The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

IoT Connectivity Technologies and Applications: A Survey

This a book review featured in Quality Management Journal and reviewed by Nicole Radziwill...

Book review: Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business, and the World

The Industry 4.0 IoT network integration with blockchain architecture is a decentralized, distributed ledger mechanism used to record multi-user transactions. Blockchain requires a data storage system designed to be secure, reliable, and fully transparent, emerged as a preferred IoT-based digital storage on WSN. Blockchain technology is being used in the paper to construct the node recognition system according to the storage of data for WSNs. The data storage process on such data must be secure and traceable in different forensics and decision making. The primary theme of the dynamic data security is therefore for rejecting exploitation of the unauthorized user and for evaluating the mechanism in tracing and evidence of system’s data operation in a dynamic manner, growth and quality features under the stochastic state of the model; (1) a mathematical method for the secured storage of data in dynamic is built through distributed node cooperation in IoT industry. (2) the ownership transition feature and the dynamic storage of data system architecture are configured, (3) the emerging distributed storage architecture for blockchain-based WSN will substantially reduce overhead storage for each node without affecting data integrity; (4) minimize the latency of data reconstruction in distributed over storage system, and propose an effective and scalable algorithm for optimizing storage latency issue. In addition to this research, the system implements verified possession of data for replacing the evidence in original digital currency for mining and to store new data blocks that will be compared to the proof system, dramatically reduces computational capacity. The proposed ODSD framework has exceptional benefits for real-time applications while maintaining the security of the dynamic storage of data.

Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks

The study and exploration of massive multiple-input multiple-output (MMIMO) and millimeter-wave wireless access technology has been spurred by a shortage of bandwidth in the wireless communication sector. Massive MIMO, which combines antennas at the transmitter and receiver, is a key enabler technology for next-generation networks to enable exceptional spectrum and energy efficiency with simple processing techniques. For massive MIMOs, the lower band microwave or millimeter-wave band and the antenna are impeccably combined with RF transceivers. As a result, the 5G wireless communication antenna differs from traditional antennas in many ways. A new concept of the MIMO tri-band hexagonal antenna array is being introduced for next-generation cellular networks. With a total scaling dimension of 150 × 75 mm2, the structure consists of multiple hexagonal fractal antenna components at different corners of the patch. The radiating patch resonates at 2.55–2.75, 3.45–3.7, and 5.65–6.05 GHz (FR1 band) for better return loss (S11) of more than 15 dB in all three operating bands. The coplanar waveguide (CPW) feeding technique and defective ground structure in the ground plane have been employed for effective impedance matching. The deviation of the main lobe of the radiation pattern is achieved using a two-element microstrip Taylor antenna array with series feeding, which also boosts the antenna array’s bandwidth and minimizes sidelobe. The proposed antenna is designed, simulated, and tested in far-field radiating conditions and generates tri-band S-parameters with sufficient separation and high-quality double-polarized radiation. The fabrication and testing of MIMO antennas were completed, where the measurement results matched the simulation results. In addition, the 5G smartphone antenna system requires a new, lightweight phased microwave antenna (μ-wave) with wide bandwidth and a fire extender. Because of its decent performance and compact architectures, the proposed smartphone antenna array architecture is a better entrant for upcoming 5G cellular implementations.

A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication

Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

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Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Urban water resource management for sustainable environment planning using artificial intelligence techniques

https://www.techscience.com/cmc/v70n2/44647/pdf

Osamah Ibrahim Khalaf

Papers

Urban water resource management for sustainable environment planning using artificial intelligence techniques

Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks

A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Optimization of Wireless Sensor Network Coverage using the Bee Algorithm.