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Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid‐based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering‐based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid‐based dynamic sink routing scenario. The significance of clustering‐based protocols for improving lifetime of the network is evaluated as case study. The clustering‐based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks. [ABSTRACT FROM AUTHOR]

Summary: Massive multi‐input multi‐output has the advantages of high energy efficiency and spectrum use, but it also brings some problems because of cheap hardware use, such as high‐power amplifier (HPA) nonlinearity. When the HPA operates in the nonlinear regime, nonlinear distortions, including amplitude and phase distortions, are introduced into the transmitted symbols, which in turn can cause adjacent channel interference and power losses. In this paper, we first study the deficiency of the conventional precoding algorithm when the HPA is nonlinear. Then, this paper proposes the improved precoding algorithms according to the effect of HPA nonlinearity on the signal, in which the adaptive factor is achieved to largely control the amplitude and phase distortions of the nonlinear HPA. Finally, the simulation results are provided and show that the proposed algorithms in this paper based on the relative block of diagonalization precoding and the forced zero precoding can efficiently decrease the bit error rate and increase the throughput, especially in the case of high signal‐to‐noise ratio. [ABSTRACT FROM AUTHOR]

Summary Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary: Communicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency. [ABSTRACT FROM AUTHOR]

Summary: The non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS. [ABSTRACT FROM AUTHOR]

Summary: Node localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are group of nodes capable enough to communicate with other nodes wherever they are deployed. The demanding need for a wireless sensor network in various fields of monitoring and tracking has gained more popularity in recent years. However, as sensor nodes have limited and dissipating battery power, energy conservation is a critical challenge in wireless sensor networks. Clustering is one of the promising methodologies to reduce the consumption of energy. This paper presents a cluster‐based routing algorithm for transferring the data to the base station. The algorithm uses the k‐means approach to select the clusters. Each cluster consists of two cluster heads, one for aggregating the data and the other for data transmission; thereby, they share the load and improve the network lifetime. The data are sent to the base station by a multi‐hop approach. For energy efficiency, a minimum cost path is chosen by considering the energy and distance. Simulations are implemented in NS2. The results obtained show that the proposed protocol is more efficient than the benchmarked algorithms in network lifetime, packet delivery ratio, throughput, total energy consumption, and end to end delay. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a multi‐hop cognitive radio (CR) enabled device‐to‐device (D2D) network is considered to extend the communication from a functional area (FA) to a non‐functional area (NFA). A D2D user utilizes a cellular user (CU) band in the cognitive mode for information transmission while maintaining the service quality of CUs by satisfying a collision constraint. It transmits with full power in overlay mode, while the power is controlled in underlay mode. We derive the outage probability of the considered D2D network. The main challenge in CR communications lies in the system robustness to uncertainties. In this paper, we also show the impact of the noise power uncertainty in a multi‐hop D2D network. The expressions for throughput and energy efficiency are also developed under collision constraint and noise uncertainty. The outage probability, throughput, and energy efficiency are investigated for several network parameters such as collision probability, sensing time, noise uncertainty, etc. It is observed that the collision constraint and noise uncertainty have significant impacts on network performance. A simulation test‐bed is developed in MATLAB, and the results obtained in the simulation are verified with the analytical results. [ABSTRACT FROM AUTHOR]

