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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: In this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS. [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: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [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]

ABSTRACT The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS). [ABSTRACT FROM AUTHOR]

ABSTRACT A cooperative device‐to‐device (D2D) communication system with cognitive radio (CR) enabled that integrates ambient backscattering (AB) and energy harvesting is studied in this paper. Cellular network (CN) resources are exploited by D2D users. The transmission between source and destinations is hybrid (overlay or underlay) based on sensing information. In overlay, cellular users (CUs) are absent in the given channel; thus, relay transmits with high power based on available harvested energy. On the other hand, in underlay CUs are present, and they communicate with each other via base station (BS); thus, in D2D network, relay transmits with controlled power using nonorthogonal multiple‐access (NOMA) technique. In contrast to the conventional hybrid transmission, in the proposed work, relay adopts the ambient backscatter communications (ABCs) in underlay because RF signal of BS is available. The relay first backscatters the source information using available RF signal of BS; thereafter, it uses its own battery for controlled transmission and keeps the interference to the CU receiver within a tolerable limit. Spectrum sensing (SS) and a transmit power limitation are taken into consideration while developing analytical models for throughput, outage, and energy efficiency (EE). An algorithm is proposed to optimize the relay transmit power in the underlay scenario. Through meticulous simulation, the network performance in terms of throughput, outage, and EE is examined for hybrid transmission with ABCs. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates the green design of rate‐splitting multiple access (RSMA) for multicell, multiantenna downlink communication systems to maximize rate fairness under quality of service (QoS) constraints. An optimization framework is developed to minimize the weighted sum power consumption by jointly optimizing transmit beamformers, power allocation, and common/private rate split while ensuring per‐user minimum rate requirements. The algorithm design explicitly considers the rate fairness among users via a newly proposed green fairness index. The nonconvex transceiver design problems are transformed into tractable forms and efficiently solved by applying tools from alternating optimization and successive convex approximation. Extensive simulations demonstrate that the proposed green RSMA optimization framework can effectively balance the throughput among cell‐edge and cell‐center users, hence combating the near‐far effect. Simulation results show that at 20‐dB SNR, green RSMA achieves over 20% and 50% sum rate improvement compared with NOMA and OMA, respectively. Moreover, green RSMA maintains high fairness among users, even when the channel conditions are disparate. When the weakest user is located 120 m from the base station, green RSMA exhibits a 21.2% higher fairness index than NOMA, showcasing its ability to ensure equitable service quality across users. The framework also achieves substantial energy efficiency gains, consuming less power while providing higher data rates than baseline schemes. These results demonstrate the effectiveness of green RSMA in enhancing spectral efficiency, user fairness, and energy efficiency in multicell, multiantenna communication systems, making it a promising solution for sustainable and high‐performance wireless networks. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) continues to find novel applications across various domains. These applications require the support of efficient communication protocols in the Low Power and Lossy Networks (LLNs). The development of efficient multicast protocols is one such area that requires critical research attention. Efficient multicast protocols are especially important in the non‐storing mode of IPv6 Routing Protocol for LLNs (RPL), considering its applicability in real‐world IoT applications. However, the development of non‐storing RPL multicast protocols needs to handle the intricacies associated with source routing. Addressing these issues, this paper presents a novel multicast protocol named Multicast Forwarding in LLNs under Non‐storing mode (MFLN). MFLN uses Bloom Filters to minimize the overhead that comes with source routing. While retaining the best features of multicast protocols operating in the storing mode of RPL such as bidirectional traffic and cross‐layer optimization with ContikiMAC, MFLN also includes a novel metric named Mode Selection Metric (MSM) to decide the suitable link‐layer forwarding method. MSM makes this decision based on the ability of a node, in terms of its residual energy, to perform multiple link‐layer unicasts. Thus, MFLN saves the node battery from running out and at the same time reduces the duplicate packets created due to link‐layer broadcasts. The simulations conducted using Contiki‐COOJA indicate considerable improvements in terms of packet delivery ratio, energy consumption per delivered packet, packet transmissions, end‐to‐end delay, and normalized routing overhead for the proposed protocol. [ABSTRACT FROM AUTHOR]

