A Lightweight Unsupervised Intrusion Detection Model Based on Variational Auto-Encoder.

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

An Access Control System Based on Blockchain with Zero-Knowledge Rollups in High-Traffic IoT Environments.

The access control (AC) system in an IoT (Internet of Things) context ensures that only authorized entities have access to specific devices and that the authorization procedure is based on pre-established rules. Recently, blockchain-based AC systems have gained attention within research as a potential solution to the single point of failure issue that centralized architectures may bring. Moreover, zero-knowledge proof (ZKP) technology is included in blockchain-based AC systems to address the issue of sensitive data leaking. However, current solutions have two problems: (1) systems built by these works are not adaptive to high-traffic IoT environments because of low transactions per second (TPS) and high latency; (2) these works cannot fully guarantee that all user behaviors are honest. In this work, we propose a blockchain-based AC system with zero-knowledge rollups to address the aforementioned issues. Our proposed system implements zero-knowledge rollups (ZK-rollups) of access control, where different AC authorization requests can be grouped into the same batch to generate a uniform ZKP, which is designed specifically to guarantee that participants can be trusted. In low-traffic environments, sufficient experiments show that the proposed system has the least AC authorization time cost compared to existing works. In high-traffic environments, we further prove that based on the ZK-rollups optimization, the proposed system can reduce the authorization time overhead by 86%. Furthermore, the security analysis is presented to show the system's ability to prevent malicious behaviors.

Potential for volatile fatty acid production via anaerobically-fermenting rice straw pretreated with silage effluent and phenyllactic acid.

To resolve environmental problems associated with rice straw and silage effluent disposal, silage effluent pretreating rice straw for the anaerobic production of volatile fatty acids (VFAs) was investigated. To prevent the lactic acid bacteria in silage effluent from inhibiting anaerobic fermentation, four phenyllactic acid (PLA) levels were set (0, 0.1, 0.3, 0.5 mg/kg). The total VFA yields of treatments pretreated only with silage effluent (CK) were higher than the groups combined with PLA during 15 days fermentation. Compared to PLA treatments, the total VFA of CK increased by 11.4 % ∼ 25.1 % on day 15. The CK showed higher lactic and propionic acid contents and lower pH values (<4.9). The PLA treatments decreased Lactobacillus abundance while increasing bacterial richness and evenness, and acetic and butyric acid contents. These demonstrated silage effluent has the potential to be used as a biological pretreatment for VFA production in anaerobic fermentation.

Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention.

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as "attention" to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.

High-Speed Extraction of Regions of Interest in Optical Camera Communication Enabled by Grid Virtual Division.

Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.

High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio.

Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED's emission power and the receiver's height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED's emission power is normally time-varying due to the fact that the LED's optical output power is prone to changing with the LED's temperature, and the receiver's height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED's emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver's height and LED's emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.

Antioxidant Activities of Hot Water Extracts from Mycelial Biomass of Different Combinations of Medicinal Agaricomycetes Mushrooms.

This study aimed to determine the free radical scavenging and antioxidant potential of hot water extracts prepared from different combinations and ratios of submerged cultivated mycelial biomass of medicinal mushrooms. Total phenolic compounds, flavonoid content, and antioxidant activity were evaluated for combined crude hot water extracts from medicinal higher Basidiomycetes mushrooms belonging to ten genera. The results demonstrate that almost all tested combinations were good sources of phenolic compounds and flavonoids, ranging between 16.42 and 18.83 gallic acid equivalents/g and 1.5 and 4.34 mg rutin equivalents/g, respectively. Moreover, free radical scavenging properties were evaluated with the DPPH and ABTS assays and metal chelating effects were investigated. All tested samples and/or extracts demonstrated significant free radical scavenging properties and antioxidant potential.

Effects of LAB Inoculants on the Fermentation Quality, Chemical Composition, and Bacterial Community of Oat Silage on the Qinghai-Tibetan Plateau.

