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Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Summary: Internet of Mobile Things (IoMT) have become very popular recently. The routing protocol for low power and lossy networks (RPL) is standardized for static topologies. However, mobility is the nature of IoT. Mobility serves as a promising candidate to harness hand‐off time issues, delay in data transmission, overhead, and low packet delivery rate (PDR) effectively. This study presents a comprehensive account of the mobility‐aware RPL‐based routing protocols to validate and compare the experimental results. Remarkably, classification methods are used in many articles. The aim is to introduce significant research efforts to improve RPL objective functions (OF) performance in hand‐off time, PDR, delay, overhead, and so forth. In this regard, a complete analysis of the existing routing protocols in IoMT has been presented to compare the results. The main focus of this study is on approaches that proposed new OFs for supporting mobility in RPL. Two main categories are considered to study RPL‐based routing protocol mechanisms: The mobile and static sink. The related studies on the mobile sink are divided into three groups: Single metric‐based OF, composite metric OF, and hybrid routing protocols. Also, the related works based on the static sink are categorized into four groups: Fuzzy logic‐based OF, trickle timer‐based OF, composite metrics‐based OF, and modification control messages‐based OF approach. This paper presents a detailed comparison of mechanisms in each category. It also highlights the pros, cons, open issues, and evaluated metrics of each paper. Besides, challenges of mobility in the RPL‐based routing protocol mechanism in IoMT for future studies. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network. [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: In the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance. [ABSTRACT FROM AUTHOR]

Summary: The main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively. [ABSTRACT FROM AUTHOR]

Summary Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as “(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase.” In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods. [ABSTRACT FROM AUTHOR]

Summary: The fifth generation of mobile technology is referred to as "5G." 5G refers to the next significant phase of mobile communications standards after the upcoming 4G standards. With 5G technology, the bulk of high‐bandwidth consumers would be able to use their phones in innovative ways. When 5G is pushed over a VOIP‐enabled device, individuals encounter record levels of call volume and data transmission. In this paper, we reviewed smart antenna 5G for Internet of Things (IoT) application. Beamforming is a 5G active antenna technique that uses directional radio links to concurrently and selectively supply high bandwidth to certain mobile devices. Multi‐antenna systems are required when using larger frequency ranges. The better the propagation conditions for electromagnetic waves, the higher the frequency. To some extent, multi‐antenna arrays and beamforming can assist mitigate this. Radio signals can be transmitted and received in a spatially targeted manner due to beamforming. The better the beamforming works, the more dipoles (antenna elements) are available. In contrast to earlier eras on wireless networking, such as Global System For Mobile Communication (GSM), Universal Mobile Telecommunication System (UMTS), also 4G/LTE, 5G would not require major technological advances. Additional systems and equipment are added to the existing LTE technology to increase data capacity and reduce latency. The 5G NR infrastructure depends heavily on active antenna arrays, which enable multi‐user multiple‐in multiple‐out (MIMO) technologies. For targeted radio contact with the receiver, these antenna modules use beamforming. [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 rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource‐constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state‐of‐the‐art routing protocols. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN‐assisted IoT applications, even though better data communication is achieved through cross‐layer models. This paper proposes a new cross‐layer‐based clustering and routing model to provide a scalable and energy‐efficient long data communication in WSN‐assisted IoT systems for smart agriculture. Initially, the fuzzy k‐medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy‐hole problems in WSN. A cross‐layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio‐inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end‐to‐end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime. [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: 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: With the advent of the era of Big Data, users are increasingly demanding network bandwidth and data storage in the Internet of Things (IoT) system. While current research on the opportunistic social network does not take the redundant influence of high‐speed data transmission into account, which leads to problems such as lower delivery ratio, we present an effective data transmission scheme based on IoT system (EDTS). To divide the community, which consists of devices with relatively close connections, we measure the social relationship of nodes by quantifying the remaining cache, social relation, and interest similarity in opportunistic social networks. An efficient strategy is designed in this paper to update the community by analyzing the change of edge weights between two nodes in the community, deciding whether the community should be further divided, and whether the nodes should join the larger community. The proposed scheme also involves data transmission methods based on community, which involve inter‐community forwarding and intra‐community forwarding. Simulation results show that EDTS outperforms other comparative algorithms with regard to average delivery ratio, end‐to‐end delay, and routing overhead. [ABSTRACT FROM AUTHOR]