Summary: Virtualized network functions (VNFs) are virtualized network services running on the network functions virtualization infrastructure in order to replace physical hardware. Integrating with software defined networking and network functions virtualization, internet service providers (ISPs) can chain VNFs together, called service function chain (SFC), to provide various network service demands with virtual links. Many studies have proposed for the VNFs deployment under different objectives, for example, energy, delay, quality of service (QoS), and quality of experience (QoE). From the aspects of both ISPs and customers, both energy consumption and QoE are the primary concerns for the purpose of operating expense reduction and users' satisfaction. This paper proposes a QoS/QoE/energy‐aware deep Q‐network (DQN)‐based scheme, called DQN‐QQE for VNFs deployment under the consideration of both energy consumption and QoE requirement with QoS constraints. The reward of the proposed scheme is formulated by the Weber‐Fechner law and the exponential interdependency of QoE and QoS (IQX) hypothesis. Afterwards, we compared the proposed DQN‐QQE to the QoS/QoE‐aware approach DQN‐Q2‐SFC, brute force, and randomness in terms of energy, QoE, error rate, and processing time. The simulation results revealed that DQN‐QQE is more stable and is better than DQN‐Q2‐SFC and randomness approach in terms of energy, QoE, and error rate. The average processing time and energy consumption of the proposed DQN‐QQE was 43% and 11% less than DQN‐Q2‐SFC, respectively. Although brute force approach is better than others in terms of energy, QoE, and error rate, the processing time of brute force approach is nearly 20 times larger than the proposed DQN‐QQE. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, due to the increased amount of network users and resources, some emerging technologies like big data and cloud have been developed. But managing these types of networks is more complex; in order to solve this issue, a software‐defined network (SDN) is developed for managing the network in a comfortable manner, in which maintaining load balance and energy efficiency of network is highly important to satisfy the requirements of quality of service (QoS). For these reasons, various optimization‐based algorithms are developed in the traditional works, but these lack with the limitations of increased network traffic and energy consumption. In order to solve these issues, an enhanced hybrid flower pollination algorithm (HFPA) with improved harmony search (IHS) is developed in this paper. It intends to obtain an improved resource utilization and reduced network load and delay by efficiently allocating the bandwidth to the network. Moreover, an optimal routing protocol (ORP) is employed to perform the data transmission with condensed network traffic. During experimentation, the performance results of the proposed algorithm are examined and compared with some traditional methodologies. From the results, it is noted that the proposed HFPA‐IHS‐ORP provides the better results than the existing methods. [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: Fifth‐generation (5G) networks deal with high‐frequency data rates, ultra‐low latency, more reliability, massive network capacity, more availability, and a more uniform user experience. To validate the high‐frequency rates, 5G networks engage beam searching operation. By adopting a beam searching state between the short and long sleep, one can reduce the system's delay. The energy consumption of user equipment (UE) in 5G networks is much higher than in the 4G networks. To reduce the energy consumption and increase the energy saving in UE, Long‐Term Evolution (LTE)‐5G networks adopt the discontinuous reception (DRX) scheme with a fixed number of short sleep. LTE‐DRX without beam search operation (i.e., beam alignment) cannot work in 5G networks. Hence, keeping this scenario in mind, we have modeled a new modified directional discontinuous reception (MD‐DRX) mechanism for LTE‐5G networks. The MD‐DRX mechanism captures the behavior of a beam searching, an inactive, an active, a long sleep, an ON, and a short sleep states. The short sleep state consists of a maximum M short sleep. To get the optimal energy saving and energy consumption (i.e., energy efficiency) from the MD‐DRX mechanism, it is required to check the system's throughput. The trade‐off between energy saving/energy consumption and throughput will provide the system's optimal energy saving and optimal energy consumption. In this paper, we have obtained the system's optimal energy saving and throughput by optimizing the maximum short sleep and short sleep duration. To get the energy efficiency for LTE‐5G networks, the trade‐off between average energy consumption/energy saving and throughput is shown. [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: In a high‐speed vehicular scenario, due to vehicle penetration loss (VPL) and fast signal fluctuations, frequent handovers occur, which results in more battery consumption. So power saving is a critical concern in high‐speed wireless networks. In the past, moving relay (MR)‐based heterogeneous network (HetNet) models were proposed and studied to overcome the effect of VPL, but the issue of power saving has not been studied till now for vehicular HetNet. In this paper, we propose an energy‐efficient algorithm that selects the best number of cooperative MRs among multiple MRs installed on the top of the train for efficient utilization of transmission power in a NOMA‐based wireless network. A half‐duplex amplify‐and‐forward relaying protocol is used for the investigation. We define the optimization problem of energy efficiency for cooperative MR. The problem of energy maximization is stated as an optimization problem, and an energy‐efficient MR selection algorithm is proposed as the solution to the optimization problem. The simulation results show that the proposed algorithm enhances the energy efficiency of vehicular and nonvehicular users and it also decreases the outage probability at fixed spectral efficiency in comparison with the fixed MR‐based selection combining and maximum ratio combining diversity techniques. This result will be beneficial in vehicular communication to achieve maximum energy efficiency. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study energy‐efficient power allocation (PA) to secure the confidential information of massive multiple‐input multiple‐output (MIMO) systems in the presence of an eavesdropper. First a training scheme is presented in the uplink using the MMSE method. Based on the estimated channel state information (CSI), the base station (BS) encrypts the confidential information. Next, the ergodic secret rate is obtained at the downlink. In this paper, in order to maximize the ergodic secrecy rate, the allocation of common power in the uplink and downlink is proposed by considering the constraints such as the transmit power constraint in the uplink, the transmit power constraint in the downlink, and the minimum ergodic secret rate constraint. The objective function in the proposed scenario is a nonconvex problem, which first becomes a convex problem and then, by using the Lagrange dual function method, the problem becomes an unconstraint problem. We also divide the cell into two areas using the cell division method. A practical energy‐efficient PA algorithm is presented which deals with the adverse effect of the eavesdropper. Simulations are carried out to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Summary: The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non‐linearity, multi‐modality, and inseparability. On the other hand, meta‐heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator (Q‐MOSFO‐CA) to solve the constrained UAV path planning issues. The multi‐objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q‐learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