Lactic acid bacteria (LAB) have been proposed for the control of undesirable fermentation and, subsequently, aerobic deterioration due to their ability to produce antimicrobial metabolites in silage mass. To investigate the effect of specific LAB on the silage fermentation characteristics and bacterial community composition of oat in cold regions, silages were treated without (control) or with three LAB strains (LB, Lentilactobacillus buchneri; nLP, low temperature tolerant Lactiplantibacillus plantarum; pLP, phenyllactic acid-producing Lactiplantibacillus plantarum), and then stored at ambient temperature (−2.63 ± 5.47−14.29 ± 5.48 °C) for 30, 60, and 90 days. Compared with control, inoculation of LAB decreased the final pH value, butyric acid content, ammonia-N of total N and dry matter loss of silage. Treatments with nLP and pLP increased (p < 0.05) lactic acid content, whereas LB increased (p < 0.05) acetic acid content of silage. Lactiplantibacillus and Leuconostoc dominated in the silages with relative abundance of 68.29−96.63%. A prolonged storage period enhanced the growth of Leuconostoc in pLP-treated silage. In addition, pLP increased (p < 0.05) the aerobic stability of silage as compared with nLP. In conclusion, inoculation of LAB improved silage fermentation and/or delayed aerobic deterioration by shifting bacterial community composition during ensiling. Phenyllactic acid-producing Lactiplantibacillusplantarum as an inoculant exhibited potential for high quality silage production.

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling.

With the development of the Internet of Things for smart grid, the requirement for appliance monitoring has become an important topic. The first and most important step in appliance monitoring is to identify the type of appliance. Most of the existing appliance identification platforms are cloud based, thus they consume large computing resources and memory. Therefore, it is necessary to explore an edge identification platform with a low cost. In this work, a novel appliance identification edge platform for data gathering, capturing and labeling is proposed. Experiments show that this platform can achieve an average appliance identification accuracy of 98.5% and improve the accuracy of non-intrusive load disaggregation algorithms.

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation.

BACKGROUND: It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images' lack of spatial information. OBJECTIVE: The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. METHODS: We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. RESULTS: A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. CONCLUSIONS: According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.

Inclusion of abandoned rhubarb stalk enhanced anaerobic fermentation of alfalfa on the Qinghai Tibetan Plateau.

To investigate the effects of lactic acid bacteria inoculant (LI) and abandoned rhubarb stalk (RS) on the anaerobic fermentation and bacterial community of alfalfa on the Qinghai Tibetan Plateau, the alfalfa was harvested and ensiled without (control) or with LI and RS at ambient temperature (5 âˆ¼ 15℃) for 90 days. Addition of RS at ensiling increased (P < 0.05) lactate, acetate and propionate contents, and decreased (P < 0.05) the final pH value as compared with control. Addition of RS increased (P < 0.05) the bacterial alpha diversity indices, while inherent Lactococcus lactis and/or Lactobacillus sakei dominated the anaerobic fermentation. In particular, addition of RS restricted the growth of yeasts and Lactobacillales at the early stage of ensiling, but continuously stimulated anaerobic fermentation. These indicates that RS could be used as additive to facilitate anaerobic fermentation of alfalfa.

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems.

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

Improved optical camera communication systems using a freeform lens.

Optical camera communication (OCC) systems, which utilize image sensors embedded in commercial-off-the-shelf devices to detect time and spatial variations in light intensity for enabling data communications, have stirred up researchers' interest. Compared to a direct OCC system whose maximum data rate is strongly determined by the LED source size, a reflected OCC system can break that limitation since the camera captures the light rays reflecting off an observation plane (e.g., a wall) instead of those light rays directly emanated from the light source. However, the low signal-to-noise ratio caused by the non-uniform irradiance distribution produced by LED luminaire on the observation plane in current reflected OCC systems cannot be avoided, hence low complexity and accurate demodulation are hard to achieve. In this paper, we present a FreeOCC system, which employs a dedicatedly tailored freeform lens to precisely control the propagation of modulated light. A desired uniform rectangular illumination is produced on the observation plane by the freeform lens, yielding a uniform grayscale distribution within the received frame captured by the camera in the proposed FreeOCC system. Then, the received signal can be easily demodulated with high accuracy by a simple thresholding scheme. A prototype of the FreeOCC system demonstrates the high performance of the proposed system, and two pulse amplitude modulation schemes (4-order and 8-order) are performed. By using the freeform lens, the packet reception rate is increased by 35% and 32%, respectively; the bit error rate is decreased by 72% and 59%, respectively, at a transmission frequency of 5 kHz. The results clearly show that the FreeOCC system outperforms the common reflected OCC system.

Effects of Additives on Silage Fermentation Characteristic and In Vitro Digestibility of Perennial Oat at Different Maturity Stages on the Qinghai Tibetan.

