain that dynamics. This paper discusses the interpretation of a Hodgkin-Huxley circuit as an energy model for real biological neurons and uses it to evaluate the consumption of metabolic energy in the transmission of information between neurons coupled by electrical synapses, i.e., gap junctions. We show that for a single postsynaptic neuron maximum energy efficiency, measured in bits of mutual information per molecule of adenosine triphosphate (ATP) consumed, requires maximum energy consumption. For groups of parallel postsynaptic neurons we determine values of the synaptic conductance at which the energy efficiency of the transmission presents clear maxima at relatively very low values of metabolic energy consumption. Contrary to what could be expected, the best performance occurs at a low energy cost.

Information-theoretic Studies and Capacity Bounds: Group Network Codes and Energy Harvesting Communication Systems

Network information theory and channels with memory are two important but difficult frontiers of information theory. In this two-parted dissertation, we study these two areas, each comprising one part. For the first area we study the so-called entropy vectors via finite group theory, and the network codes constructed from finite groups. In particular, we identify the smallest finite group that violates the Ingleton inequality, an inequality respected by all linear network codes, but not satisfied by all entropy vectors. Based on the analysis of this group we generalize it to several families of Ingleton-violating groups, which may be used to design good network codes. Regarding that aspect, we study the network codes constructed with finite groups, and especially show that linear network codes are embedded in the group network codes constructed with these Ingleton-violating families. Furthermore, such codes are strictly more powerful than linear network codes, as they are able to violate the Ingleton inequality while linear network codes cannot. For the second area, we study the impact of memory to the channel capacity through a novel communication system: the energy harvesting channel. Different from traditional communication systems, the transmitter of an energy harvesting channel is powered by an exogenous energy harvesting device and a finite-sized battery. As a consequence, each time the system can only transmit a symbol whose energy consumption is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is random, instantaneous, and has memory. Furthermore, naturally, the energy harvesting process is observed causally at the transmitter, but no such information is provided to the receiver. Both of these features pose great challenges for the analysis of the channel capacity. In this work we use techniques from channels with side information, and finite state channels, to obtain lower and upper bounds of the energy harvesting channel. In particular, we study the stationarity and ergodicity conditions of a surrogate channel to compute and optimize the achievable rates for the original channel. In addition, for practical code design of the system we study the pairwise error probabilities of the input sequences.