Summary: An unmanned aerial vehicle (UAV)‐assisted communication is a promising technique for enabling ground users in a non‐functional area (NFA) or disaster area to communicate. All the base stations in the disaster area may be partially or fully damaged due to the natural calamity. Device‐to‐device (D2D) communication can be a viable solution for immediate connection between users in an NFA. We propose a UAV‐assisted multi‐hop D2D communication following a hybrid power‐time switching (PTS) in this paper. Moreover, a D2D user of a cluster can communicate with another D2D user in a different cluster through UAVs. However, D2D users can harvest energy from their respective ad hoc energy stations and forward the information to the nearby D2D user following a hybrid PTS‐based strategy. We propose a time frame for the same and show a node‐based energy harvesting strategy. The expressions of outage probability, throughput, end‐to‐end energy consumption, and energy efficiency are developed for the Rician and Nakagami‐m faded channel. The impact of several network parameters such as energy harvesting factor, energy harvesting efficiency, and fading parameter on the network performance is also indicated. [ABSTRACT FROM AUTHOR]

Underwater Acoustic Sensor Networks (UW-ASN) are provisioned with limited bandwidth, long variable propagation delay, limited available energy, highly unreliable acoustic channels, and random node mobility. Consequently, efficient data routing between source destination pair requires UW-ASN to apply a technology different than terrestrial networks. For the past few years researchers have proposed many robust and efficient routing protocols for UW-ASN, thus reviewing the challenges posed by stringent underwater environment. These protocols can be broadly categorized into localization based and localization-free protocols. This paper presents a critical review of beacon-based localization-free routing protocols and suggest possible solution to improve the working of studied beacon based protocols. This work categorizes beacon based protocols into hop-by-hop, end-to-end, single, and multiple parameters based forwarding protocols. This categorization will help to differentiate and identify the requirements for the development of new beacon-based protocols. Finally, this paper presents performance comparison based on simulation results and outlines the research gap and future directions. [ABSTRACT FROM AUTHOR]

Summary: The progressive strides in the exploration of sensing technology and the potential use of electrical devices have rendered a promising technology, termed as Wireless Body Area Networks (WBANs). This technology has rendered competency to reform healthcare and making available ubiquitous health care support to remotely located patients. However, the sensor nodes which are embedded on or underneath the body of the patients to measure multifaceted physiological parameters are suffered from the limited battery constraints. In this paper, to address this issue, we propose Intelligent‐Routing Algorithm for WBANs (I‐RAW) to elongate life period of these sensor nodes. The two sinks are located on the front and back side of the patient's body that collects data from the sensor nodes which form clusters and forward the data through the cluster head (CH). We apply Tunicate Swarm Algorithm (TSA) for selection of CH which considers the essential parameters namely, residual energy, network's average energy, distance of node from the sink, path loss model, and energy consumption rate. The use of two sinks in WBAN mitigate hot‐spot problem in the network by avoiding the multi‐hop communication. The simulation results show that I‐RAW improves stability period and network operational period by 37.7% and 42.7%, respectively, as compared to Dual Sink approach using Clustering in Body area network (DSCB) protocol, respectively. Further, I‐RAW also shows supreme performance for various performance metrics as compared to other state‐of‐the‐art protocols. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are one of the nonnegligible ingredients in any Internet of Things (IoT)‐based system. Fault tolerance in the IoT‐enabled WSNs is an essential part of avoiding harmful actions. Many approaches are reported in the literature on fault detection and fault tolerance for WSNs. However, they either consume additional energy to identify and tolerate failures or use extra hardware and software resources. This paper proposes an intelligent fault tolerance routing scheme for IoT‐enabled WSNs that significantly improves the QoS of the IoT‐enabled WSNs. We propose a novel two‐population z‐test‐based fault detection mechanism to detect faulty nodes in the networks. Furthermore, an intelligent routing scheme is proposed that reuses different partially faulty nodes to tolerate faults within the network. An extensive experiment on the proposed intelligent fault tolerance routing scheme is performed to demonstrate the efficiency of the proposed scheme. Experiment results are compared with the state‐of‐the‐art algorithms to show the effectiveness of the proposed scheme in terms of false‐positive rate, false alarm rate, fault detection accuracy, energy consumption, fault detection accuracy, and network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) as an emerging technology is going to play a critical rule in the physical‐cyber ecosystem. In this environment, sensing and actuating are considered as services for building intelligent applications. From performance point of view, a main question is how to locate these services on devices can realize a remarkable cost‐efficiency and energy saving. In this paper, the goal is to specify which accompanying service with another specific service can be set on a device to optimize the energy consumption, load amount on devices, and the service response time. For this purpose, we formulate this goal as a multiobjective decision‐making problem, and then we solve it by using the PROMETHEE method. In this solution, the utility of each device is determined and estimated based on entropy technique that used for computing weights for different criteria. Numerical simulation results show that the proposed co‐locating algorithm, called p‐co‐locating, outperforms none‐co‐locating approach in terms of energy consumption, load amount, and the service response time. [ABSTRACT FROM AUTHOR]

