Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics

Two-dimensional permutation vectors’ (PV) code for optical code division multiple access systems

In this paper, we present a new algorithm to generate two-dimensional (2D) permutation vectors’ (PV) code for incoherent optical code division multiple access (OCDMA) system to suppress multiple access interference (MAI) and system complexity. The proposed code design approach is based on wavelength-hopping time-spreading (WHTS) technique for code generation. All possible combinations of PV code sets were attained by employing all permutations of the vectors with repetition of each vector weight (W) times. Further, 2D-PV code set was constructed by combining two code sequences of the 1D-PV code. The transmitter-receiver architecture of 2D-PV code-based WHTS OCDMA system is presented. Results indicated that the 2D-PV code provides increased cardinality by eliminating phase-induced intensity noise (PIIN) effects and multiple user data can be transmitted with minimum likelihood of interference. Simulation results validated the proposed system for an agreeable bit error rate (BER) of 10−9

Wireless positioning and tracking for Internet of Things in heavy snow regions

The adaptation of localization in a real-world environment can be observed by popular commercial and non-commercial GPS applications. However, with the advent of the Internet of Things (IoT), wireless sensor networks (WSNs), these problems are again brought into focus. The requirements, such as low-cost, nodal resource, and multihop characteristics, have made difficult problems such as localization. WSNs in snowy environments can support a wide range of applications such as environmental monitoring, the rescue of snow avalanche and winter sports activities. All these applications require knowing the position of the nodes to process the event. Of course, the obvious solution to equip all nodes with a GPS module is extremely expensive and is subject to many constraints. Besides, the node position estimation is most often influenced by measurement errors. These errors depend on the nature of the environmental media in which the sensors are deployed. In this paper, the problem of node localization at 2.425 GHz in icy and snowy environments is investigated

Smart water distribution system based on IoT networks, a critical review

The purpose of this paper is to discuss different existing technologies related to sensing in smart cities. The continuous growth of urban areas is a reality that should be faced by innovating more solutions that are efficient. Smart cities are one of the remarkable solutions, it can be seen as different intelligent systems or platforms that work together to ensure better sustainability. Sensors are at the core of smart cities. They collect data from different environments or infrastructures in order to send them to the cloud using different communications platforms. These data can be used to better manage the infrastructures or provide smarter services. However, they are various issues and challenges related to the ubiquitous sensors that should be solved. In the last section of this paper, a case study of smart water distribution system is presented with an overview of the related issues and challenges such as reliability, cost, and scalability. Also, a table is provided in this section to compare the results and challenges of the last five studies on producing smart pipes with the most common challenges

Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms

Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes (normal, degraded, and faulty). The proposed solution can be deployed as predictive maintenance for Industry 4.0

Machine learning classification of hermite gaussian beams for 5G and beyond free-space optical backhaul links

Free space optical (FSO) communication offers an excellent opportunity to develop energy-efficient, secure, and ultrafast data links for 5G and beyond applications, including heterogeneous networks with massive connectivity and wireless backhauls for cellular systems. However, the effect of an optical beam's pointing inaccuracy combined with the impact of climate factors must be considered in the FSO communication system. In this paper, we first evaluate the performance reliability and availability of NRZ-based mode division multiplexing (MDM)-FSO backhaul. In particular, a single wavelength laser is used to transmit four different optical beams, using four different wavelengths. It also explores and classifies four beams used for capacity enhancement in mode division multiplexed MDM-FSO backhaul links. Several Machine Learning (ML) models are used to classify the four optical modes. Results indicate successful transmission of 80 Gbps. Furthermore, the primary findings indicate that the ML model exhibits an impressive accuracy rate of approximately 97% in classifying four distinct beams

Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures

Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced