The Underwater Wireless Sensor Network (UWSN) is crucial for private, military, and commercial maritime operations, like environmental monitoring, underwater research, scientific data collection, and underwater wireless communications. The existing protocols for Terrestrial Wireless Sensor Networks (TWSNs) could be better in terms of power efficiency, reliability, and effectiveness. Due to their unique characteristics. The energy-efficient convergent depth variation routing (ECDVR) algorithm is introduced to evaluate and reduce power consumption by calculating the distance between each node point and node depth variation. This algorithm considers the two-hop neighbour depth variation when sending data to a neighbor node with a higher forward angle toward the receiver. It calculates the receive time contribution of the Adaptive Time Difference of Arrival (ATDoA). Adaptive Time Difference of Arrival (ATDoA) The arrival time of a signal makes it simple to estimate the time difference between two signals at a node. The storage node-to-receiver approach is the one that has been proposed for determining the node’s remaining power for each information exchange. In the subsequent UWSN transmission, the sink allocates the node’s higher power when the node reduces its power. Extending the packet’s lifetime and shortening the time it takes to reach its destination boosts system performance. It outperforms the compared algorithms regarding node lifetime, dead node count, power consumption, and propagation delay.

The 6G-enabled net of Things (IoT) has recently gained traction, resolution a range of period of time application considerations. AI is vital in huge knowledge analytics as a result of it provides reliable data analysis in real time. However, there are important concerns concerning security, privacy, the coaching data, and a centralized design once mistreatment artificial intelligence to develop the big data analysis. The combination of artificial intelligence with blockchain for IoT applications is bestowed during this article, that proposes a blockchain-based IoT framework with artificial intelligence. The planned architecture’ performance is assessed using each qualitative and quantitative metrics. The outline of AI centered B.C. and BC destined AI is employed to quantify however the combination of blockchain and AI tackles specific difficulties. The planned AI-BC architecture’ performance is assessed and compared to existing qualitative activity approaches. The instructed framework outperforms existing progressive methodologies, per the results of the experiments.

Device-to-Device (D2D) communication is one of the prominent and key technique in the next-generation wireless network. In D2D communication, resources are share between Device-to-Device users and cellular users. If the resource allocation is not shared properly then, there is an interference between device-to-device users and cellular users in the network. In D2D communication to mitigate the interference and improve the channel capacity is one of the challenging tasks. So, in this paper underlay as well as overlay scenarios in Long Term Evolution-Advanced (LTE-A) using uplink resource allocation scheme is proposed. In this analysis, orthogonal frequency resources are utilized and thereby reducing the interference in the channel, but at the cost of some throughput of cell-edge users. The proposed method makes use of the same orthogonal frequency resources and simultaneously allows the D2D users to reuse the left-over frequency from the cellular users. As a result, the throughput of the cellular users, as well as the cell-edge users, remains the same whereas the throughput of the D2D users increases significantly, thereby increasing the overall system throughput.

Artificial neural network (ANN) has been applied in many fields including wireless systems. ANN has been used in the design and optimization of different antenna types. The dynamic ability of the ANN to adapt a system variable in any given application is beneficial in smart antenna design for 5/6G wireless systems. This chapter presents a novel ANN algorithm, the single neuron weight optimization model (SNWOM), that optimizes the radiation patterns of uniform linear and nonlinear array (ULA) smart antenna in a desired direction. The robustness of the algorithm was compared against the least mean square (LMS) algorithm for three different functions, namely, the hyperbolic tangent, bipolar, and squash or Elliot functions, for varying number of antenna array elements. SNWOM showed excellent performance as a smart antenna beamformer. The benefit of SNWOM includes fast convergences and demands less hardware resources to offer the favorable performance.

A multiport multiple-input-multiple-output (MIMO) antenna is designed and proposed with E- and U-shaped slots and a defected ground structure (DGS) on the base portion of the inexpensive substrate material. The proposed structure is comprised of a two port MIMO antenna, and E-shape and U-shape slot antennas are positioned in the patch antenna with circular and rectangular defects in the ground plane. A parasitic structure is employed between the two antennas to achieve better isolation. An isolation of -34.4 dB is achieved by embedding a parasitic structure on the substrate. The proposed intent achieved a bandwidth of 5.25-9.01 GHz with a return loss (RL) of -24.38 dB and a gain is 5.03 dB. To realize the notched band characteristic, E- and U-shaped slots and DGS are used. Thus, a compact MIMO antenna is designed and fabricated with an overall dimension of 25 × 38 mm2. Moreover, the prototyped antenna provides high isolation, enhanced gain, and excellent RL. In addition, other MIMO antenna factors, such as envelope correlation coefficient and total active reflection coefficient, have also been studied and found to be at a satisfactory level. Therefore, the proposed multiport with a DGS antenna makes it realistic for ultra-wideband (UWB) applications.