Summary: Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

Summary: The handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN. [ABSTRACT FROM AUTHOR]

Summary Even with existing methodologies, dynamic resource allocation, service inconsistency, operational costs, and excess energy/power consumption in optical access networks remain the key challenges for the next‐generation passive optical network (NG‐PON) design and development. This paper introduces a novel method for analyzing energy/power consumption in time and wavelength division multiplexed (TWDM) passive optical networks (PONs) with a ring‐based topology. We propose subscriber‐based channel allocation models (CAMs) for on‐demand services, allocate bandwidth dynamically, and evaluate their downstream power consumption. To determine the necessary optical devices for the various CAMs, we investigate subscriber take‐up rates by optimizing the utilization of optical components based on active subscribers. Implementing a novel Poisson distribution‐based network traffic model addresses energy‐related concerns and explores optimization techniques to minimize power and improve energy efficiency. With a take‐up rate of 64 active subscribers, simulation results show significant power consumption reductions ranging from 62.2 to 67.7%, achieved by the proposed subscriber‐based architecture models with varying subscriber counts, when compared with the conventional model. The model with the most subscribers (2048 subscribers) saves the most energy by reducing power consumption to 292 W from the conventional model's (2048 subscribers) 440 W. Furthermore, the model with the most subscribers saves 33.4% of energy, outperforming other subscriber‐based architecture models without affecting QoS even during peak hours of the day. These numerical results show that TWDM‐PONs with the most subscribers exhibit improved energy efficiency than the conventional and other proposed subscriber‐based architecture models. [ABSTRACT FROM AUTHOR]

Summary By transmitting concurrently signals of different users, non‐orthogonal multiple access (NOMA) meliorates spectral efficiency. Moreover, intelligent reflective surface (IRS) is introduced as a bridge between a NOMA transmitter and a distant NOMA receiver. This role enhances spectral efficiency and communication reliability. Furthermore, energy scavenging underlay networks (ESUNs) enable unlicensed transmitters to operate using scavenged energy on licensed spectrum allotted to licensed transmitters, enhancing both spectral and energy efficiencies. Although IRS‐assisted NOMA further meliorates spectral efficiency and communication reliability for traditional ESUNs, it faces challenges like fading severity and nonlinear energy scavenging (nlES), which directly impact scavenged energy and communication reliability. Thence, the paper assesses three pivotal performance metrics—energy efficiency, outage probability, and throughput—for IRS‐assisted NOMA in ESUNs, shortly RaNOehU, considering realistic factors such as relatively‐general Nakagami‐m fading and nlES. Demonstrative results reveal that the quantity of reflective elements, fading severity, and nlES dramatically influence the performance of RaNOehU. Notably, appropriate selection of power/time division parameters and desired data rate can hinder complete outage and attain optimum performance. In addition, the proposed IRS‐assisted NOMA is shown to outperform its reference, which is IRS‐assisted orthogonal multiple access, emphasizing the advantages of NOMA. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [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 Internet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering. [ABSTRACT FROM AUTHOR]

Summary The performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems. [ABSTRACT FROM AUTHOR]