Summary: Shared transportation is a new way of traveling generated by the rapid development of the "Internet +" and the sharing economy. Due to the limited energy carried by shared e‐bikes, the energy problem becomes the primary obstacle restricting its development. At present, one way to solve the energy problem is to use mobile charging vehicles (MCVs). However, how to schedule MCVs to achieve fast response, reduce energy consumption, and improve charging efficiency is crucial. In this paper, we propose a Charging Scheduling scheme based on Dynamic Characteristic of shared e‐bike in Obstacle Space (CSDC‐OS). Firstly, we design two MCV departure mechanisms to solve the problems of low utilization of MCVs and long waiting time for shared e‐bikes. Then, we design the schedulability conditions based on the A‐star algorithm and the dynamic change of e‐bikes to ensure that MCVs can return to the service station in time. Finally, we propose a charging sequence algorithm based on linear skyline query and a selective insertion algorithm to improve the charging efficiency of MCVs. Simulation results show that the CSDC‐OS can improve the response time, mobile consumption, and charging efficiency by more than 8% on average. [ABSTRACT FROM AUTHOR]

Summary: Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput. [ABSTRACT FROM AUTHOR]

Summary: Massive multiple‐input multiple‐output (MIMO) can considerably enhance the "spectral efficiency and energy efficiency" since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large‐scale radio frequency (RF) chains represent the large‐scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high "spectral efficiency and energy efficiency" owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding "energy efficiency and spectral efficiency." Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize "energy efficiency and spectral efficiency" when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell‐coyote optimization (ASS‐CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS‐CO Algorithm. The proposed ASS‐CO algorithm‐based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS‐WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding "spectral efficiency and energy efficiency" is accomplished over the traditional systems. [ABSTRACT FROM AUTHOR]

Summary: Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with high‐response computing capacities, storage, and high battery power consumption. The data‐intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data‐intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size‐Aware Offloading Model (DSAOM) for data‐intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min‐cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low‐latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data‐intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests. [ABSTRACT FROM AUTHOR]

Summary: Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods. [ABSTRACT FROM AUTHOR]

Summary: There are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple‐input multiple‐output (MIMO) telecommunication systems. Non‐orthogonal multiple access (NOMA) methods and millimeter‐waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm‐Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO‐NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non‐convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes. [ABSTRACT FROM AUTHOR]

Summary: Terrorist attacks have contributed significantly to using wireless technologies to identify concrete destruction survivors. A dynamic ad hoc mobile network (MANET) consists of wireless linked nodes, which route hop‐by‐hop without the support of a fixed infrastructure acquires information from trapped survivors. The energy efficiency that extends the lifespan of the network is an essential prerequisite of MANET. Researchers have suggested many strategies to accomplish this purpose and a cluster of these techniques in MANETs are used to provide an energy‐efficient approach. In this paper we are proposing a red deer multi‐objective constraint applied for an energy efficient QoS routing (RD‐MOCER) algorithm to the number of clusters in an ad hoc network and the energy dispensing in nodes to provide an energy effective solution and to minimise network traffic. Intracluster and intercluster traffic is handled by the cluster heads in the proposed approach. The algorithm suggested takes account of mobile nodes' node degrees, transmitting capacity and battery power usage. This approach gives a variety of options at a time, the key benefit of which is that the ideal Pareto front results in these solutions. We correlate the findings with two other well‐known methods of clustering; MOPSO and MOEAQ‐based clustering with different results. We conduct detailed simulations to demonstrate that the solution proposed is an effective and stronger solution to clustering in ad hoc cell networks than the other two techniques. [ABSTRACT FROM AUTHOR]