Summary: Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies. [ABSTRACT FROM AUTHOR]

Summary: The ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s. Key findings: The AEMDPG‐ARO approach is proposed to solve user association problems in UDN.Data sample for this study is obtained through the WINNER channel model.AEMDPG‐ARO approach achieves high data transmission and less latency rate.It offers better performance with reduced time complexity and better metrics rate. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) plays an indispensable role in the human life by supporting a diversified number of applications that includes military, environment monitoring, manufacturing, education, agriculture, etc. However, the sensor node batteries cannot be replaced under its deployment in an unattended or remote area due to their wireless existence. Cluster‐based routing is significant in handling the issue of energy stability and network lifetime. The meta‐heuristic algorithms‐based cluster head (CH) selection is determined to be highly promising for attaining the objective of CH selection that results in acquiring an optimal network performance. In this paper, a Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm (HGICSOA)‐based clustering scheme is proposed for attaining potential CH selection and guarantee significant sink mobility‐based data transmission. The capability of GHOA that controls the rate of exploitation and exploration degree is utilized for CH selection. It specifically adopted OBL‐based GHOA for optimal CH selection based on the objective function, which is formulated using node density, residual energy, and distance between sensor node and sink. It incorporated new CSOA for mobility‐based data transmission for increasing population diversity. It also utilized the benefits of ICSOA with a predominant local search strategy for achieving better sink mobility‐based data transmission. Simulation and statistical results confirmed that the proposed HGICSOA is better in attaining maximum energy stability by 17.21% and improved network lifetime by 23.82%, compared to the benchmarked schemes used for investigation. Moreover, the prevention rate of worst sensor nodes selected as CH is improved by 21.38%, better than baseline approaches. [ABSTRACT FROM AUTHOR]

Summary: One of the infrastructure‐free networks is mobile ad hoc networks (MANETs) that are built with limited battery life using wireless mobile devices. This restricted battery capability in MANETs creates the necessity of considering the energy‐awareness constraint in designing them. As routing protocols, the major aim of MANETs is to create the energy awareness in the network; it improves the network's lifetime through effectively utilizing the available restricted energy. Moreover, it creates some limitations like the mobility constraint, wireless link's sensitivity to environmental impacts, and restricted transmission range and residual energy of nodes that causes rapid modifications in the network topology and frequent link failure. By taking those problems, this paper plans to develop a new multipath routing protocol, where the hybrid optimization algorithm with the integration of cuckoo search optimization (CSO) and butterfly optimization algorithm (BOA) is proposed and named sensory modality‐based cuckoo search butterfly optimization (SM‐CSBO) for determining the optimal path between the source and destination. The main goal is to select the path with better link quality and more stable links to guarantee reliable data transmission. The multi‐objective function is considered with the factors regarding distance, normalized energy, packet delivery ratio, and control overhead to develop an effective routing protocol in MANET. The proposed model of SM‐CSBO algorithm has superior than 5.8%, 30.4%, 36.7%, and 39.3%, correspondingly maximized than PSO, SFO, CSO, and SFO algorithms while considering the number of nodes as 150. The simulation outcomes proved that it enhances network performance when compared with the other traditional protocols. [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: 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: In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we have devised a method for spectrum‐and energy‐efficient wireless communication systems for the Internet of Things (IoT) devices that require continuous uninterrupted power for their operation. However, the potential application of the proposed scheme is vast and is relevant to any wireless communication systems deployed for the next generation connected systems that face challenges in spectrum availability and battery charging of the devices. To fulfill the demand of wireless devices, such as sensor nodes deployed in the IoT, we have proposed a spectral‐and energy‐efficient‐medium access control (SEE‐MAC) protocol with a radio‐frequency energy harvesting (EH) facility. The proposed protocol has integrated wireless power harvesting with cognitive sensor nodes to provide both the energy‐ and spectrum‐efficient wireless communication system. In the proposed SEE‐MAC, the cognitive sensor nodes take the advantage of both the idle and active licensed channels of the primary network for data transmission and EH. The proposed system identifies the active licensed channel of the network to be allotted to each cognitive sensor node for EH and idle channel recognition for data transmission. The mathematical analysis has also been performed representing allocation of primary user channels for the EH and data transmission. The closed‐form expressions obtained along with simulation results have illustrated the amount of harvested energy, throughput, and energy efficiency achieved by the cognitive sensor nodes of the network. [ABSTRACT FROM AUTHOR]

