Fog computing has been an effective paradigm of real-time applications in the IoT area, which enables task offloading at network edge devices. Particularly, many emerging vehicular applications require real-time interaction between the terminal users and computation servers, which can be implemented in fog-based architecture. However, it is still challenging to apply fog computing in vehicular networks due to high mobility of vehicles and uneven distribution of vehicle density, which may result in performance degradation, such as unbalanced workload and unexpected task failure. In this article, we investigate a new service scenario of task offloading under a three-layer service architecture, where the resources of vehicular fog (VF), fog server (FS), and central cloud (CC) are utilized in a cooperative way. On this basis, we formulate the probabilistic task offloading (PTO) problem by synthesizing task transmission, computation, and result retrieval, as well as characterizing the heterogeneity of computation servers. The objective of the PTO is to minimize the weighted sum of execution delay, energy consumption, and payment cost. To resolve the PTO problem, we propose a comprehensive task offloading algorithm by combining the alternating direction method of multipliers (ADMMs) and particle swarm optimization (PSO), called ADMM-PSO. The basic idea of the ADMM-PSO is to divide the PTO problem into multiple unconstrained subproblems and achieve the optimal solution in the form of an iterative coordination process. For each iteration, the solution is achieved by solving each subproblem with the PSO and updated based on a designed rule, which is able to converge to the optimal solution when the stop criterion is satisfied. Finally, we build the simulation model and implement the proposed algorithm for performance evaluation. The simulation results demonstrate the superiority of the proposed algorithm under a wide range of service scenarios.

Stress-induced birefringence can lead to distortion in the reflection spectra of fiber Bragg grating (FBG) sensors, thereby resulting in the loss of accuracy and stability of strain measurements. The bonding layer is a direct factor in producing stress birefringence within FBGs. To assess the impacts quantitatively, a theoretical model that links the bonding layer and the reflection spectrum was established. At the same time, the finite element method, based on the theoretical model, was used to study the relationships between characteristics of the bonding layer and reflection spectrum in detail. The analytical results indicate that high elastic modulus and mismatched Poisson’s ratio of bonding layer decrease the available strain measuring range of FBGs remarkably, and that unreasonable geometric parameters of the bonding layer should be avoided. In addition, a validation experiment was conducted and experimental results proved the prediction of the theoretical analysis. It can be concluded from the results that the bonding layer is the major limiting factor for the application of surface-bonded FBG sensors in large strain measurements. The bonding materials and bonding processes used in producing FBG sensors deserve serious consideration.

Aiming at the problems of Doppler-induced error and slow measurement rate in frequency-swept interferometry, a novel dynamic clearance measuring method is proposed. By combining frequency-swept and frequency-fixed interferometry, and using fusion processing of interferometric signals, this method not only can eliminate the Doppler-induced error, but also can give the dynamic clearance value at each sampling point. To verify this method, an all fiber-optic measurement system was constructed, and experiments under 500 Hz triangular and 4 kHz sinusoidal vibrations were carried out. Experimental results show that the dynamic clearance can be reconstructed accurately.

If the measurand changes during the spectrum acquisition process, it easily leads to the failure of the classic demodulation algorithms of low-finesse optical fiber Fabry-Perot (FP) sensors. To address this problem, a novel demodulation model is proposed based on the definition of the instantaneous frequency. The proposed model establishes the relationship between the optical path length (OPL) of the FP cavity and the instantaneous frequency distribution of the FP interference spectrum. The link between the classic FFT algorithm and this model is discussed, and it is found that this model can be viewed as a generalized form of the FFT algorithm. Based on this model, the Doppler-induced demodulation error is analyzed. The analysis uncovers that the average frequency of the FP interference spectrum should be used for the evaluation of the error, and the error is proportional to the variation of OPL during the spectrum acquisition period. Further, numerical simulation and an experiment were carried out to verify the proposed model, and results show that the proposed model is effective for the dynamic low-finesse FP cavity. It is the first time that the idea of instantaneous frequency is introduced for the FP demodulation, and this model provides us a new way to cope with the FP sensing signal.

To realize accurate dynamic axial clearance measurement in high-speed rotating machinery using basic Frequency-swept interferometry (FSI), a novel algorithm of Doppler error suppression is proposed. In this algorithm, we first extract the incremental phase of the FSI signal by Hilbert transform and use it to construct a virtual clearance variation. Then, using the periodic characteristics of rotor rotation, optimized initial clearance can be obtained. Eventually, the dynamic axial clearance at each sampling point can be recovered with the optimized initial clearance and the Doppler error is greatly suppressed. The feasibility of this method is tested and verified by both simulation and experiment. For a 4 kHz rotation simulating target located at 5 mm away, the experimental results show that the dynamic clearance measurement error is less than 2.057 µm, which meets the measurement accuracy required for most high-speed rotating machinery.

Spectral distortions are complex and unpredictable in Fiber Bragg Grating (FBG) sensing applications. An inappropriate choice of FBG demodulation algorithm will lead to unacceptable results in practice. Although many demodulation algorithms have been developed for distorted spectra, they may be limited for some types of spectral distortion. Until now, no model of FBG spectral distortion has been proposed to test different demodulation algorithms. Here, we fulfill this aim by introducing a comprehensive model with asymmetrical distortion, multi-peaks distortion, interferometric noise, background noise and random noise. Using numerical simulations, we evaluate the performance of eight most typical demodulation algorithms with this model. We show that different algorithms should be used within specific ranges of spectral distortions. Altogether, this study provides a guide map as useful reference for selecting an appropriate FBG demodulation algorithm for practical applications. Furthermore, our proposed model paves the way for developing more reliable FBG demodulation algorithms for different spectral distortions.