With the growth of the number of Internet of Things (IoT) devices and the emergence of new applications, satisfying distinct QoS in the same physical network becomes more challenging. Recently, with the advance of network functions virtualization and software-defined networking (SDN) technologies, the network slicing technique has emerged as a promising solution. It can divide a physical network into multiple virtual networks, therefore providing different network services. In this article, to meet distinct QoS in industrial IoT, we design a network slicing architecture over the SDN-based long-range wide area network. The SDN controller can dynamically split the network into multiple virtual networks according to different business requirements. On this basis, we proposed a deep deterministic policy gradient (DDPG) based slice optimization algorithm. It enables LoRa gateways to intelligently configure slice parameters (e.g., transmission power and spreading factor) to improve the slice performance in terms of QoS, energy efficiency, and reliability. In addition, to accelerate the training process across multiple LoRa gateways, we leverage the transfer learning framework and design a transfer learning-based multiagent DDPG algorithm.

In this paper, we propose adaptive channel-matched detection (ACMD) to effectively compensate channel distortions for C-band 64-Gbit/s intensity-modulation and direct-detection (IM/DD) optical on-off keying (OOK) system over a 100-km dispersion-uncompensated link. The proposed ACMD can adaptively compensate most of the link distortions based on channel and noise characteristics, which includes a polynomial nonlinear equalizer (PNLE), a decision feedback equalizer (DFE) and maximum likelihood sequence estimation (MLSE). Based on the channel characteristics, PNLE eliminates the linear and nonlinear distortions, while the followed DFE compensates the spectral nulls caused by chromatic dispersion. Finally, based on the noise characteristics, a post filter can whiten the noise for implementing optimal signal detection using MLSE. To the best of our knowledge, we present a record C-band 64-Gbit/s IM/DD optical OOK system over a 100 km dispersion-uncompensated link achieving 7% hard-decision forward error correction limit using only the proposed ACMD at the receiver side.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

Rolling bearing is one of the core parts of modern industrial equipment, with high safety and reliability. Therefore, predicting its remaining useful life (RUL) is essential and far-reaching. The critical difficulty of RUL estimation technology is how to extract the features reflecting the bearing degradation trend and how to select the RUL estimation algorithm. Although the RUL estimation algorithms based on deep learning (such as CNN and RNN) have achieved remarkable results recently, these methods are not good at analyzing and predicting long-time series. We propose a transformer prediction model with a multi-scale gated convolution network (MSGCNN-TR) to solve this question to predict the RUL of rolling bearings. This model mainly includes sample feature extraction and bearing RUL estimation. The first part is a feature extractor composed of a depth convolution path with residual and a GRU path. It can effectively extract the spatial and temporal features that reflect the bearing degradation trend in the rolling bearing life data. The second part is an RUL estimation model composed of a four-layer transformer encoder. The multi-head self-attention mechanism in the Transformer encoder can effectively capture the long-term or short-term dependency relationship in the rolling bearing life data. Finally, we performed comparative and ablation experiments on the IEEE PHM2012 dataset. The results prove that the performance of the proposed model is better than other methods in the comparative experiment, and the ablation experiment proves the necessity and effectiveness of each module.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM–OAM-MDM transmission channel. Therefore, although the transmission symbols in the WDM–OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM–OAM-MDM system.

Temperature cross-sensitivity is a long-standing challenge for most of the in-line fiber optofluidic waveguide biosensors. In this paper, we propose a dual-optofluidic waveguide antiresonant reflecting optical waveguide (ARROW) biosensor for the detection of interferon-gamma (IFN-γ) concentration with temperature compensation. Two Fabry-Perot resonators infiltrated with IFN-γ and NaCl were formed in a hollow core fiber, which generate two resonance dips based on the ARROW model. The optical biosensor for the detection of interferon-gamma (IFN-γ) has been a key research interest in recent years because IFN-γ is an important early biomarker for many serious human diseases. Based on the dual-optofluidic waveguide ARROW biosensor, the IFN-γ concentration can be measured through the modulation of the resonance condition of the ARROW, while the temperature fluctuation can be eliminated due to same thermo-optic coefficients of two infiltration liquids. The experimental results show that the response of the ARROW biosensor can be amplified significantly with the signal-enhanced streptavidin, and the limit of detection of 0.5 ng/ml can be achieved for the IFN-γ concentration. More importantly, the influence of the temperature could be compensated through the referenced resonance dip. The proposed fiber biosensor has a great potential for the real-time detection of IFN-γ concentrations in the fields of health monitoring, cancer prevention, biological engineering, etc.