Summary: The indoor terahertz sources cause power fluctuation at various locations due to obstacles, which can be avoided by deploying multiple sources, but will lead to high energy consumption. In this paper, the reverse ray‐tracing method is used to establish a terahertz indoor channel. The free path loss, atmospheric absorption, and reflection loss are considered. Adaptive algorithms are adopted to solve the large energy consumption of deploying multiple sources. A weight vector coefficient is used based on the location and received user signal to adjust the transmission power of each terahertz source dynamically. The results show that the adaptive power control of multiple terahertz sources substantially reduces the energy consumption and the signal power fluctuations. For static users, the energy consumption can be reduced by 5 and 9 dB, and the maximum data transmission rate can be increased by one to two times using four and nine sources, respectively. Moreover, energy consumption can be reduced by more than 6 dB for several indoor users. For dynamic users with a changing channel environment, the adaptive algorithm can adjust the source power in real time and improve signal efficiency. However, the efficiency improvement of the algorithm is limited by the distribution of the multiple sources. The energy consumption of four sources with the adaptive algorithm operating in the center of the room is greater than that of nine sources without the adaptive algorithm. Nevertheless, the efficiency of the nine sources with the adaptive algorithm is much higher than that of the four sources. [ABSTRACT FROM AUTHOR]

Summary: The main aim of this paper is to address the problem of redundant broadcast packets using the extension of rough set concept. In general, a rough set is a mathematical tool for dealing with issues of uncertainty. In MANET, mobile nodes are moving from one place to another place in a simulation environment; hence, it creates uncertainty issues. Due to the fast mobility of mobile nodes, network metrics such as bandwidth and hop count vary. The findings were useful in identifying and improving the network's performance. The proposed NRS‐based route selection approach has been compared with conventional techniques such as rough set, covering rough set (CRS). The reduced control packets are obtained using pruning concept based on selection of one‐hop and two‐hop nodes. Eventually, the proposed approach proved that it attains a higher throughput (85%), reducing the route request packets (3406) and delay of data transmission (0.02274 s). Also, two well‐known ad hoc routing protocols were used such as Dynamic Source Routing (DSR) and Ad Hoc On‐Demand Distance Vector (AODV) protocols. [ABSTRACT FROM AUTHOR]

