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In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.

Following the rapid development of information and communication technology, and the huge amounts of data that have undergone explosive growth, artificial intelligence and machine learning have been used for predictive analysis in many fields. However, the prediction accuracy of these machine learning recognition models depends on the quality of the features selected for training. It is therefore very important to analyse characteristics that are meaningful and in line with the target variables as the training conditions for machine learning recognition models. In this paper, we analyse the correlation between features and target variables using the Pearson product-moment correlation coefficient, and integrate transfer learning technology for sequential feature extraction to enhance the prediction accuracy of a machine learning recognition model for the prediction of multiple crop pests and diseases as the performance verification target of the proposed method. The performance of our machine learning recognition model is compared with schemes in related work, and our approach is shown to increase the prediction accuracy by between 3% and 15%.

The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.

In today's society, the number of people rearing pets has increased and their awareness of the need to protect pets' health has increased. Pet posture behaviour analysis and prediction are providing assistance in the medical treatment of pets. Hence, the demand for pet skeleton drawing applications has risen dramatically. Our proposed system predicts pet posture using smart camera networks powered by the artificial intelligence of things. This system is built on a platform using a Raspberry Pi embedded system. The system can determine from an image whether there is a detection target and generate a contour mask based on Mask R-CNN Technology. According to object detection, poses and key parts can be identified to predict and draw pet skeletons. Simultaneously, the behavioural action of a pet can be determined according to continuous skeleton data and then the system will actively inform the owner to perform subsequent processing.

The MapReduce architecture can reliably distribute massive datasets to cloud worker nodes for processing. When each worker node processes the input data, the Map program generates intermediate data that are used by the Reduce program for integration. However, as the worker nodes process the MapReduce tasks, there are differences in the number of intermediate data created, due to variation in the operating-system environments and the input data, which results in the phenomenon of laggard nodes and affects the completion time for each small-scale cloud application task. In this paper, we propose a dynamic task adjustment mechanism for an intermediate-data processing cycle prediction algorithm, with the aim of improving the execution performance of small-scale cloud applications. Our mechanism dynamically adjusts the number of Map and Reduce program tasks based on the intermediate-data processing capabilities of each cloud worker node, in order to mitigate the problem of performance degradation caused by the limitations on the Google Cloud platform (Hadoop cluster) due to the phenomenon of laggards. The proposed dynamic task adjustment mechanism was compared with a simulated Hadoop system in a performance analysis, and an improvement of at least 5% in the processing efficiency was found for a small-scale cloud application.

In this paper, we propose a smart campus care and guiding framework with deep learning-based face recognition, called DeepGuiding, for students through Internet of Things technologies. The DeepGuiding framework can construct the dedicated video trajectory of a campus student, where the recorded video for each student can be automatically classified to achieve efficient footprint review as necessary. In addition, DeepGuiding can provide time-efficient indoor and outdoor guiding in a campus to quickly reach places, meet friends, and find students. To the best of our knowledge, DeepGuiding is the first campus care and guiding system which provides the following features: 1) it achieves the seamless outdoor and indoor navigation between buildings in a campus; 2) it keeps additional construction cost low by utilizing existing surveillance cameras in a campus; and 3) it reduces the total searching time for finding a specific event/target in a campus by alleviating time-consuming labor overhead to review a huge amount of video data. An Android-based prototype using iBeacon indoor localization and global positioning system outdoor positioning with surveillance cameras is implemented to verify the feasibility and superiority of our DeepGuiding framework. The Experimental results show that DeepGuiding outperforms existing face recognition methods and can achieve high recognition accuracy for students not close to surveillance cameras.

