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Summary: The Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the "deadline‐cost aware task scheduling" (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result. [ABSTRACT FROM AUTHOR]

Summary: With the promotion and application of IPv6 in the world, there is a growing demand for IPv6 alias resolution. How to resolve IPv6 alias efficiently and accurately becomes an urgent problem to be solved. After analyzing the features of IPv6 addresses, this paper proposes a large‐scale adaptive IPv6 alias resolution method based on fingerprint information by combining Too‐Big Trick, UAv6, APD, and other alias resolution algorithms. It sends ICMPv6 probe packets to different types of IPv6 addresses to get the fingerprint information of target hosts. After filtering the classified addresses, our proposed alias resolution method is adaptively selected to resolve alias addresses. In the experiment, we use multi‐thread method to resolve the aliases of large‐scale IPv6 addresses, which greatly improves the efficiency of detection. Meanwhile, we use IPv6 datasets collected from the organizations RIPE and CAIDA. By comparing with Speedtrap and Too‐Big Trick, we confirm the accuracy of this method and obtain more alias pairs. [ABSTRACT FROM AUTHOR]

Summary: This paper overcomes two key issues within radiation monitoring networks. Secure and accurate transmission of detected gamma radiation in remote areas is the first challenge. The second one is related to the robustness of transmitted signal under different fading scenarios. These issues are handled during signal transmission over worldwide interoperability for microwave access (WiMax) networks. The signals are experimentally captured using a 137Cs point source, a NaI (TI) detector, and an analog‐to‐digital converter with 16 MS/s. For the first issue, several algorithms are implemented for secure and efficient transmission of gamma‐based signals. These algorithms include encoding of captured signal, decoding, interleaving, and frequency transformation of modulated signal. Encoding and decoding of target signals are conducted using the Reed Solomon technique and convolutional encoding with a Viterbi decoder. A superior algorithm for channel interleaver using the combinational interleaving method is introduced. Fast lifting transform (FLT) is applied for transforming modulated signal into frequency domain. The results declare that CC = 1/2 with BPSK‐1/2 coding rate introduces a better BER of 0.00068. A higher SNR of 1.5 dB is accomplished by the combinational interleaving algorithm. For the second issue, several models are suggested for overcoming channel degradations under gamma, exponential, Poisson, and geometric fading. Closed‐form expressions that describe outage probability, bit error rate (BER), and symbol error rate (SER) are suggested. Geometric and gamma fading achieve respectively a lower BER of 0.18 and 0.48. These channels demonstrate similar channel performances at 3.6 dB. The proposed models are validated through comparison with published work. Finally, the proposed algorithms enable higher data rate transmission of detected 137Cs signals under higher robustness using WiMax networks. [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: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a novel paradigm that consists of a wide network of machines and intelligent devices with the capability of communication and interaction with each other. One of the significant issues for the IoT is its healthcare applications. The positioning systems based on their environment can be classified into two main categories: Indoor and outdoor. Recently, IoT indoor positioning attracts too much attention among researchers for patient monitoring. Hence, in this paper, a stress‐free floor plan indoor positioning algorithm for patient monitoring is proposed, called THIP. The history of the patient's movement is stored in a trajectory history, and this information is employed to estimate the patient's more accurate position. The proposed THIP algorithm is simulated in MATLAB software. Several experiments are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed THIP algorithm raises patient positioning accuracy and declines the distance error cumulative compared to LiFS by about 4.68% and 60.6 m, respectively. Moreover, it can identify the floor level of the moving user. [ABSTRACT FROM AUTHOR]

Summary: In recent years, neighbor discovery techniques using directional antennas have attracted widespread attention. Most currently available directional neighbor discovery techniques are designed based on two‐dimensional (2D) space. Although these algorithms can accomplish the discovery between nodes, the algorithm portability is poor for three‐dimensional (3D) platforms, and the application scenarios are limited since new communication scenarios such as unmanned aerial vehicle (UAV) groups and maritime fleets emerge where information needs to be delivered in real time and nodes are located in 3D space. This paper proposes a new deterministic directional neighbor discovery algorithm in 3D space named 3D scan‐based algorithm (3D‐SBA) to meet the needs of the line‐of‐sight (LOS) scenes. Four existing neighbor discovery algorithms, namely, SBA‐based random mode selection (SBA‐R), quorum, complete random algorithm (CRA), and SBA‐based leader election algorithm (LE), have been extended to our proposed 3D algorithm model for simulation and comparative analysis with 3D‐SBA. The simulation results show that the 3D‐SBA algorithm consumes 51.15%, 180.20%, 9.48%, and 17.49% of the time slots compared with the four existing algorithms mentioned above. However, the quorum algorithm has a very high node collision rate, up to 92.11%. Ultimately, the 3D‐SBA algorithm has the best performance considering the conflicts and the density of network nodes. [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: In large‐scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K‐means clustering algorithm and optimised K‐means based modified intelligent CH selection based on BFOA for large‐scale network (lar‐OK‐MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK‐MICHB algorithm for large‐scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar‐OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi‐level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment. [ABSTRACT FROM AUTHOR]