Summary: Deepwater utilization and underwater sound communication with inadequate bandwidth, elongated propagation interruption, signal failure issues, and sensor node malfunction due to ecological circumstances are the limitations of the underwater wireless sensor network. Further, the challenges in underground wireless sensor network (WSN) include superior bandwidth, high power expenses, information handling, and cross‐layer design. On the other hand, cooperative communications facilitate competent employment of communication resources, by permitting nodes in a network to work together in information communication. As wireless sensor networks adapt to multi‐hop transmission in high data traffic, there is a high demand for achieving energy efficiency. Moreover, in the course of proximity nodes to the sink, an energy hole is started for the extended network lifetime. This paper proposes the design of multi‐hoped cooperative communication‐based wireless underground sensor networks that guarantees energy efficiency in a hostile environment. In addition, network coverage and connectivity improvement are also improved by this proposed design. [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: The performance of backhaul networks is the major concern for internet service providers, cloud service providers, and data centers for redundancy and reliability. With the integration of new data communication technologies, the system should be more smart and intelligent to handle data traffic coming from new networks like 5G. This new 5G standard provides connectivity among devices where all the traffic is handled by backbone and backhaul networks. The devices and nodes need multiple interfaces, but the TCP/IP standard uses a single path per connection for data communication. This limitation leads to the degradation of network performance and communication failures. There is a need to enhance data throughput and performance by using available paths and interfaces. Multipath Transport Control Protocol (MTCP) is one of the advanced extension of the TCP/IP standard for using available interfaces. This paper proposed an MTCP based infrastructure for 5G networks to handle all the coming data from these networks and provide fast reliable data handling. The proposed infrastructure is evaluated in simulation to check its performance with existing standards. The results indicate that the proposed infrastructure is minimizing the convergence time in case of any failure in the main path. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio (CR) was developed to solve the issue of spectrum scarcity because CR users are unlicensed and can use the underutilized licensed spectrum without interfering with licensed users. During handoff, however, this CR creates a target channel sequence (TCS) to transfer the channel to the multiusers. It must also deal with issues such as equally assigning TCS to different secondary users (SUs), channel access collisions, and channel obsolesces during TCS development. TCS is issued to SUs in the same and different primary user (PU) areas. In this paper, the hybrid multilayer perceptron (MLP)–convolutional neural network (CNN) (MLP‐CNN) technique is specifically proposed to provide services to the SU even under proactive TCS constraints, as well as to address the aforementioned challenges. The MLP can increase cell quality by resolving the transmission time gap problem that occurs during the proactive stage. The MLP determines the characteristics of each SU in a specific area with high reliability for channel allocation, while the CNN allocates the channel based on the characteristics of the entire SU present in a specific area. This hybrid MLP‐CNN approach, on the other hand, is unable to achieve the optimal degree of optimized TCS generation accuracy due to the need for parameter fine‐tuning, which is accomplished using the golden eagle optimization technique. Different parameters, such as single‐user scenario, multiuser scenario, multichannel scenario, and network access overhead, are used to measure the performance of the proposed model. The handoff delay, average interval per target channel, and the overhead that occurs during the transmission of data by our proposed method are 0.56%, 0.17%–0.03%, and 50%–10% respectively. Thus, our proposed method outperformance all the existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Wireless Sensor Network (WSNs) is a collection of sensor nodes with a base station for data transmission. Mostly the network has various paths with the unbalanced nodes; therefore, energy consumption is always high. This causes the letdown of quality of service (QoS) routing metrics such as delay, inefficient throughput in WSNs. This paper concentrates on this problem and proposes an energy‐efficient multipath routing (EEMR) protocol for WSNs. EEMR protocol computes the optimal path using the multi‐objective lion optimization algorithm with less energy consumption. The main aim of EEMR protocol is to achieve an efficient path among multiple paths to satisfy quality requirements such as delay, throughput, energy consumption, delivery ratio, loss ratio, and latency. Thus, the proposed EEMR protocol is designed with a multi‐objective function using an optimization algorithm and implemented in MATLAB. The simulation results show that the performance of the proposed protocol is very efficient in comparison to state of the art. [ABSTRACT FROM AUTHOR]

Summary: With the tremendous increase in the research and development of Internet of Things and Internet of Vehicles, the research scope of Mobile Ad Hoc networks has also increased, rapidly. A number of routing protocols have been proposed to transfer data packets from source to destination nodes. Among these routing protocols, some of them have focused upon multiple paths for data transfer so that route re‐discovery time could be minimized. Thus, the end‐to‐end delay has reduced. The main purpose of these multi‐path routing protocols is to balance the load among all or some of the available routes. However, the selection of proper parameters is very crucial and important for load balancing. In this paper, we develop a load‐balanced multi‐path routing protocol with energy constraints (EE‐LB‐AOMDV). It first categorized the multiple paths based upon three parameters: hop‐count, round‐trip time, and residual energy. Then only, it starts data transmission based upon the quality of the path. The simulation results show that the performance of the proposed routing protocol has been improved significantly in terms of packet delivery ratio (PDR), average end‐to‐end delay (delay), routing overhead (overhead), energy exhausted nodes, and route rediscovery frequencies. [ABSTRACT FROM AUTHOR]