Summary In wireless sensor networks (WSNs), trusted routing path needs to be determined for guaranteeing reliable data dissemination with maximized Quality of Service (QoS). But the sensor nodes may not exhibit a cooperative behavior for the objective of conserving energy and remaining active in the network. Trust management techniques are essential for alleviating the problem of packet dropping attacks of sensor nodes that intentionally deteriorates the performance of the network. In this paper, Hybrid ELECTRE and bipolar fuzzy PROMOTHEE‐based trust management (HEBFPTM) scheme is proposed for addressing the impact of packet dropping attacks that targets on improving QoS in WSNs. This is proposed as a multi‐criteria decision analysis solution for obtaining feasible number of parameters that could be derived from the sensor nodes of the network to determine its cooperation degree in the network. This HEBFPTM is proposed with the objective of integrating the ordinal evaluation of mobile nodes into a cardinal procedure using the method of PROMETHEE to attain quantitative and qualitative analysis that aides in identifying the weights of each criterion considered for cooperation determination using pairwise comparison. It adopted three preference models using partial, complete, and outranking through intervals. It handled the problem of uncertainty using the merits of bipolar fuzzy that helped in attaining the weight of the criteria and preference functions used for ranking the sensor nodes in the routing path. The experiments of the proposed HEBFPTM achieved using ns‐2 simulator confirmed its efficacy in improving the attack detection rate by 21.38%, reduced false positive rate by 15.42%, maximized packet delivery rate of 18.94%, and reduced energy utilization of 19.84% better than the benchmarked approaches used for investigation. [ABSTRACT FROM AUTHOR]

Summary The long‐range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy‐intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy‐efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter‐based ADR (KF‐ADR) and moving median‐based ADR (Median‐ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. [ABSTRACT FROM AUTHOR]

Summary The cluster networking protocols are the roots that embed intelligent decision‐making and enhance the lifespan of wireless sensor networks (WSNs). Wireless sensors with limited capabilities face several challenges due to the heterogeneous application environments. Especially, the mobility‐incorporated sensors in most situations trouble the cluster network's robustness. Many cluster networking protocols have been presented in the past to enhance the network lifespan and data delivery ratio. However, they lack a dedicated and efficient mechanism for mobility assistance, an adequate cluster management process and cluster head selection criteria. To overcome these issues and for the uniform energy load distribution, we propose a mobility‐compatible cache controlled cluster networking protocol (MC‐CCCNP) in this paper. It is an energy‐efficient cluster networking protocol that supports sensor movement. Network resource management and routing are controlled distributively by an optimal number of cache nodes. It defines a new strategy for cache node deployment based on neighbour density as well as a weight formula for cluster head selection and cluster formation based on the residual energy, the distance to the base station and the node velocity. It also includes techniques for detaching and reconnecting a mobile node to an appropriate cluster cache if it crosses the cluster boundary. We simulate and compare the performance of our protocol with the centralised energy‐efficient clustering routing, energy‐efficient mobility‐based cluster head selection protocol and dual tier cluster‐based routing protocols over different network configurations with varying mobility, scalability and heterogeneity. The MC‐CCCNP showed remarkable improvements in energy utilisation uniformity and energy consumption. With the improved network lifespan, it also maintains a higher data throughput rate of 95% or more in almost all network configurations. [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: The heterogeneous ultra‐dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy‐efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four‐tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF‐MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy‐efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930. [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: 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]

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: 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: 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: 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: 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 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: 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: Information transmission is extremely challenging in underwater acoustic sensor networks (UASNs) because, in the acoustic channel, the packet loss is high compared to other channels due to low bandwidth and a long propagation delay. So, designing an efficient energy algorithm is a big challenge in the acoustic channel. In this paper, first, a cluster head (CH) election fitness function based on a hybrid particle swarm optimization and gravitational search algorithm (PSO‐GSA) is proposed for a three‐dimensional (3D) underwater sensor network (UASN). The proposed algorithm includes CH election, CH load, and neighbor CH distance for multi‐hop transmission (MH‐PSOGSA). Then, based on the proposed algorithm, a relay‐based traffic‐aware energy‐efficient routing protocol (R‐TAEERP) with a weight function is proposed to offload the heavy consumption of the CHs. MATLAB simulations were accomplished to compare the performance of MH‐PSOGSA and R‐TAEERP with the existing representative protocols. The heterogeneity in energy and traffic has been considered. The simulation results show better performance of the proposed algorithm than other existing representative protocols in terms of energy consumption, the performance of the stable region, and the lifetime of the network. [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]

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: 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: 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: 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: 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: 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: 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]