Summary: Fog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Due to the energy scarcity that the wireless sensor networks currently face, many energy‐efficient protocols have been developed and proposed in different layers, especially in the data link and network layers, as a large amount of energy consumed by the transceiver, idle‐listening, collision, selecting the correct nodes to act as routers, long‐distance transmission, etc., which are the responsibility of data link and network layers. Since these protocols address the reasons for energy loss in one layer and neglect the treatment of energy loss in other layers, as each layer of the TCP/IP module has its own features and has no control over the other layers. Therefore, there was a need for a cross‐layer design. In this paper, an energy‐efficient cross‐layer between MAC layer and network layer has been proposed using slotted ALOHA based p‐persistent CSMA MAC protocol and Enhance Energy Conservation based on Residual Energy and Distance (EECRED) in order to reduce energy consumption and increase the network lifetime. The performance of the proposed cross‐layer protocol is evaluated by simulating the protocol using the MATLAB simulation. The simulation results are compared with the Fuzzy and Ant Colony Optimization (ACO) based MAC/Routing cross‐layer protocol (FAMACRO), FAMACRO for wireless sensor networks (FAMACROW), fuzzy cross‐layer LEACH, routing‐MAC, and Energy‐efficient Cross‐layer‐sensing Clustering Method (ECCM) protocols to ensure the efficiency of the proposed cross‐layer. The simulation results show that the proposed cross‐layer protocol is very energy efficient, provides stable networks, consumes little energy, and significantly increases the life of the network compared to the single‐layer protocols where the improvement rate was 45%, 53%, 37%, 91%, and 28.72% compared to FAMACRO, FAMAROW, fuzzy cross‐layer LEACH, routing‐MAC, and ECCM protocols respectively in terms of the number of alive nodes or residual energy. [ABSTRACT FROM AUTHOR]

Summary: The combination of non‐orthogonal multiple access in millimeter‐wave communication called as mmWave‐NOMA is capable of supporting more data streams with a larger number of users to cope up with the increasing traffic demand. In this paper, an energy‐efficient scheme for downlink mmWave‐NOMA systems with joint user grouping, scheduling, and optimal power allocation with the objective of energy efficiency maximization is proposed. Enhanced k‐means clustering for user grouping which can handle the possible simultaneous access of network to enhance the sum throughput is proposed. Weighted sum algorithm‐based scheduling where weights are calculated based on the service requirement of the users to maximize the sum rate and a proportional‐based scheme of power allocation modeled as a resource allocation problem considering the beam gain and discrimination weight of each user for energy‐efficient power allocation is discussed. The simulation results show that the proposed scheme is energy efficient than the conventional mmWave orthogonal multiple access systems. [ABSTRACT FROM AUTHOR]

Summary: Many wireless sensor‐based applications use LEACH as a preferred routing protocol owing to its unique features such as optimal sleeping time, minimum packet collisions, dynamic channel selection, and least power consumption. The traditional LEACH protocol wastes the transmission opportunities as it processes data only in an event occurring, leading to wastage of resources. To resolve this issue, this paper proposes a more robust Energy Enhanced Threshold Routing Protocol (ETH‐LEACH) for WSNs. The working of ETH‐LEACH is conceptualized in two parts. In the first part, TDMA is implemented to estimate the opportunistic paths to remove network overhead. Furthermore, in the second part, a threshold value is calculated for choosing the forwarder nodes. The proposed technique minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death. The ETH‐LEACH protocol is contrasted with the different variants of LEACH to verify its effectiveness. The experimental results show that the proposed ETH‐LEACH protocol outperforms the traditional routing protocols. In this paper, the ETH‐LEACH protocol performs nearly 54.6% efficient than LEACH, 47.6% efficient than Q‐LEACH, 41.3% efficient than NR‐LEACH, 33.6% efficient than LEACH‐GA, and 29.7% efficient than LEACH‐POS in reducing the usage of energy in the overall simulation. [ABSTRACT FROM AUTHOR]

