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Video streaming is expected to account for a large portion of the traffic in future networks, including wireless networks. It is widely accepted that the user datagram protocol (UDP) is the preferred transport protocol for video streaming and that the transmission control protocol (TCP) is unsuitable for streaming. The widespread use of UDP, however, has a number of drawbacks, such as unfairness and possible congestion collapse, which are avoided by TCP. In this paper we investigate the use of TCP as the transport layer protocol for streaming video in a multi-code CDMA cellular wireless system. Our approach is to stabilize the TCP throughput over the wireless links by employing a recently developed simultaneous MAC packet transmission (SMPT) approach at the link layer. We study the capacity, i.e. the number of customers per cell, and the quality of service for streaming video in the uplink direction. Our extensive simulations indicate that streaming over TCP in conjunction with SMPT gives good performance for video encoded in a closed loop, i.e. with rate control. We have also found that TCP is unsuitable (even in conjunction with SMPT) for streaming the more variable open-loop encoded video. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The concept of a person-based number is attractive due to several pressing demands in implementation of current wireless communication systems. Some of these are: sudden proliferation in number of area codes; the need for freedom from number changes due to changes in service provider, location, or area code; and demand for improved and integrated communication service for users. Some important factors that determine the efficiency of a mobility management scheme include fewer number of times that locations need to be updated, reduced amount of overhead data that represents mobility related information, and efficient storage mechanisms that allow for fast storage and retrieval of this information. In this paper, we shall outline a mobility management scheme that provides a globally unique personal number and offers important benefits in regard to the issues cited above. For an efficient implementation of such a scheme however, an investigation of its performance is required in order to estimate signaling traffic and signaling delays. This paper will focus on some recent studies conducted on an analysis of this scheme by using query and update operations as the metrics for determining average call delay and control data storage and transmission requirements. A comparative analysis is also performed with equivalent cases of query and update in the IS-41 system. This analysis provides a valuable tool to determine network management requirements in the implementation of a person-based number scheme. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The traditional cable TV network has recently emerged as a promising infrastructure for high-speed data communications such as the Internet and multimedia. A cable TV network may cover a wide range of geographical area that thousands of subscribers live in or work at. However, this broad service range also brings some problems such as bandwidth congestion due to too many users, and the overhead in the design of access protocol. This article presents a bridged architecture that can increase the bandwidth re-usability and data transmission reliability over the cable TV network. It first describes the method of bridging the cable TV networks and then gives the design of a cable bridge which partitions the cable segment into several subnetworks. For load-balancing and fairness among subnetworks, a prioritized queuing scheme with a numerical analysis will be also described. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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: Cloud computing has been gaining widespread significance in recent times, especially with the advent of state‐of‐the‐art communication technologies and data‐handling systems. Large volumes of data are being handled in real time and their availability is made feasible to consumers in an efficient manner through cloud computing networks. However, security of such data being transferred through clouds characterized in a centralized configuration is of prime concern, which has been investigated in this research paper. Secured transmission of data through a secured key transmission using appropriate authentication mechanism has been the driving force behind the research carried out in this paper. In order to resolve the existing issues in clouds in terms of computational as well as storage complexities, a secure key transmission Rivest‐Shamir‐Adleman (SKT‐RSA) protocol is proposed based on the secure key transmission. Key distribution between the certified authority and the end user is proposed. The proposed protocol is a tree‐based cluster key distribution scheme. [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: 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]

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: In the ocean, huge number of sensor nodes (SNs) are located to transfer the information between other nodes using the Underwater Acoustic Sensor Network (UASN) framework. An underwater acoustic communication technique is utilized by this UASN to exchange the information. Because of environmental conditions and adverse channel, the SNs in UASN may have link breakages. Likewise, maximum target coverage rate for SN deployment is considered as another issue. So it is very essential to create a strong communication system in underwater together with the different kind of variations in ocean environment. As a result, the system will perform better data transmission with the severely fluctuating underwater communication conditions. In this paper, a latest optimization algorithm named as Sea Lion Optimization (SLO) procedure is proposed to discover the optimal location for SN in underwater communication. This algorithm optimally places the acoustic SNs based on the maximum connectivity rate by finding the targeted optimal position. The Matlab tool is utilized for implementation purpose, and the different kinds of parameters like connectivity rate, coverage rate, and delay are taken to evaluate the performance of proposed methodology. Moreover, the existing methods like deployment scheme, Connected Dominating set based depth computation Approach (CDA) approach, and distributive approach are taken to contrast the performance of proposed methodology. When compared to the previous algorithms, our proposed methodology achieves 95% connectivity ratio for varying number of acoustic SNs. [ABSTRACT FROM AUTHOR]

The article discusses various papers published in this issue, including one by H. Eltaief and H. Youssef on multi-layers connected chains and another by Binod Vaidya et al. on wireless network.

Summary: Device‐to‐device (D2D) communication in a cellular spectrum increases the spectral and energy efficiency of local communication sessions, while also taking advantage of accessing licensed spectrum and higher transmit power levels than when using unlicensed bands. To realize the potential benefits of D2D communications, appropriate mode selection algorithms that select between the cellular and D2D communication modes must be designed. On the other hand, physical‐layer network coding (NWC) at a cellular base station—which can be used without D2D capability—can also improve the spectral efficiency of a cellular network that carries local traffic. In this paper, we ask whether cellular networks should support D2D communications, physical‐layer NWC, or both. To this end, we study the performance of mode selection algorithms that can be used in cellular networks that use physical‐layer NWC and support D2D communications. We find that the joint application of D2D communication and NWC scheme yields additional gains compared with a network that implements only one of these schemes, provided that the network implements proper mode selection and resource allocation algorithms. We propose 2 mode selection schemes that aim to achieve high signal‐to‐interference‐plus‐noise ratio and spectral efficiency, respectively, and take into account the NWC and D2D capabilities 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: In multichannel cognitive sensor networks, the sensor users which have limited energy budgets sense the spectrum to determine the activity of the primary user. If the spectrum is idle, the sensor user can access the licensed spectrum. However, during the spectrum sensing, no data transmits. For improving the network throughput and saving more energy consumption, we propose the simultaneous spectrum sensing and data transmission scheme where the sensor receiver decodes the received signal, and from the remaining signal, the status of the channel (idle/busy) is determined. We also consider that the sensor users are powered by a radio‐frequency (RF) energy harvester. In this case, energy harvesting, data transmission, and spectrum sensing are done simultaneously. On the other hand, we select the proper sensor users for spectrum sensing and energy harvesting. We also allocate the best channels for data transmission simultaneously so that the network throughput maximizes and the constraints on the energy consumption and the detection performance are satisfied for each band. We formulate the problem and model it as a coalition game in which sensors act as game players and decide to make coalitions. Each coalition selects one of the channels to sense and transmit data, while the necessary detection probability and false alarm probability and also the energy consumption constraints are satisfied. The utility function of a coalition is proposed based on the energy consumption, false alarm probability, detection probability, and the network throughput. This paper proposes an efficient algorithm to reach a Nash‐stable coalition structure. It is demonstrated that the proposed method maximizes the network throughput and reduces the energy consumption while it provides sufficient detection quality, in comparison to other existent methods. [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]