Summary: The digitization of objects has given birth to the concept of the Internet of Things (IoT) which is revolutionizing traditional objects by replacing them with intelligent objects such as smart vehicles, smartphones, smart home...etc. In this context, and with the emergence of vehicle networks, is born the need to increase the vehicular resources in order to benefit from several applications facilitating driving and ensuring the drivers safety, even going to think of the automated driving. The use of cloud computing has become the key solution to the lack of resources required to run compute‐intensive applications and the lack of storage space to back up all data related to roads and applications. In addition, we are witnessing the birth of the fog computing paradigm, which brings the functionalities of cloud computing at the edge of the network, thereby solving the latency problem for some time‐sensitive applications and also saving the bandwidth of the network because vehicle requests will not need to cross the entire network to be processed at the cloud level. In this paper, we discuss the different principles of cloud/fog computing and compare the two paradigms. We present a classification of data dissemination schemes in vehicular ad hoc networks (VANETs), vehicular cloud, and vehicular fog computing with their different architectures proposed. We finally present several cloud/fog computing applications. [ABSTRACT FROM AUTHOR]

Summary: Energy‐efficient clustering and routing are two well‐known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP‐hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi‐objective particle swarm optimization with Levy distribution (MOPSO‐L) algorithm. Since the parameters in WSN are related to one another, multi‐objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO‐L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO‐L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives. [ABSTRACT FROM AUTHOR]

Summary: Energy is the cornerstone of the existence and development of modern society. The energy consumption continues to increase, and the ensuing environmental pollution and increasing lack of traditional energy sources have also become the world's most concerned issues. The emergence of new energy has eased the current unfavorable situation. Among them, distributed photovoltaic (PV) power generation has been favored because of its abundant resources and no geographical restrictions. However, due to the scattered construction of solar PV power stations, it is not easy to manage. Therefore, research and development of PV remote monitoring systems for unified monitoring and management of PV power stations. In addition, the problem of solar cell output efficiency is also an important reason that restricts the utilization of solar energy. Improving the output efficiency of the battery based on the existing solar cell conversion efficiency is also a focus of current research. Based on the above background, the research content of this article is the network communication monitoring system for distributed PV power generation systems. This article first conducts the overall design of the PV remote monitoring system, constructs the system's distributed design structure, and divides the underlying hardware part of the system into control modules and transmission. According to the application requirements, the STM32 series single‐chip microcomputer is selected as the controller of the system control module and the transmission module. The hardware circuit design and software process implementation of the system are studied to realize the data collection, MPPT control, and remote data transmission of the photovoltaic system. Features: Finally, through experimental simulation, it was verified that the light intensity increased from 600 to 800 W/m2, and the output current of the inverter increased accordingly. The output current of the distributed PV power generation system was directly proportional to the change of the light intensity. The designed system is feasible. [ABSTRACT FROM AUTHOR]

Summary: Over the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT‐based networks due to increased communication power consumption. It has become crucial to propose solutions for network‐based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP‐hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies. [ABSTRACT FROM AUTHOR]