Summary: Millimeter wave communication and massive MIMO technology are important ingredients for the future generation communication system. In this context, hybrid precoding (HP) is one of the most promising solutions, and it becomes utterly important in the case of the multi‐user scenario. In this paper, authors have analyzed the performance of the lattice reduction aided fully connected structured precoder for millimeter wave massive MIMO under multi‐user scenario. This paper deals with Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms for the lattice reduction (LR). And the presented results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE)‐based linear precoder with LLL algorithm‐based reduction‐based ZF (LLR‐ZF), LLR‐MMSE, KZ‐ZF, KZ‐MMSE, and Kalman filter‐based precoder. Here, the performance of the precoders has been evaluated based on spectral efficiency, energy efficiency diversity order, and bit error rate. This work conveys some important findings: (i) effectiveness of the lattice reduction aided precoder in multi‐user scenario; (ii) numerical results pointed out that at low SNR condition, KZ‐aided MMSE hybrid precoder provides 4‐dB performance than a Kalman filter‐based precoder; (iii) KZ‐MMSE hybrid precoder outperforms the other precoder. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the problems of relay selection, clustering, and routing to extend the lifetime of wireless body area networks. We first propose an efficient algorithm called "Energy‐aware Relay Selection and Cluster‐based Routing (ERSCR)" to develop a hybrid data aggregation tree in the network. ERSCR has three phases, including the relay selection, clustering and Cluster Head (CH) selection, and data transmission. It divides the biosensors into several clusters and selects an appropriate CH for each cluster. Each biosensor transmits data to its CH or relay node. The aggregated data are then routed to the sink through an energy‐balanced routing tree. The proposed scheme considers both residual energy and distance for routing data of biosensors. It not only reduces the energy consumption in the network but also balances the energy consumed by different biosensors. Subsequently, we improve the ERSCR algorithm and introduce the "Joint Relay Selection, Clustering, and Routing (JRSCR)" algorithm to achieve a better network performance. JRSCR benefits from the advantages of the ERSCR algorithm. Moreover, it reduces the number of transmissions with the direct use of the relay nodes as CH. As another advantage, both ERSCR and JRSCR algorithms are compatible with the natural physical states of the human body. Simulation results demonstrate the efficiency of the proposed algorithms in terms of energy consumption, network lifetime, and maximum hop count. [ABSTRACT FROM AUTHOR]

Using mobile sink with directional antennas (DAs) is one of the major efforts in the literature to improve energy efficiency in wireless sensor networks (WSNs). However, there are three important concerns that have remained unresolved when utilizing this method: namely, (i) packets may be lost because of the continuous motion of the mobile sink; (ii) energy may be wasted when the mobile sink is transiting from one point to another; and (iii) the antennas may not completely scan the entire vicinity of the mobile sink because of continuous directional motion. Our research for this paper targeted these three concerns. For this purpose, a novel semi-Markov decision process-based model is constructed for energy-efficient WSNs with a mobile sink using DAs. Optimal results in terms of the average optimal value of the sum of expected total discounted energy consumption is achieved. Through this optimal analysis, the conclusion is reached that packet loss due to the continuous directional motion of the mobile sink will be eliminated, energy will be preserved, and the mobile sink will completely scan its vicinity using the four DAs. Our results may provide guidelines for using mobile sinks with DAs to further improve energy efficiency in WSNs. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: High congestion, mobility, and energy restrictions are the critical challenges to mobile networks. The conventional routing protocols are unable to ensure effective and reliable communication in such dynamic and unpredictable situations. In this paper, a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector (mZ‐ETAODV) protocol is designed to provide routing solutions in extreme conditions. In this work, the complete network is divided into smaller zones of nxn. The extreme zone position‐based evaluation is conducted to handle the stability and to identify the next intermediate node. The zone‐covered neighbors are further computed under energy and trust measures to identify the most reliable neighbor. The proposed protocol is simulated under high mobility, congestion and man‐in‐middle attack situations. The analytical observations are recorded in terms of Packet Delivery Ratio (PDR) ratio, energy consumption, delay and communication loss parameters. The comparative results are generated against AODV, SAODV, PSAODV, PDS‐AODV, NVS‐AODV, iNVS‐AODV, Tripathy et al., DFMCRP, FBSSR, MBDP‐AODV, AGHA, and Singh et al. protocols. The experimentation is conducted in a highly challenging mobile network with high congestion, high mobility, energy restriction, existence of blackhole, grayhole, DDOS, and mixed attacks. These attacks are performed with 0% to 10% density, and analysis is conducted against PDR ratio, communication delay, route switching, dead node count, remaining energy, and attack detection rate measures. The analysis results identified that the proposed protocol reduced the communication delay and energy consumption and achieved significant gain in attack detection rate, network life and PDR ratio. [ABSTRACT FROM AUTHOR]

