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The general use of wireless sensor devices and their importance in terms of deployment cost, size and user-friendliness have given an increase to many different applications of cognitive radio networks. In cognitive radio networks, energy efficiency is a very important problem. In the literature, many different methods are developed by researchers which are not able to produce the best results. Hence, in this article, a hybrid technique is developed to attain energy efficiency deliberation intended for properly maintaining objective signal to interference plus noise ratio (SINR) need for the received interferences and cognitive users at a receiver of primary under a reference value. In the proposed methodology, interference threshold protection margin and protection factor to the SINR target are developed. The hybrid WOFA algorithm can be a combination of the Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA). In the WOA algorithm, updating positions of the whales are achieved with the utilisation of the FA algorithm. The proposed method is designed in the MATLAB platform, and it is compared with the existing approaches of WOA by robustness in addition to energy efficiency under the disturbance of channel gain. The proposed method is analysed with the two different cases such as the target SINR ratio condition of 3.52 dB and the target SINR ratio condition of 5.11 dB. [ABSTRACT FROM AUTHOR]

One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) attains a promising result for the several access problems and fulfils the requirements of fifth-generation (5G) networks by strengthening the service quality, like massive connectivity and energy efficiency. To achieve the power allocation of multiple users with the layered transmission, the NOMA is extended with the multiple input multiple output (MIMO) system. In this work, the allocation of power in MIMO–NOMA is optimally done with the layered transmission by the developed Fractional Salp Particle Swarm Optimisation (FSPSO) algorithm. In the MIMO–NOMA, the implemented FSPSO algorithm attained the better sum rate using allocating the powers at multiple layers of users. Also, the closed-form phrase is formed for the achievable sum rate using the Channel State Information (CSI) existing at the side of transmitter. To increase the achievable sum rate, the CSI permits the users to assign the powers at various layers. The proposed algorithm optimally allocates the power with the lower Bit Error Rate (BER) of 0.00039 and better energy efficiency, spectral power, and achievable sum rate of 20.8134 J, 181.660 dB, and 110.615 dB, respectively. [ABSTRACT FROM AUTHOR]

WSN consists of many sensors, they need high throughput and network lifetime to get involved in communication. Duty Cycle is a key factor in sensor nodes to maintain sleep, active condition in the presence of overhearing, collisions of packets while data transmission between sensor nodes. The key challenge of sensor nodes is to attain high energy efficiency, throughput and low End-End Delay. This paper presents several of existing approaches for MAC protocol. They are (1) Synchronization between nodes (2) Mathematical Analysis (3) TDMA based MAC and (4) Cross-Layer Design. We proposed a new method for MAC protocol with variable duty cycle. Neighboring nodes follow same duty cycle and same active time. Far away nodes doesn't maintain same duty cycle, hence their active time is not same. The duty cycle is maintained in synchronous way and adaptive manner among the nodes within the network. In this paper, a conventional MAC and proposed MAC are simulated using NS-2.35. Variable duty cycle approach in the proposed MAC with sleep and idle states for the nodes yields better throughput and energy efficiency with respect to other conventional technique. These results validate that the proposed variable DC-MAC is feasible for Wireless Sensor Network applications. [ABSTRACT FROM AUTHOR]