Summary: For the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time‐series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR‐NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)‐based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR‐NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA‐based clustered routing and clustering scheme using Canada Geese Migration Principle (CS‐CGMP) for routing, respectively. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Vehicles (IoV) is a new framework for intelligent transportation systems. One of its goals is to improve safety and increase the quality of service for passengers. Topology changes in IoV present significant challenges to the function of safety applications. Most routing algorithms offer their solution, independent of infrastructure or based exclusively on infrastructure. Although the lack of access to roadside units disrupts infrastructure‐based algorithms, methods that do not use infrastructure will also not enjoy the benefits of high‐speed data transmission. This research proposes the reliable and infrastructure‐independent routing (RIIR) routing algorithm to increase the reliability of real‐time routing in the IoV. The RIIR, in addition to simultaneous covering capability to use intelligent infrastructure for accelerating data transmission and flexibility in conditions of lack of access to infrastructure, by introducing three new criteria entitled "history of vehicle movement," "conformity of vehicle speed with the harmonic speed average of nearby vehicles," and "number of reliable neighbors," scores vehicles and then selects the most suitable vehicles as route members. The RIIR measures the conformity of the previous presence of vehicles on the current route and the stability of their link with adjacent vehicles. Furthermore, it prevents congestion by halting the spread of route request messages at intersections not leading to the destination. The efficiency of the RIIR has been theoretically proven, and extensive simulations with multiple scenarios in SUMO and NS3 show the superiority of the RIIR in increasing the packet delivery rate and reducing route failure, average delay, and control overhead. [ABSTRACT FROM AUTHOR]

Summary: Various opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC. [ABSTRACT FROM AUTHOR]

Summary: A multi‐stream cyclic interleaving architecture for an interleaved frequency division multiple access (IFDMA) system is proposed. The proposed scheme exploits the full benefits of maximal length sequence (m‐sequence), in contrast to single data stream transmission scheme proposed in earlier works, thereby allowing simultaneous transmission of multiple data streams by each user. The cyclic spreading allows the separation of multiple data streams for each user in addition to intersymbol interference (ISI) minimization. The assignment of orthogonal subcarriers to each user compensates multiuser interference (MUI). The increase in the number of data stream transmission improves the data rate of the system. The proposed scheme employs maximal ratio combining (MRC) to maximize the instantaneous signal‐to‐noise ratio (SNR) of the multipath signal. The performances of the proposed interleaving scheme are compared with the minimum mean square error frequency domain equalization (MMSE‐FDE)‐based conventional interleaving scheme. Despite the simultaneous transmission of multiple data streams, the proposed scheme retains the performance of single data stream transmission. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient‐based technique needs to be implemented in data transmission. The most widely used methods are clustering‐based data transmission and energy efficient routing detection. In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bio‐inspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced. [ABSTRACT FROM AUTHOR]

Summary: Recently, flying ad hoc networks (FANETs) has become a core research area in wireless networks that involves multiple unmanned aerial vehicles (UAVs). It is widely used in modern warfare for surveillance, monitoring and reconnaissance. The routing in FANETs poses a more significant challenge due to limited energy and frequent link disconnection between the UAVs. Consequently, an effective route is always required to ensure data transmission between UAVs. Therefore, this research proposes a link‐optimized cone‐assisted location (LoCaL) routing protocol for FANETs. The main goal of the proposed LoCaL is to enhance the link duration between the UAVs in which a source selects a forwarding UAV from a given set of neighbours by estimating the residual energy, link duration time and safety degree parameters. Proposed LoCaL provides better stability and less frequent route breaks between source and destination. Further, the mathematical formulation of the proposed approach is presented through the utility function to enhance the route stability by selecting all those relay UAVs in the cone‐shaped request zone, which reduces the routing overhead in discovering the route. Finally, the performance of the LoCaL has been presented through key indicators such as energy consumption, routing overhead, message delivery ratio, network lifetime and delay compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Wireless communication has been adapted for a variety of difficulties as a result of recent developments in sensor networks. The sensors are meant to communicate with network nodes located at a distance and to adapt to various conditions. In addition, Internet of Things (IoT) devices are becoming a part of WSN and are being tailored to various healthcare systems. Nonetheless, the presence of massive nodes and rogue nodes makes transmitting data packets difficult. The procedure, however, confronts a number of obstacles, including data security, continuous monitoring, and routing issues. A variety of ideas are explored to help with the situation, but they all have flaws. A network statistics‐based routing and path orient data encryption system has been developed to address this problem (NSR‐PDE). The strategy focuses on keeping track on patients and providing strong, decisive support systems. The model is created to access data stored on cloud servers and allows for the monitoring of multiple patients in different locations. The model begins by monitoring the whole bodily states of patients in real time using dedicated equipment. Such monitored characteristics are provided over a variety of channels, with route selection based on the patient's class and the type of information being transmitted. The approach calculates the network constraint secure route measure (NCSRM), which is calculated using route information, node behavior, and physical attributes. Similarly, data transmission is protected by path orient data encryption (PDE), which ensures data security according on the route's type and conditions. [ABSTRACT FROM AUTHOR]