Summary: As the state‐of‐the‐art technology in the areas of IoT, battlefield surveillance, medical, military applications, and so on, the Mobile Wireless Sensor Network (MWSN) has attracted the attention of vendors and academics in recent years. In MWSN, energy optimization is the major issue because of its dynamicity property. The energy optimization can be achieved by avoiding node compromise attacks and ensuring secured data transmission in the network. In this paper, an algorithm SDCH‐M (Secured Data for Mobile Clustering Hierarchy) is proposed to ensure secure data delivery in a double cluster head hybrid topology, which optimizes the network energy consumption. Data redundancy is reduced by estimating spatial‐temporal correlation factors, and the variant‐round voting mechanism is used to verify the accuracy of delivered data and to identify compromised nodes through voting by the base station. Results from the simulation show that the proposed SDCH‐M algorithm optimizes energy than M‐LEACH and C‐LEACH algorithm. The average delivery ratio of SDCH‐M (DCH), SDCH‐M (SCH), E2RP, M‐LEACH, and C‐LEACH are 0.9175, 0.9037, 0.828, 0.799, and 0.681, respectively. The average delay of M‐LEACH, C‐LEACH, E2RP, SDCH‐M (DCH), and SDCH‐M (SCH) algorithms is 6.851, 8.655, 6.3, 5.585, and 5.97 s, respectively. [ABSTRACT FROM AUTHOR]

Summary: The continuous growth of wireless connectivity and the emergence of the concept of the Internet of Everything in the future sixth‐generation (6G) network require a new communication paradigm. Different from the prior works that focus on performance optimization for communication networks, in this paper, we attempt to analyze the energy efficiency of unmanned aerial vehicles (UAVs) communication aided by reconfigurable intelligent surfaces (RIS) which is a new disruptive technology, for the reason that UAV has the line‐of‐sight (LoS) link transmission and flexibility and RIS has the low power for improving the communication reliability and network coverage. At firstly, when central limit theorem (CLT) assumption is used and the number of RIS reflecting elements is large, we derive the closed‐form expression of the average signal‐to‐interference‐plus‐noise ratio (SINR) of each user based on the new BS‐RIS‐user communication link and RIS‐user/BS association. Following this, we further gain the energy efficiency (EE) in closed‐form based on random access between BS and user. Finally, we further show the performance of EE under certain assumptions. In particular, the offered results demonstrate that the use of the RIS can significantly improve EE due to RIS assist in improving the SINR and has very low transmission power; meanwhile, the UAV equipped with RIS can also improve EE for the reason that RIS can be used as a mobile relay to take advantage of the LoS link transmission. [ABSTRACT FROM AUTHOR]

Summary: Wireless visual sensor networks (WVSN) have vital roles in surveillance applications. In these networks, wireless visual sensors include camera and transceiver module and collect visual information. However, energy consumption and coverage of the tracked targets are important challenges in WVSNs since increasing the coverage leads to increasing energy consumption. Therefore, energy optimization and satisfying the quality of experience (QoE) of the tracked targets are essential issues in these networks. In fact, the appropriate focal length setting leads to increasing the target coverage and quality of the captured targets' images and energy consumption. Therefore, selection of the suitable visual sensors and setting their focal length can overcome the energy consumption challenges and improve QoE of the tracked targets. In this case, compressive sensing is also expected to overcome the battery constraints of the WVSN resources. In this paper, the problem is to minimize the energy consumption of the multi‐target tracking with high reliability, while the coverage and also the quality of the received image of the targets are satisfied by selection of the proper visual sensors and the focal length adjustment. The convex optimization method is used to solve the problem. Also, based on the Karush‐Kuhn‐Tucker conditions, the optimal solution for the problem is obtained. Simulation results validate the efficiency of the proposed method in comparison with the other bench mark algorithms. [ABSTRACT FROM AUTHOR]