To effectively use local grass resources to cover the winter feed shortage on the Qinghai-Tibetan Plateau, the silage fermentation and in vitro digestibility of perennial oat (Helictotrichonvirescens Henr.) were investigated. Perennial oat was harvested at the heading/flowering stage, wilted under sunny conditions, chopped, vacuumed in small bag silos, and stored at ambient temperatures (5-15 °C) for 60 days. The silages were treated without (CK) or with local lactic acid bacteria (LAB) inoculant (IN1), commercial LAB inoculant (IN2), and sodium benzoate (BL). Control silages of perennial oat at early heading stage showed higher (p < 0.05) lactate and acetate contents and lower (p < 0.05) final pH, butyrate, and ammonia-N contents than those at the flowering stage. High levels of dry matter recovery (DMR) and crude protein (CP) were observed in IN1- and BL-treated silages, with high in vitro gas production and dry matter digestibility. Compared to CK, additives increased (p < 0.05) aerobic stability by inhibiting yeasts, aerobic bacteria, and coliform bacteria during ensiling. In particular, the local LAB inoculant increased (p < 0.05) concentrations of lactate, acetate and propionate, and decreased concentrations of butyrate and ammonia-N in silages. This study confirmed that local LAB inoculant could improve the silage quality of perennial oat, and this could be a potential winter feed for animals such as yaks on the Qinghai Tibetan Plateau.

Study on the bacterial community structure and fermentation characteristics of fresh and ensiled paper mulberry.

Two experiments were conducted to investigate the bacterial community of fresh and ensiled paper mulberry prepared with or without lactic acid bacteria (LAB) inoculants in South China. In Experiment 1, the bacterial community, chemical composition, and fermentation products of paper mulberry were analyzed. The results showed that fresh paper mulberry had high crude protein content, buffering capacity value, and amounts of uncultured bacteria. Ensiled paper mulberry showed poor fermentation with high pH value, ammonia-N content, and butyric acid content. In addition, Enterobacter was the dominant genus in silage, followed by Lactobacillus and Enterococcus. Water-soluble carbohydrates, ammonia-N, propionic acid, pH, and lactic acid (LA) were the main factors affecting bacterial community of silage. In Experiment 2, the BP17 (Lactobacillus plantarum) from natural fermented paper mulberry silage and two commercial inoculants (Silage-help [SH] and Chikuso-1 [CH]) were used as additives. Compared with other treatments, BP17 inoculant decreased (p < 0.05) pH and ammonia-N content and increased (p < 0.05) LA content of silage. Inoculation of BP17 also increased the dominance of desirable Lactobacillus and inhibited the growth of harmful bacteria in silage. These results confirmed that paper mulberry could be ensiled and epiphytic LAB inoculant can improve its fermentation quality.

Milk Production and Quality of Lactating Yak Fed Oat Silage Prepared with a Low-Temperature-Tolerant Lactic Acid Bacteria Inoculant.

This study aimed to investigate the effect of oat silage treated with a low-temperature-tolerant lactic acid bacteria (LAB) inoculant on milk yield and the quality of lactating yaks. Oat silages were prepared in big round bales, treated without (control) or with a low-temperature-tolerant LAB inoculant (a mixture of Lactobacillus plantarum BP18, Pediococcus pentosaceus HS1 and Lactobacillus buchneri LP22; the application rate of 105 cfu/g on a fresh matter basis). Eighteen lactating yaks were divided into nine pairs with a similar milk yield. Each pair of yaks was randomly allocated to the control or LAB-inoculated silage treatment. The inoculated silage increased the dry matter intake and the total volatile fatty acid (mainly acetate, propionate and butyrate) in rumen fluid compared with the control. The inoculated silage also enhanced the yield of yak milk with high contents of total N, fat and lactose. In addition, high levels of essential amino acids (Thr, Leu and Phe), polyunsaturated fatty acids and low saturated fatty acids were observed in milk when lactating yaks were fed with the inoculated silage. Therefore, inoculation with a low-temperature-tolerant LAB during ensiling could promote the milk yield of lactating yaks by enhancing dry matter intake and ruminal fermentation.

Chemical Composition and Antioxidant Properties of Different Combinations of Submerged Cultured Mycelia of Medicinal Mushrooms.

