179 research outputs found
How Agile is the Adaptive Data Rate Mechanism of LoRaWAN?
The LoRaWAN based Low Power Wide Area networks aim to provide long-range
connectivity to a large number of devices by exploiting limited radio
resources. The Adaptive Data Rate (ADR) mechanism controls the assignment of
these resources to individual end-devices by a runtime adaptation of their
communication parameters when the quality of links inevitably changes over
time. This paper provides a detailed performance analysis of the ADR technique
presented in the recently released LoRaWAN Specifications (v1.1). We show that
the ADR technique lacks the agility to adapt to the changing link conditions,
requiring a number of hours to days to converge to a reliable and
energy-efficient communication state. As a vital step towards improving this
situation, we then change different control knobs or parameters in the ADR
technique to observe their effects on the convergence time.Comment: 9 Figures, 2 Tables Accepted to appear in the proceedings of IEEE
GLOBECOM 201
PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning
Facilitated by mobile edge computing, client-edge-cloud hierarchical
federated learning (HFL) enables communication-efficient model training in a
widespread area but also incurs additional security and privacy challenges from
intermediate model aggregations and remains the single point of failure issue.
To tackle these challenges, we propose a blockchain-based HFL (BHFL) system
that operates a permissioned blockchain among edge servers for model
aggregation without the need for a centralized cloud server. The employment of
blockchain, however, introduces additional overhead. To enable a compact and
efficient workflow, we design a novel lightweight consensus algorithm, named
Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for
local model training. Specifically, the leader node is selected by evaluating
the intermediate FEL models from all edge servers instead of other
energy-wasting but meaningless calculations. This design thus improves the
system efficiency compared with traditional BHFL frameworks. To prevent model
plagiarism and bribery voting during the consensus process, we propose
Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth
Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to
motivate continuous contributions from clients to the learning task.
Experimental results demonstrate that our proposed BHFL system with the
corresponding consensus protocol and incentive mechanism achieves
effectiveness, low computational cost, and fairness
5-{(2S,3R,4S,5S,6R)-3,4-DihydrÂoxy-6-hydroxyÂmethÂyl-3-[(2S,3R,4R,5R,6S)-3,4,5-trihydrÂoxy-6-methylÂtetraÂhydroÂpyran-2-yloxy]tetraÂhydroÂpyran-2-yloxy}Â-7-hydrÂoxy-2-(4-hydroxyÂphenÂyl)chromen-4-one monohydrate
In the title compound, C27H30O14·H2O, the hydroxyÂphenyl ring makes a dihedral angle of 20.05 (11)° with the chromenone ring system. The crystal structure is stabilized by intra- and interÂmolecular O—H⋯O hydrogen bonds. The absolute configuration was assigned on the basis of an analagous structure
Experimental Demonstration of Deterministic Chaos in a Waste Oil Biodiesel Semi-Industrial Furnace Combustion System
In this paper, the nonlinear dynamic characteristics of the oxygen-enriched combustion of waste oil biodiesel in semi-industrial furnaces were tested by the power spectrum, phase space reconstruction, the largest Lyapunov exponents, and the 0-1 test method. To express the influences of the system parameters, experiments were carried out under different oxygen content conditions (21%, 25%, 28%, 31%, and 33%). Higher oxygen enrichment degrees contribute to finer combustion sufficiency, which produces flames with high luminance. Flame luminance and temperature can be represented by different gray scale values of flame images. The chaotic characteristics of gray scale time series under different oxygen enrichment degrees were studied. With increased oxygen content, the chaotic characteristics of flame gradually developed from weak chaos to strong chaos. Furthermore, the flame maintained a stable combustion process in a high-temperature region. The stronger the chaotic characteristics of the flame, the better the combustion effect. It can be seen that the change of initial combustion conditions has a great influence on the whole combustion process. The results of several chaotic test methods were consistent. Using chaotic characteristics to analyze the waste oil biodiesel combustion process can digitize the combustion process, find the best combustion state, optimize, and precisely control it
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