Summary: Providing the appropriate coverage is quite essential for the effective functioning of many applications in Wireless Sensor Networks. Therefore, the efficiency of node deployment algorithms to supply the requested coverage is of high significance. In this paper, inspired by the equilibrium of molecules, a novel node deployment algorithm, called Smart Self‐organising Node Deployment (SSND), is proposed to provide maximum coverage. Despite other proposed algorithms, which provide coverage based on the collective movement of nodes with massive energy consumption, SSND moves one sensor in every neighbourhood at each step to reduce the sensor nodes' movement and hence the energy consumption. The chosen sensor nodes at each time step are distributedly determined by an eligibility function to reduce the non‐essential movements while improving the accuracy of the reported locations of neighbours. Our extensive simulation study shows that SSND can achieve up to 30% coverage improvement compared to those of other algorithms in most scenarios and provides an adequate trade‐off between coverage and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: The energy efficiency sustenance in wireless sensor networks completely depends on the process of clustering to prolong network lifetime. The success of this clustering process completely depends on the cluster head (CH) selection process as potential CH selection prevents frequent clustering and aids in maintaining maximized energy in the network. In this paper, an improved Game Theory‐Gray Wolf Optimization algorithm (GAGWO)‐based efficient CH selection strategy is improved for maintaining the energy in the network through the construction of equal size clusters. This GWOA mimicked the hunting and attacking characteristics of gray wolves and facilitated better CH selection, which completely prevented worst sensor nodes from being selected as CH. It is proposed for establishing a better trade‐off between the local and global search for the objective of preventing re‐clustering that unnecessarily drains network energy. The simulation results of the proposed GAGWO confirmed better performance in terms of network lifetime, energy stability, mean throughput, and packet delivery ratio, on an average by 23.18%, 21.86%, 19.84%, and 20.98%, compared to the baseline CH approaches used for investigation. [ABSTRACT FROM AUTHOR]

Summary: Heterogeneous wireless sensor networks (HWSNs) have a wide range of application in the contemporary world particularly in sensing and hunting of events without human interference. Due to this limitation of network resources and energy of sensor nodes, the contention of available resources for transmission will increase the energy consumption in the network and degrade the performance of Quality of Service. During dense traffic conditions, sensor nodes in close proximity to base station (BS) faces severe bottleneck resulting in congestion and energy hole problem. The efficient utilization of energy and packet loss is due to link break and by traffic analysis attack is a primary threat in HWSN applications. To sustain maximum energy in the network, two important aspects are considered, namely, flawless selection of path and concise clustering. This paper describes about a multi‐objective‐based Hybrid Diffusion Clustering Scheme (HDCS) through Jellyfish routing protocol with Time Division Multiple Access (JRP‐TDMA)‐based routing method in HWSN. Cluster head is elected based on multiple objectives such as residual energy, cost, and distance. Once CH selection is done, JRP‐TDMA based on available timeslot handpicks the best path for transmitting to BS. The proposed protocol is analyzed, simulated, and performance results are correlated with existing protocols namely, diffusion clustered routing protocol (DCRP), efficient unequal clustering (EEUC), Markov chain model‐based optimal cluster head (MOCH), and genetic algorithm (GA). The number of alive sensor nodes of proposed method has increased by 24.4% when compared with other approaches. [ABSTRACT FROM AUTHOR]

Summary: Efficient geographic routing techniques in multi‐hop wireless networks are popular and attractive for their effective routing mechanism; here, for routing, the location information is used instead of topology information. In recent years, most of the proposed position‐based routing protocols have been designed for 2D space, but in real‐life scenarios, the nodes are usually deployed in 3D space. Working with 3D space is more complicated than with 2D space. Enhancement of the end‐to‐end throughput while reducing the energy consumption and the end‐to‐end delay is the primary consideration for designing routing protocols in a 3D geographical multi‐hop wireless network. This paper proposes a projection‐based routing technique, where routes obtained in 3D multi‐hop wireless networks are projected on 2D planes and on the reference line (RL). The main goal of this study is to simplify the operational difficulties in 3D space and performance enhancement of the network in terms of throughput, energy consumption and delay. The proposed technique presents the various projection methods for the routes obtained in 3D space. The projections are made on (i) the RL joining source to destination; (ii) the planes XY, YZ and ZX; and (iii) a particular 3D plane. The effectiveness of the proposed technique is demonstrated through simulation, considering the network parameters, end‐to‐end delay, throughput and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: The problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J‐RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT‐based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k‐covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi‐objective seagull optimization algorithm (MO‐SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end‐to‐end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW‐AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay. [ABSTRACT FROM AUTHOR]