This research describes the investigation of submerged cultivated mycelial biomass and hot water extracts prepared from different combinations and ratios of medicinal mushroom (MM) dry powders, comprising various biologically active compounds/secondary metabolites. In particular, it was evaluated the proximate composition (moisture, ash, crude protein, fat, total carbohydrates, and total energy), γ-aminobutyric acid (GABA) and ergothioneine (ERG), amino acid content of mycelia of 16 higher Basidiomycetes MM species. The results obtained demonstrate that almost all tested combinations were found to be good sources of polysaccharides, with content varying in the ranges of 4.73 ± 1.33% and 58.46 ± 4.15%. Total protein contents varied in 1.97 ± 0.40% - 5.37 ± 0.40% range. ERG was detected in all tested samples, while GABA existed only in eight samples out of 15 and varied from 0.03 ± < 0.01 to 0.61 ± 0.03 mg/g, and from 0.16 ± 0.03 to 5.69 ± 0.41 mg/g respectively. Analyses of total phenolic and flavonoid contents demonstrate considerable content in all samples (15.53 ± 0.23 - 18.88 ± 0.34 mg gallic acid equivalents/g and 1.23 ± 0.04 - 4.34 ± 0.73 mg rutin equivalents/g respectively). In present research the complexity of samples/extracts were evaluated by multiple antioxidant assays to verify their antioxidant capacity. Determination of in vitro antioxidant activity was successfully carried out by several different methods such as 2,2-diphenyl-1-picrylhydrazyl scavenging activity, reducing power, chelating ability, hydroxyl radical scavenging activity, and 2,2'-azino-bis(3-ethylbenzothi-azoline-6-sulfonic acid scavenging activity. Therefore, all tested samples confirm the capable antioxidant activities of bioactive compounds extracted from MMs.

Effects of Delayed Harvest and Additives on Fermentation Quality and Bacterial Community of Corn Stalk Silage.

This study aimed to investigate the effects of delayed harvest and additives on the fermentation quality and bacterial community of corn stalk silage in South China. The corn stalks after ear harvest at the 0 day (D0), 7 days (D7), and 15 days (D15) were used to produce small-bale silages. The silages at each harvest time were treated without (control, CK) or with Lactobacillus plantarum (LP) and sodium benzoate (BF). The results showed that delayed harvest increased pH and acetic acid content and reduced lactic acid content in corn stalk silage (p < 0.05). Compared with CK, the additives decreased the contents of butyric acid and ammonia nitrogen (NH3-N; p < 0.05). The silage treated with LP increased the content of lactic acid and decreased pH (p < 0.05); the silage treated with BF decreased counts of coliform bacteria and yeasts and increased residual water soluble carbohydrates (WSC) content (p < 0.05). Single Molecule, Real-Time sequencing (SMRT) revealed that the abundance of L. plantarum increased, while the abundance of Lactobacillus brevis and Lactobacillus ginsenosidimutans decreased with the delayed harvest. Additives influenced the bacterial community structure of corn stalk silage, revealed by enhanced bacterial diversity on D0 and reduced on D7 (p < 0.05). Our research indicated that delayed harvest could exert a positive effect on acetic acid production, and additives could inhibit the butyric acid fermentation and protein degradation of corn stalk silage by shifting bacterial community composition.

HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet.

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.

Enhancing the performance of optical camera communication via accumulative sampling.

Deemed as a practical approach to realize Visible Light Communication on commercial-off-the-shelf devices, the Optical Camera Communication (OCC) is attracting increasing attention, thanks to its readiness to be built purely upon ubiquitous LED illuminating infrastructure and handy smartphones. However, limited by the low sampling ability of the built-in camera on a smartphone, the performance of existing OCC systems is still far away from the requirements of practical applications. To this end, we further investigate the reception ability of the smartphone's camera and propose an accumulative sampling scheme to improve the performance of the OCC system. Essentially, the proposed scheme can use all the grayscale information of the pixels projected by the LED transmitter, whereas the conventional ones normally use single row (or column) pixels for demodulating. By implementing the lightweight demodulation algorithm with accumulative sampling, we experimentally verify its effectiveness for supporting higher transmission frequency hence better performance in terms of data rate. Extensive evaluations have shown the BERs of the proposed method are over 87% and 96% lower than that provided by the baselines at a maximum transmission frequency of 5 kHz for the Samsung S8 and iPhone 8 Plus receivers, respectively.