91 research outputs found
DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things
We propose a new energy harvesting strategy that uses a dedicated energy
source (ES) to optimally replenish energy for radio frequency (RF) energy
harvesting powered Internet of Things. Specifically, we develop a two-step dual
tunnel energy requesting (DTER) strategy that minimizes the energy consumption
on both the energy harvesting device and the ES. Besides the causality and
capacity constraints that are investigated in the existing approaches, DTER
also takes into account the overhead issue and the nonlinear charge
characteristics of an energy storage component to make the proposed strategy
practical. Both offline and online scenarios are considered in the second step
of DTER. To solve the nonlinear optimization problem of the offline scenario,
we convert the design of offline optimal energy requesting problem into a
classic shortest path problem and thus a global optimal solution can be
obtained through dynamic programming (DP) algorithms. The online suboptimal
transmission strategy is developed as well. Simulation study verifies that the
online strategy can achieve almost the same energy efficiency as the global
optimal solution in the long term
Enhancing In-Situ Structural Health Monitoring through RF Energy-Powered Sensor Nodes and Mobile Platform
This research contributes to long-term structural health monitoring (SHM) by
exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in
concrete. Unlike traditional in-situ monitoring systems relying on batteries or
wire-connected power sources, the RF-SN captures radio energy from a mobile
radio transmitter for sensing and communication. This offers a cost-effective
solution for consistent in-situ perception. To optimize the system performance
across various situations, we've explored both active and passive communication
methods. For the active RF-SN, we implement a specialized control circuit
enabling the node to transmit data through ZigBee protocol at low incident
power. For the passive RF-SN, radio energy is not only for power but also as a
carrier signal, with data conveyed by modulating the amplitude of the
backscattered radio wave. To address the challenge of significant attenuation
of the backscattering signal in concrete, we utilize a square chirp-based
modulation scheme for passive communication. This scheme allows the receiver to
successfully decode the data even under a negative signal-to-noise ratio (SNR)
condition. The experimental results indicate that an active RF-SN embedded in
concrete at a depth of 13.5 cm can be effectively powered by a 915MHz mobile
radio transmitter with an effective isotropic radiated power (EIRP) of 32.5dBm.
This setup allows the RF-SN to send over 1 kilobyte of data within 10 seconds,
with an additional 1.7 kilobytes every 1.6 seconds of extra charging. For the
passive RF-SN buried at the same depth, continuous data transmission at a rate
of 224 bps with a 3% bit error rate (BER) is achieved when the EIRP of the
transmitter is 23.6 dBm
RF Energy Harvesting Wireless Communication: RF Environment, Device Hardware and Practical Issues
Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC
Teaching “Imaginary Objects” Symbolic Play to Young Children with Autism Spectrum Disorder
Symbolic play skills are important in language acquisition and child development. Children with autism spectrum disorder (ASD) often have difficulties demonstrating such play behaviors. Imaginary objects symbolic play refers to play behavior in which children perform play actions without actual objects. Three boys with ASD (3-7 years) participated in this study. A multiple-probe across three participants and two settings design was employed to evaluate the effects of intraverbal training on the acquisition and generalization of imaginary objects symbolic play. Results indicated that all children acquired and maintained target imaginary objects play activities. Generalization to untaught activities occurred in one child. All three children’ symbolic play emerged or increased in free play after the instruction
Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks
In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However,the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-basedreceiver-initiated MAC(TERI-MAC)to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications
Receiver-Initiated Handshaking MAC Based On Traffic Estimation for Underwater Sensor Networks
In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications
Excitation and voltage-gated modulation of single-mode dynamics in a planar nano-gap spin Hall nano-oscillator
We experimentally study the dynamical modes excited by current-induced
spin-orbit torque and its electrostatic gating effect in a 3-terminal planar
nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial
perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating
spin-wave and localized "bullet" modes are achieved and controlled by varying
the applied in-plane magnetic field and driving current. The minimum linewidth
shows a linear dependence on the actual temperature of the active area,
confirming single-mode dynamics based on the nonlinear theory of spin-torque
nano-oscillation with a single mode. The observed electrostatic gating tuning
oscillation frequency arises from voltage-controlled magnetic anisotropy and
threshold current of SHNO via modification of the nonlinear damping and/or the
interfacial spin-orbit coupling of the magnetic multilayer. In contrast to
previously observed two-mode coexistence degrading the spectral purity in
Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with
a low driven current can be achieved by selecting the ferromagnet layer with a
suitable IPMA because the nonlinear mode coupling can be diminished by bringing
in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the
simulations demonstrate that the experimentally observed current and
gate-voltage modulation of auto-oscillation modes are also closely associated
with the nonlinear damping and mode coupling, which are determined by the
ellipticity of magnetization precession. The demonstrated nonlinear mode
coupling mechanism and electrical control approach of spin-wave modes could
provide the clue to facilitate the implementation of the mutual synchronization
map for neuromorphic computing applications in SHNO array networks.Comment: 11 pages, 10 figure
Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks
Federated Learning (FL), as an effective decentral-
ized approach, has attracted considerable attention in privacy-
preserving applications for wireless edge networks. In practice,
edge devices are typically limited by energy, memory, and
computation capabilities. In addition, the communications be-
tween the central server and edge devices are with constrained
resources, e.g., power or bandwidth. In this paper, we propose
a joint sparsification and optimization scheme to reduce the
energy consumption in local training and data transmission.
On the one hand, we introduce sparsification, leading to a
large number of zero weights in sparse neural networks, to
alleviate devices’ computational burden and mitigate the data
volume to be uploaded. To handle the non-smoothness incurred
by sparsification, we develop an enhanced stochastic gradient
descent algorithm to improve the learning performance. On
the other hand, we optimize power, bandwidth, and learning
parameters to avoid communication congestion and enable an
energy-efficient transmission between the central server and edge
devices. By collaboratively deploying the above two components,
the numerical results show that the overall energy consumption
in FL can be significantly reduced, compared to benchmark FL
with fully-connected neural networks
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