Summary: A multi‐mode supported elliptical ring core fiber is demonstrated in the suggested work. The reported fiber structure is capable of supporting a large number of modes and influences to be favorable for high capacity, less crosstalk in the area of mode division multiplexing, and Internet of Things (IoT) compatible communications. To ensure the efficiency of the described structure, many characteristics such as V‐parameter, birefringence, sensitivity response, and confinement loss are analyzed. The investigation results are obtained from the offered structure by taking 22 distinct eigenmodes and the wavelength range from 1.54 to 1.7 μm. The evaluation of the numerical analysis shows the confinement loss of less than 2.85 × 10−9 dB/m, the sensitivity response of 80,757.73292 nm/RIU. These are, to the best of our knowledge, the maximum responses received in this trade. IoT‐based technologies allow networked tools to communicate, sense, and interact with each other. Where the wireless networks are harsh to operate on the ecological surroundings, the optical technology as fiber optic cables delivers the best resources of handling data and transferring it from one place to another effectively. [ABSTRACT FROM AUTHOR]

Summary: Internet of Everything (IoE) is one of the emerging technologies in the advancement of digital life and innovation. But the major issue that is to be addressed is the security concerns over such an environment, especially in end‐to‐end device communication. The proposed approach primarily focuses on reliable end‐to‐end device communication using post‐quantum location‐aware encryption, aiming to achieve confidentiality and integrity in an Internet of Everything environment. Even though the post‐quantum techniques have shown to be one of the evolving solutions for reliable data communication, but its robustness against the Man‐In‐The‐Middle and Sybil attacks is still a wide‐open subject and undiscovered. Numerous traditional encryption algorithms are now in use, while post‐quantum encryption is being studied and considered as a quantum‐safe substitute. The proposed approach also demonstrates a robust attack model which focuses on communication network threats like namely, Man‐In‐The‐Middle and Sybil attacks intending to evaluate the overall performance of the network by analyzing the performance parameters like, number of nodes in the network, message size, execution time, and memory consumption by the nodes and aiming to achieve an appreciable accuracy in an IoE environment. [ABSTRACT FROM AUTHOR]

Summary: Internet of Thing (IoT) is a trending internet connectivity extension to connect physical devices on the heterogeneous networks to meet application requirements on various grounds, that is, health, agriculture, industry, and so forth. The amount of streamed data is generated by the IoT devices and processed via IoT device itself or external resources such as Cloud/Fog by considering real‐time constraints. The problem arises in real‐time data transmission when a huge amount of stream data is generated from IoT devices, and it travels through the network and needs to be processed on low‐speed IoT infrastructure. Considering this scenario, there is an extremely requirement for designing a model relying on real‐time stream data handling for speedy transmission considering IoT device real‐time communication constraint. On the other hand, IoT devices are also very sensitive towards energy consumption during the streamed data transmission, and low battery devices become a barrier in data handling in real‐time. To overcome these limitations, a model is proposed to compress and encrypt the data stream during the IoT network transmission. In this model, delta encoding facilitates the compression in form of deltas. To ensure security requirements, a light weighted stream cipher encryption technique is proposed. For the experimentation, a test‐bed setup is prepared to utilize Raspberry Pi (IoT device), CPU, and collectors to collect data stream. The proposed approach is compared with the baseline model, that is, LDPC encoding on temperature sensor datasets. As an outcome, the data are compressed up to 37.59%, and the average transmission time and the average power consumption are reduced by 72.57% and 68.86%, respectively. [ABSTRACT FROM AUTHOR]

