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125
Wireless Information Transfer with Opportunistic Energy Harvesting
- Wireless Communications, IEEE Transactions on
, 2013
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Joint energy harvesting and communication analysis for perpetual wireless nanosensor networks in the terahertz band
- IEEE TRANSACTIONS ON NANOTECHNOLOGY
, 2012
"... Wireless nanosensor networks (WNSNs) consist of nanosized communicating devices, which can detect and measure new types of events at the nanoscale. WNSNs are the enabling technology for unique applications such as intrabody drug delivery systems or surveillance networks for chemical attack preventi ..."
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Cited by 14 (2 self)
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Wireless nanosensor networks (WNSNs) consist of nanosized communicating devices, which can detect and measure new types of events at the nanoscale. WNSNs are the enabling technology for unique applications such as intrabody drug delivery systems or surveillance networks for chemical attack prevention. One of the major bottlenecks in WNSNs is posed by the very limited energy that can be stored in a nanosensor mote in contrast to the energy that is required by the device to communicate. Recently, novel energy harvesting mechanisms have been proposed to replenish the energy stored in nanodevices. With these mechanisms, WNSNs can overcome their energy bottleneck and even have infinite lifetime (perpetual WNSNs), provided that the energy harvesting and consumption processes are jointly designed. In this paper, an energy model for self-powered nanosensor motes is developed, which successfully captures the correlation between the energy harvesting
Wireless sensor networks with energy harvesting
- MOBILE AD HOC NETWORKING: CUTTING EDGE DIRECTIONS
, 2013
"... This chapter covers the fundamental aspects of energy harvesting-based wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With th ..."
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Cited by 13 (8 self)
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This chapter covers the fundamental aspects of energy harvesting-based wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With the advancement of energy harvesting techniques, along with the development of small factor harvester for many different energy sources, EHWSNs are poised to become the technology of choice for the host of applications that require the network to function for years or even decades. Through the definition of new hardware and communication protocols specifically tailored to the fundamentally different models of energy availability, new applications can also be conceived that rely on “perennial ” functionalities from networks that are truly self-sustaining and with low environmental impact. Wireless sensor networks (WSNs) have played a major role in the research field of multihop wireless networks as enablers of applications ranging from environmental
Spatial Modulation for Generalized MIMO: Challenges, Opportunities and Implementation
, 2013
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Relay selection for simultaneous information transmission and wireless energy transfer: A tradeoff perspective,” Available on-line at arXiv:1303.1647
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Optimal Random Access and Random Spectrum Sensing for an Energy Harvesting Cognitive Radio with and without Primary Feedback Leveraging
"... We consider a secondary user (SU) with energy harvesting capability. We design access schemes for the SU which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. We study two problem-formulations. In the first problem-for ..."
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Cited by 8 (7 self)
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We consider a secondary user (SU) with energy harvesting capability. We design access schemes for the SU which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. We study two problem-formulations. In the first problem-formulation, we characterize the stability region of the proposed schemes. The sensing and access probabilities are obtained such that the secondary throughput is maximized under the constraints that both the primary and secondary queues are stable. Whereas in the second problem-formulation, the sensing and access probabilities are obtained such that the secondary throughput is maximized under the stability of the primary queue and that the primary queueing delay is kept lower than a specified value needed to guarantee a certain quality of service (QoS) for the primary user (PU). We consider spectrum sensing errors and assume multipacket reception (MPR) capabilities. Numerical results show the enhanced performance of our proposed systems.
An energy harvesting AWGN channel with a finite battery
- In IEEE ISIT
, 2014
"... Abstract—In energy harvesting communication systems, the transmitter is adapted to harvest energy per time slot. The harvested energy is either used right away or is stored in a battery to facilitate future transmissions. We consider the problem of determining the Shannon capacity of an energy harve ..."
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Cited by 8 (0 self)
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Abstract—In energy harvesting communication systems, the transmitter is adapted to harvest energy per time slot. The harvested energy is either used right away or is stored in a battery to facilitate future transmissions. We consider the problem of determining the Shannon capacity of an energy harvesting transmitter communicating over an additive white Gaussian noise (AWGN) channel, where the amount of energy harvested per time slot is a constant ρ and the battery has capacity σ. This imposes a new kind of power constraint on the transmitted codewords, and we call the resulting constrained channel a (σ, ρ) power constrained AWGN channel. When σ is 0 or ∞, the capacity of this channel is known. For the finite battery case, we obtain an expression for the channel capacity. We obtain bounds on capacity by considering the volume of Sn(σ, ρ) ⊆ Rn, which is the set of all length n sequences satisfying the (σ, ρ) constraints.
Empirical Modeling of a Solar-Powered Energy Harvesting Wireless Sensor Node for Time-Slotted Operation
- in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Cancun
"... Abstract—Energy harvesting wireless sensor networks (EH-WSNs) are gaining importance in smart homes, environmental monitoring, health care and transportation systems, since they enable much longer operation time as energy can be replenished through energy harvesting. This is unlike WSN nodes that us ..."
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Cited by 7 (1 self)
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Abstract—Energy harvesting wireless sensor networks (EH-WSNs) are gaining importance in smart homes, environmental monitoring, health care and transportation systems, since they enable much longer operation time as energy can be replenished through energy harvesting. This is unlike WSN nodes that use non-rechargeable batteries which need to be replaced once energy is depleted. However, the sporadic availability of ambient energy makes the design of networking protocols and predicting network performance very challenging. In this paper, we perform an empirical energy characterization of a time-slotted solar energy harvesting node with different system and environmental parameters. We use six different statistical models (uniform distribution, geometric distribution, transformed geometric distribution, Poisson distribution, transformed Poisson distribution and a Markovian model) to fit the empirical datasets. Our results show that there is no single statistical model that can fit all the datasets, thus justifying the need to use empirical data to validate the theoretical analysis of any time-slotted MAC protocol for EH-WSNs. I.
Optimal training for wireless energy transfer,” submitted for possible conference publication, available: http://arxiv.org/abs/1403.7870
"... Abstract—Wireless energy transfer (WET) is potentially a promising solution to provide convenient and reliable energy supplies for energy-constrained networks, and has drawn growing interests recently. To overcome the sig-nificant prorogation loss over distance, employing multi-antennas at the energ ..."
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Cited by 6 (2 self)
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Abstract—Wireless energy transfer (WET) is potentially a promising solution to provide convenient and reliable energy supplies for energy-constrained networks, and has drawn growing interests recently. To overcome the sig-nificant prorogation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming, is an essential technique for WET. However, the achievable gain of energy beamforming crucially depends on the available channel state informa-tion (CSI) at the ET, which needs to be acquired practically. In this paper, we study the optimal design of one efficient channel-acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system, by exploiting the channel reciprocity based on which the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net energy at the ER, which is the total energy harvested offset by that used for channel training. The optimal training design, including the number of receive antennas to be trained, as well as the training time and power allocated, is derived. Our result shows that training helps only when either the channel coherence time, or the number of antennas at the ET, or the effective signal-to-noise ratio (ESNR), is sufficiently large; otherwise, no training should be applied and isotropic energy transmission is optimal. I.
An effective multi-source energy harvester for low power application
- in Proc. of the Design, Automation & Test in Europe Conference & Exhibition (DATE 2011), Mar 14-18 2011
"... ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
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Cited by 5 (3 self)
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©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