Photodynamic therapy (PDT) provides a potential therapeutic approach for killing malignant cell/solid tumors, but currently approved photosensitizers (PSs) are generally excited by visible light, limiting the penetration depth in tissues. It is necessary to develop a near-infrared (NIR) responsive photodynamic platform, providing maximum tissue penetration. Here, we present a gold nanopeanut platform exhibiting dual functions of NIR PDT and two-photon luminescence imaging. The nanopeanut with a size less than 100 nm exhibits two distinct NIR surface plasmon absorption bands at approximately 1110 and 1300 nm. To perform PDT, we conjugated commercial toluidine blue O (TBO) PS on the surface of the nanopeanuts. With spectral overlap, the 1230-nm femtosecond Cr: forsterite laser can excite the surface plasmons of nanopeanuts, transfer energy to TBO, and generate singlet oxygen to kill cells. Moreover, the plasmon resonance-enhanced two-photon luminescence of nanopeanuts can be used to map their delivery in vivo. These results demonstrate that the PS-conjugated gold nanopeanut is an effective theranostic system for NIR PDT.

Chondroitin sulfate (ChS) is usually used as an oral nutraceutical supplement, and has been popular in Asia, Europe, and United States for many years. In this study, a potential and sustainable source of ChS from jumbo squid (Dosidicus gigas) cartilage was explored; ultrasound-assisted extraction (UAE) was used to extract ChS from jumbo squid cartilage. The result of mass transfer coefficients based on Fick’s law showed that UAE had higher mass transfer efficacy. The response surface methodology (RSM) combined with Box–Behnken design (BBD) was employed to evaluate the effects of the extraction parameters. The optimal conditions were extraction temperature of 52 °C, extraction time of 46 min, and NaOH concentration of 4.15%. The crude extract was precipitated by 50% ethanol, which obtained a purified ChS with 23.7% yield and 82.3% purity. The purified ChS measured by energy-dispersive X-ray spectroscopy (EDX) had a carbon to sulfur molar ratio of approximately 14:1. The FTIR, 1H, and 13C NMR confirmed jumbo squid ChS were present in the form of chondroitin-4-sulfate and chondroitin-6-sulfate, with a 4S/6S ratio of 1.62. The results of this study provide an efficient process for production and purification of ChS, and are significant for the development and utilization of ChS from jumbo squid cartilage in the nutrient food or pharmaceutical industries.

Refined cobia liver oil is a nutritional supplement (CBLO) that is rich in polyunsaturated fatty acids (PUFAs), such as DHA and EPA; however, PUFAs are prone to oxidation. In this study, the fabrication of chitosan-TPP-encapsulated CBLO nanoparticles (CS@CBLO NPs) was achieved by a two-step method, including emulsification and the ionic gelation of chitosan with sodium tripolyphosphate (TPP). The obtained nanoparticles were inspected by dynamic light scattering (DLS) and showed a positively charged surface with a z-average diameter of between 174 and 456 nm. Thermogravimetric analysis (TGA) results showed the three-stage weight loss trends contributing to the water evaporation, chitosan decomposition, and CBLO decomposition. The loading capacity (LC) and encapsulation efficiency (EE) of the CBLO loading in CS@CBLO NPs were 17.77–33.43% and 25.93–50.27%, respectively. The successful encapsulation of CBLO in CS@CBLO NPs was also confirmed by the Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) techniques. The oxidative stability of CBLO and CS@CBLO NPs was monitored by FTIR. As compared to CBLO, CS@CBLO NPs showed less oxidation with a lower generation of hydroperoxides and secondary oxidation products after four weeks of storage. CS@CBLO NPs are composed of two ingredients that are beneficial for health, chitosan and fish oil in a nano powdered fish oil form, with an excellent oxidative stability that will enhance its usage in the functional food and pharmaceutical industries.

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human-machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

This paper introduces a novel algorithm that increases the efficiency of the current cloud-based smart-parking system and develops a network architecture based on the Internet-of-Things technology. This paper proposed a system that helps users automatically find a free parking space at the least cost based on new performance metrics to calculate the user parking cost by considering the distance and the total number of free places in each car park. This cost will be used to offer a solution of finding an available parking space upon a request by the user and a solution of suggesting a new car park if the current car park is full. The simulation results show that the algorithm helps improve the probability of successful parking and minimizes the user waiting time. We also successfully implemented the proposed system in the real world.

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.