Summary: The evolution of technologies in data communication system has resulted in generation of large data. With the availability of limited bandwidth and to maintain the energy efficiency of the wireless communication system, data reduction plays a significant amount of role in preserving the lifetime of wireless communication system. Considering the significance of this problem in wireless communication system, in this work, we present data reduction and recovery or reconstruction on multivariate data using principal component analysis (PCA). In general, we send the eigen coefficients from the source and use these coefficients at the destination to recover or reconstruct the original data back in wireless communication system. We employ in our work two different digital modulation techniques such as phase‐shift keying (PSK) and quadrature amplitude modulation (QAM) with seven different bit rate to demonstrate our approach. We carry the experiments independently using five different signal ensemble and four different multiple‐input, multiple‐output (MIMO) configuration along with Rayleigh fading. This work presents an extensive simulated benchmark results based on performance matrix such as root mean square error (RMSE) and bit error rate (BER) at different values of signal‐to‐noise ratio (SNR). We further validate our approach by presenting synthesis of our proposed model using Xilinx target device Virtex‐6 LX240T Field Programmable Gate Array (FPGA). The experimental results reported based on simulation, synthesis, and validation of model demonstrate the applicability of our proposed approach in real‐time scenario. [ABSTRACT FROM AUTHOR]

Summary: Terahertz (THz) band communication is extensively used to reach future communication needs for large capacity. Moreover, the multiple input multiple output (MIMO) and Non‐orthogonal multiple access (NOMA) approach with multi‐antenna enable users to share more data with good spectrum efficiency. Due to its various applications, different research works are done in MIMO and NOMA. But the existing approach used in NOMA‐MIMO has a major limitation in power efficiency. Hence, this research work is presented for maximizing power efficiency. The power efficiency is improved effectively using the THz‐NOMA‐MIMO system in the presented approach. Initially, users are clustered effectively by utilizing the enhanced fuzzy c‐means clustering (EFCM) approach. Afterwards, hybrid precoding with threshold orthogonal matching pursuit (TOMP) approach is presented for generating the hybrid precoding vectors. Here, optimal precoding vectors are generated using the geometric distance‐based TOMP approach. This process eliminates the interference present in the data communication, thus improving power and spectral efficiency. Finally, the weight‐based distributed power efficient algorithm (WDPEA) achieves the finest power allocation. These combinations of effective approaches enhance the power and spectral efficiency. The presented methodology is implemented in the working platform of MATLAB. The performance of the presented approach is examined with the different existing approaches in regards to spectral efficiency, energy efficiency, effective capacity, and the mean squared error. When the users are at 20, the sum spectral efficiency of existing zero forcing (ZF) is 21, regularized zero forcing (RZF) is 54, maximum ratio transmission (MRT) is 71, and the proposed method is 77 bit/s/Hz. When the signal to noise ratio at 25 dB, the energy efficiency of existing time division multiple access (TDMA) is 1.24, digital MIMO is 1.21, orthogonal multiple access (OMA) is 1.23, weighted NOMA is 1.35, and the proposed method is 1.38 bit/joule/Hz. The proposed method gets better results when compared to other methods. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms. [ABSTRACT FROM AUTHOR]

Summary: Human–computer interaction plays a vital role in wireless body area networks, internet of things, and big data. Wearables are low‐power devices with minimal battery capacity. In general, wearables suffer from energy losses due to changes in the user's body posture, diffraction, reflection, and shadowing of the human body. As a result, many control packets are needed to ensure proper communication in wireless body area network. Hence, this research proposes a long short‐term memory‐based power‐aware (LSTM‐PA) algorithm to ensure burst data transmission during prompt heterogeneous activities in the presence and absence of inter‐WBAN interference. The proposed algorithm predicts the best quality time (BQT) for data transmission by activity classification and robust R2 similarity (RRS) metric. The minimum transmission power is estimated by the critical point classification technique. The activity classification accuracy is 92% in the LSTM‐PA algorithm. The energy consumption in the node is reduced by up to 46.34% compared to benchmark algorithms. [ABSTRACT FROM AUTHOR]