Wireless body area networks (WBANs) have been developed as human body monitoring systems to predict, diagnose and treat diseases. The two important concerns for WBAN are sensor lifetime and latency. Because the signal transmission in WBAN takes place in or around the human body, the fading and collision of the channel due to other existing wireless devices affect the packet error rate significantly. Hence, the lifetime and latency of the sensor are crucial. To mitigate these problems, a cognitive radio (CR) based WBAN can be a promising solution. In this paper, a cognitive forward error control mechanism for CR-based WBAN has been presented. Several issues in CR-based networks have been addressed so far to cope with energy and latency issues. However, the error control mechanism has not been investigated for CR networks. Furthermore, existing studies of the error control mechanism for wireless sensor networks cannot be applied to the CR network because of the opportunistic spectrum access mechanism (i.e. the intermittent availability of spectrum resources). The method presented in this paper adaptively selects the number of hops to the sink and adjusts the redundancy to minimize the expected latency and energy consumption. The mathematical analysis and numerical comparison show that the proposed method achieves better performance in terms of latency and energy efficiency in the multihop CR sensor network. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As the higher and higher frequency bands of existing metallic cables in access networks are being continuously exploited by modern transmission technologies, such as the G.fast, the necessity of providing accurate and suitable modeling of their transmission characteristics is evident. Therefore, this paper is focused on modeling of a propagation constant of twisted pairs and metallic cables at high frequencies up to 250 MHz, and an innovative arsinh model is proposed and described. This new model is based on an idea of adopting inverse hyperbolic sine function for modeling of both secondary line coefficients, attenuation constant and phase constant, and its main motivation is to provide their accurate estimations for G.fast frequencies up to 250 MHz for various types of metallic cables while maintaining a low computational complexity. The proposed model was compared with numerous characteristics measured for various real metallic cables as well as with several existing models in order to illustrate its potential. The results, which are presented within this paper, clearly illustrate that the proposed arsinh model generally outperforms existing standard models based on the equal number of required parameters. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Under some special environments in China, there exist moving radio radiations with high transmission powers that can degrade the performance of the power line communications systems significantly. Such radiations overlap with the OFDM-based signals in time and frequency domains, and hence, it is very difficult to mitigate directly if not possible. The paper introduced the fractional Fourier transform trying to deal with such moving radio interferences. When using the fractional Fourier transform to separate signals and noises/interferences, the fractional order plays a key role, but there is no method to obtain its optimal value in the published papers. In the paper, like for the time-frequency representations, we defined the second moments of the fractional Fourier transform of the signal. The fractional order, corresponding the maximum of the second moments, is the one used to separate the OFDM signals and the moving radio interferences, which is modelled as the Doppler signals in our scheme. The proposed method can mitigate the moving radio interferences significantly by applying the proposed method to the simulated OFDM signals and the measurements data. The results also show that the system's BERs can be improved from 10−2 to 10−5 for lower signal-noise-rate (SNR), and from 10−4 to 10−9 for high SNRs. Copyright © 2014 John Wiley & Sons, Ltd. [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]

This paper proposes a new survivable traffic grooming wavelength retuning (STGWR) scheme in an all-optical wavelength division multiplexing (WDM) network. In a dynamic WDM network, a connection may require a bandwidth less than a wavelength capacity. In addition, a connection should be protected against any network failures. Survivable traffic grooming (STG) can protect connections at subwavelength granularities. Wavelength retuning is a promising approach in an all-optical WDM network, where a signal must remain on the same wavelength from its source to the destination, to alleviate the wavelength continuity constraint and reduce the connection blocking probability. Although both STG and wavelength retuning have attracted extensive research attentions nowadays, no effort has been made to combine these two promising approaches in one network. In this paper, we propose a wavelength retuning scheme with no service interruption in an all-optical network with STG capability. The scheme allocates two routes, one for the active path and other for the backup path, in a shared mesh restoration manner to each incoming connection request and conducts wavelength retuning only on the backup path. Both wavelength retuning and mesh protection are done at the connection level instead of at the lightpath level. The simulation results of the proposed schemes are also presented. Copyright © 2008 John Wiley & Sons, Ltd. [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]