A less expensive miniaturized microstrip tri-band antenna to be used in microwave communication systems is proposed with this paper. By modifying the rectangular radiating element in addition to the Defected Ground plane Structure (DGS), the proposed antenna is obtained. The introduction of E-shape in the radiating patch provides the multiple band frequencies such as 7.65 GHz, 11.50 GHz, and 15.20 GHz and covers C, X, and Ku-bands. An overall dimension of the miniaturized antenna becomes 23.874 × 19.990 × 1.600 mm3 with an operating frequency band from 4-16 GHz. The HFSS is used to simulate the E-shaped antenna with the DGS antenna. The experimental validation is presented to exhibit the characteristics of the proposed antenna. The inclusion and modification of dual E-shape patch achieve increased gain and enables tri-band operations. The obtained result demonstrates a return loss of -16.05 dB, -25.02 dB, and -22.03 dB at 7.65 GHz, 11.50 GHz and 15.20 GHz, respectively.

In this chapter is presented a computationally efficient and fast method to construct smart antennas using an analytical, phase shift technique. A phase shifting method for a nonlinear array antenna is described. The array antenna elements are arranged in a regular polygon geometry, and it is shown that by controlling the electronic signal phase of the array elements, without any extra calculations as in most smart antennas, the antenna beam may be steered through 360ffi. Moreover, it is only a single beam that is generated, with minimal side lobes and no back lobes so that the wireless system is specifically focused toward a single direction, minimizing interference from other transmitters or multipath.

Smart vehicle monitoring and tracking system powered by active radio frequency identification and Internet of Things (SVMT-ARFIoT) technology is proposed, which is cost-effective and more secured. The system gives tracking assistance over the connected devices. The advantage of smart vehicle monitoring system powered by active radio frequency identification tag and Internet of Things (SVM-ARFIoT) technology over global positioning system (GPS) is that GPS is very costly and its functionality is not secured, that is, prone to hack. When a GPS-enabled device is switched off, the device is out of the tracking/coverage area; hence, it can be driven unmonitored. Hence, there is a need for a system that is more secured by continuous tracking and cost-effective. The layout of the total area is initially gridded based on the geographical area. The active radio frequency identification (RFID) transmitters are equipped in a mobile fashion, and they have been housed in the vehicle. The RF wireless sensors serve as stationary active RFID receivers that are placed as per the range of detection on the basis of the geographical gridding. The stationary active RFID receivers are retained in the range of specified zones. The wireless sensor network modules with the Internet of Things (IoT) transceiver module (ESP8266) push the data that have been harvested from the field to the IoT domain for monitoring webpage support. The database encompasses the information concerning location, recorded date and time, and time stamp in that particular zone for security purpose. Lending customers have access to all the vehicles by attaching via SVMT-ARFIoT. Therefore the vehicles are readily made for monitoring and tracking purposes with less cost and high security.

Achieving energy efficiency is a critical issue of cooperative communication in Wireless Sensor Networks (WSNs). This paper proposes hamming coded cooperative communication for enhancing energy efficiency. An energy model for the proposed hamming coded cooperative communication is derived. The influence of the hamming code rate, path loss, Bit Error Rate (BER), and distance on energy efficiency is also investigated. Through the results, it is inferred that the proposed hamming coded cooperation is efficient than conventional uncoded cooperation.

This paper presents a fifth-generation (5G) wireless smart antenna for performing both power substation communication (in space domain beam-steering) and electrostatic discharge (in time domain Ultra-high Frequency “UHF” impulse) detection. The same smart antenna used to communicate with other wireless antennas in the switchyard, as well as with the control room, is utilized to cyclically gather data from power apparatus, busbars, and switches where electrostatic discharge (ESD) may occur. The ESD poses a major threat to electrical safety and lifetime of the apparatus as well as the stability of the power system. The same smart antenna on which beam rotation in space-domain is designed by implementing an artificial neural network (ANN) is also trained in time-domain to identify any of the received signals matching the ultra-high frequency band electrostatic discharge pulses that may be superimposed on the power frequency electric current. The proposed system of electrostatic discharge detection is tested for electrostatic pulses empirically simulated and represented in a trigonometric form for the training of the Perceptron Neural model. The working of the system is demonstrated for electrostatic discharge pulses with rising times of the order of one nanosecond. The artificial intelligence system driving the 5G smart antenna performs the dual roles of beam steering for 5G wireless communication (operating in the space domain) and for picking up any ESD generated UHF pulses from any one of the apparatus or nearby lightning leaders (operating in the time domain).

