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Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Summary: Multiple‐input multiple‐output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a non‐convex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is a well‐developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error‐free data and run an energy‐efficient network. In this paper, we propose a sling‐shot spider optimization (S2SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)‐based networks. The S2SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1‐hop, 2‐hop, and multi‐hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio‐inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and cluster heads (CHs) has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework named as EEC‐MA‐PSOGA ‐ an energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and place the SINK optimally in WSNs. The cluster members send the collected data towards their respective CHs for aggregation. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput. [ABSTRACT FROM AUTHOR]

Summary: A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

Summary: Recently, academic and industrial research communities are paying more explicit attention to the 5G multiple radio access technology ultra‐dense networks (5G multi‐RAT UDNs) for boosting network capacity, especially in UD urban zones. To this aim, in this paper, we intend to tackle the user association problem in 5G multi‐RAT UDNs. By considering the decoupled uplink/downlink access (DUDA), we divide our user association problem into two distinct subproblems representing, respectively, the uplink and the downlink channels. Next, we formulated each one as a nonlinear optimization problem with binary variables. Then, to solve them, we were restricted by the hard complexity, as well as the hard feasibility of centralized user association schemes. Thus, to resolve our user association problem in a reasonable time and distributed manner, we formulated each subproblem as a many‐to‐one matching game based on matching theory. Next, we provide two fully distributed association algorithms to compute the uplink and downlink stable matching among user equipments (UEs) and base stations (BSs). Simulation results corroborate our theoretical model and show the effectiveness and improvement of our achieved results in terms of the overall network performance, quality of service (QoS), and energy efficiency (EE) of UEs. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we study a wireless access network based on the Institute of Electrical and Electronics Engineers 802.11 standard and enriched with features such as caching and mesh networking. This system is analysed in terms of energy efficiency and traffic offloading, two objectives that are somewhat in contrast but both relevant to network and service providers as they directly impact the operational cost. In addition, QoS is also accounted for in the form of guaranteed bandwidth and bounded delay. To this aim, we developed a mathematical model of the system and solved it to optimality by means of integer linear programming. We can thus show how much can be saved both in terms of energy and traffic, also considering various tradeoff points among the two contrasting objectives. As a last step, we provide an investigation on the benefits of adding traffic aggregation features to the mathematical model. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Because the energy consumption is growing rapidly, green radio, which lays emphasis on the energy efficiency (EE) in wireless networks, is becoming increasingly important. As potential paradigms for future wireless network design, cognitive radio (CR) is a promising technology to solve the spectrum shortage and inefficiency issues, while cooperative relay is capable of improving spectral efficiency by combating severe fading in wireless environment. In this paper, we investigate the energy-efficient power allocation problem in orthogonal frequency division multiplexing (OFDM)-based relaying CR networks. We develop a general framework to maximize the overall EE of the CR system, under the constraints of transmission power budget, traffic demands, and the interference constraints of the primary users. Our problem formulation is a nonconvex optimization task, and it is hard to obtain the optimal solution. We first convert our formulated problem into a convex optimization problem via its hypograph form, which can be solved by the barrier method. Then we further speed up the computation of Newton step during the barrier method, significantly reducing the complexity of the algorithm by exploiting its special structure. Numerical results validate that our method can exploit the overall EE of CR systems, while the algorithm converges efficiently and stably. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]