In Delay Tolerant Networks (DTNs), mobile nodes exploit encounter chances to transmit data via the established opportunistic link between encounter nodes. The transmission capacity between two encounter nodes is constrained by the duration of opportunistic links. How to transmit data efficiently is one of the fundamental and most important issues in DTNs. Recently, Random Linear Network Coding (RLNC) is proposed as a promising approach to improve data transmission efficiency in DTNs. To facilitate the development of deployment of RLNC based routing protocols, it is imperative to quantify the performance gain brought by RLNC in a rigorous and systematic way. In this paper, we develop an analytical model to evaluate the data transmission performance of RLNC based epidemic routing in DTNs. In the model, we consider that multiple unicast communication sessions compete for limited \capacity. Numerical results validate the effectiveness of our analytical model and demonstrate the significant performance improvement for data transmission in DTNs brought by RLNC. Our work of this paper provides a general tool for performance evaluation and useful guidelines for designing RLNC based routing protocols in DTNs. Copyright © 2015 John Wiley & Sons, Ltd. [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: Opportunistic networks aim to deal with the unpredictable environment for data communication. Messages in opportunistic networks need to be stored with the node in the absence of any suitable forwarder. With such a store‐carry‐forward paradigm, it aims to eliminate challenges of no pre‐determined/fixed connectivity, absence of direct/in‐direct routes from source to destination, and high error rate and delay in the network. Node carrying message roams in the network to find a suitable forwarding node. In this study, the survey for various routing protocols and mobility models for opportunistic networks is discussed along with tools available for simulations of opportunistic networks. Further, various challenges in opportunistic networks such as storage management, congestion management, and energy constraints are discussed. Many applications for the opportunistic network, along with real‐time case study, are presented in the paper. The objective of the study is to provide future direction to the researchers, along with the systematic review. Hence, future objectives are discussed to mitigate the challenges faced in the current scenario. Also, based upon the study, it is observed that most researchers have taken epidemic, ProPHET, and spray and wait routing protocols as their base of study. Performance analysis for these protocols is done using opportunistic network environment (ONE) simulator. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the popular 2.4‐GHz band is used in different systems, such as Wi‐Fi, Bluetooth, wireless sensor systems, and wireless cameras. Instead of the over‐crowded 2.4‐GHz Wi‐Fi band, this research offers the experience of using the 5‐GHz Wi‐Fi band, which provides more spectrum availability, more channels, larger bandwidth, faster data transmission, higher data rates, higher speed, and better quality of service compared to those of the 2.4‐GHz band. In this paper, practical implementation and testing of a cooperative spectrum sensing system are presented. The spectrum utilization in the 5‐GHz Wi‐Fi licensed band at six different locations is investigated to allow the transition of secondary users (SUs) to free bands. The spectrum measurement is performed on a centralized cooperative spectrum sensing system, which consists of a master cognitive radio node and five cognitive radio stations. The measurement and simulation results for the practical system are compared with the previous related measurements obtained in Singapore, Barcelona, North Dakota (USA), and Germany. They all agree that the spectrum is underutilized, and it needs to be better utilized for increasing the spectrum efficiency. The practical results show that the newly implemented system in the 5‐GHz range fulfills the requirements of users with high efficiency and high quality of service compared to those of the 2.4‐GHz system. [ABSTRACT FROM AUTHOR]

The digital video broadcasting-handheld (DVB-H) standard is developed by European Telecommunications Standards Institute to broadcast digital videos to handheld devices, but data loss is a critical issue due to the broadcast behavior. On the other hand, DVB-Internet protocol datacasting (DVB-IPDC) integrates DVB-H with an Internet protocol-based wireless network to provide bidirectional communication. We adopt this wireless network to deal with data retransmission and call it a recovery network. The paper argues that network coding can improve retransmission efficiency of the recovery network because DVB-H packet loss often exhibits high correlation. In addition, DVB-H packets may be heterogeneous in the sense that they have different importance. According to these two arguments, the paper considers that DVB-H packets are associated with different profit depending on their importance and proposes an α- maximum profit network coding problem. It asks the base station in the recovery network to use no more than α coded packets for handheld devices to retrieve the lost DVB-H packets such that the overall profit is the maximum. An efficient exclusive-or coding scheme, namely the profit-based exclusive-or network coding (PEN) algorithm, is proposed to solve this problem. Extensive simulation results also verify the effectiveness of the PEN algorithm. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]