In the recent past, a lot of researches have been put into designing a Multiple-Input-Multiple-Output (MIMO) system to provide multimedia services with higher quality and at higher data rate. On par with these requirements, a novel Quasi Orthogonal Space Time Block Code (QOSTBC) scheme based on code word diversity is proposed, which is a multi-dimensional approach, in this paper. The term code word diversity is coined, since the information symbols were spread across many code words in addition to traditional time and spatial spreading, without increasing transmission power and bandwidth. The receiver with perfect channel state information estimates the transmitted symbols with less probability of error, as more number of samples is available to estimate given number of symbols due to the extra diversity due to code words. The simulation results show a significant improvement in the Bit Error Rate (BER) performance of the proposed scheme when compared with the conventional schemes.

Bitcoin, the first cryptocurrency is believed to be designed by Satoshi Nakamoto in 2009 as a peer-to-peer structure whereby users can handle directly without requiring an intermediary. Cryptocurrencies have enjoyed some success and 'bitcoin' is now the largest cryptocurrency, with the total number of bitcoins currently valued at approximately 70 billion US dollars. However, globally there are economies which favour the bitcoin and some have banned the same. While many day traders have cash out their funds, veteran traders remain unfazed. In this scenario, it is essential to look into their price behaviour which reveals that there is huge a fluctuation, i.e., highly volatile in nature. Henceforth, their relationships with the trading volume, money supply, lag prices which influences the trade in bitcoin are measured using the ANN model revealing highly significant relationship.

In this paper, we review our recent, reported work on using artificial intelligence based software technique to control electronic sensor or wireless communication equipment in narrow and diverging paths such as in underground tunnels and at traffic junctions. In order to make the systems fast as well as needing minimal computational calculations and memory – thus to extend the battery life and minimize cost – we used the single layer Perceptron to successfully accomplish the formation of beams which may be changed according to the nature of the junctions and diverging paths the mobile or stationary system is to handle. Moreover, the beams that survey the scenario around (e.g. in case of guiding a driverless vehicle) or communicating along tunnels (e.g. underground mines) need to be kept narrow and focused to avoid reflections from buildings or rough surfaced walls which will tend to significantly degrade the reliability and accuracy of the sensor or communicator. These requirements were successfully achieved by the artificial intelligence system we developed and tested on software, awaiting prototype development in the near future.

In telecommunication systems and radars, the common practice in using array antennas is to place a reflector behind the array so as to reflect the backward signal also in the forward direction. Moreover, in the 5G wireless systems, smart antennas, especially those with a single beam, are expected to play a critical role in its successful launching in 2020. We show in this paper that a linear array antenna necessarily ends up with symmetrical beamforming on both sides of the array axis. Thus, single direction (forward direction) beamforming cannot be achieved by placing the electromagnetic radiators (e.g. dipole elements) in a straight line. We propose that in situations where a smart array structure demands single rotatable beams, that single rotatable beamforming can be achieved by changing the geometrical shape of the array. However, the computational intensity involved in finding optimized weight coefficients for beamforming over the entire 360o space turns into the major challenge. In order to minimize the computational repetition of optimizing weights for every direction, a regular polygon array antenna is proposed. We show that an array antenna placed in a regular polygon yields a smart antenna with a highly effective and computationally fast, reduced memory and electronically rotatable single beam.

Successful development of a fast, low memory Perceptron Artificial Neural Network (ANN) based electromagnetic signal processor for array sensors has spurred the interest in embedding the electromagnetic signal processor in highly portable (e.g. to be attached to a drone) Arduino as well as in fast parallel Graphic processing Unit (GPU) based systems. In this paper we report the initial results on the embedded system, while also improving on the perceptron based array beam forming with effective activation functions. The paper also explores the effectiveness of different geometrical arrangements of the array system for different applications from wireless communication, medical EEG data acquisition and analysis to power cable condition monitoring and diagnosis.

Array antennas have a nonlinear, complex relationship between the antenna beams generated and the array input functions that generate the steerable beams. In this paper we demonstrate the use of a simple, computationally less intensive Perceptron Neural Network with non-linear sigmoid activation function to do the synthesis of the desired antenna beam. The single neuron is used, where its optimized weights will yield the beam shape required. This paper presents a successfully implemented Perceptron and discusses the error between the desired and Perceptron generated beams The successful beam control gives high accuracy in the maximum radiation direction of the desired beam, as well as optimization in the direction of null points. Moreover, a comparison between the array antenna beams obtained using the Perceptron Single Neuron Weight Optimization method (SNWOM) and the optimized beams obtained using the Least Mean Square (LMS) method, further demonstrates the reliability and accuracy of the Perceptron based beamformer. The tests were performed for two different desired antenna beams: one braod side beam and the other with the antenna radiating in four different desired directions. The Perceptron based antenna may be embedded in the Arduino microcontroller used. It is also shown why it is not possible to get a single beam, linear array antenna with the Perceptron based array reported herein.

In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.