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

Summary: Recently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR. [ABSTRACT FROM AUTHOR]

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

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

For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The fifth paper entitled "Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles" by Gunasekaran Manogaran et al. proposed a fitness-ant colony optimization (FACO)-based route optimization for improving the driving range of electric vehicles (EVs). The second paper entitled "A SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic" by Liuwei Huo et al. proposed a novel accurate and software defined networking (SDN) based fine-grained traffic measurement approach to obtain comprehensive traffic in VCN. [Extracted from the article]

Summary: Vertical handoff is a major concern in the operation of mobile connections. Multiple wireless networks collate to provide smooth and quality service to the users over mobile connections. This paper formulates a Markov decision process for handoff decisions with a sample space that includes a union of parameters that are important for making a handoff decision. The major contribution of this paper is to propose three different yet closely related algorithms for reducing the computational complexity of the original problem. In particular, we propose a feature‐wise assessment algorithm, a network‐wise assessment algorithm, and a hybrid approach for computational complexity reduction. Discussed algorithms give pseudo‐optimal solutions with a significant reduction in computational complexity. Results indicate that different complexity reduction algorithms perform best under different circumstances. This provides a guideline for the selection of complexity reduction algorithms based on real scenarios. [ABSTRACT FROM AUTHOR]

Summary: Data center networks (DCNs) are facing challenging control problems such as flow scheduling, congestion control, load balancing, and bandwidth allocation when dynamically handling heterogeneous mice and elephant flows. This paper focuses on the load balancing in DCNs as an online decision‐making problem with high complexity (NP‐hard problem). Traditional load balancing approaches are usually the heuristic algorithms relying on the operator's viewpoint and a short‐term knowledge of the traffic conditions and network environment. This paper proposes DeepRLB, a deep reinforcement learning (DRL)‐based load balancing approach for software‐defined networking‐based DCNs which exploits the deep deterministic policy gradient (DDPG) algorithm to adaptively learn the link‐weight values by observing the traffic flow characteristics. The actor and critic neural networks in the DDPG model have been designed based on two learning models: fully connected (DeepRLB‐Full) and convolutional (DeepRLB‐Conv). The output of the model is further used by the controller to determine the forwarding paths for traffic flows accordingly. We evaluated the performance of DeepRLB by comparing it with ECMP as well as a per‐packet load balancing algorithm in three different data center topologies under 30 heterogeneous traffic demand matrices. The obtained results showed that the DeepRLB algorithm outperforms the ECMP with respect to studied load balancing metrics considerably (DeepRLB‐Conv achieved the best performance). As a sample, DeepRLB‐Full and DeepRLB‐Conv were able to reach 19.14% and 24.46% reduction in the total number of over‐utilized and under‐utilized network links compared to ECMP, respectively. [ABSTRACT FROM AUTHOR]

Summary: Among communication systems, multiple input multiple output (MIMO) system has the capability of providing spectral efficiency, high data rates, diversity gain, and improved reliability. Suitably designed orthogonal frequency division multiplexing (OFDM) transmits the signal without inter symbol interference and multipath fading. Hence, MIMO‐OFDM is a strong candidate for 4G and 5G wireless systems. However, variations in signal‐to‐noise ratio (SNR) due to the random nature of the channel and poor capacity at low SNR are critical issues in conventional MIMO‐OFDM system. This paper explains how optimal power allocation (OPA), channel estimation, and coding improve the capacity and bit error rate (BER) performance of MIMO‐OFDM system. In OPA, the transmitter power is distributed adaptively to the channels based on the SNR statistics using singular value decomposition and water filling algorithm. In case of low SNR, less number of channels are selected for power allocation. Least squares (LS), minimum mean square error (MMSE), and least mean square (LMS) estimators are considered in comb type pilot channel estimation while turbo code is used with code rates of 1/2 and 1/3. From the simulation results, capacity improvement of 5 b/s/Hz and 11 b/s/Hz is observed for MIMO‐OFDM systems with four antennas and eight antennas, respectively. It is also found that performance of LMS estimator improved by 3.5‐dB SNR over LS and 1 dB SNR over MMSE. In this paper is shown how LMS estimator and 1/3 turbo code have been integrated with MIMO‐OFDM, and this has resulted in around 4‐dB SNR improvement over conventional MIMO‐OFDM. [ABSTRACT FROM AUTHOR]

Summary: Non‐orthogonal multiple‐access (NOMA) scheme using massive multiple‐input multiple‐output (MIMO) transmission suffers from interference between users. In this paper, we propose a novel MIMO‐NOMA framework based on space–time block code (STBC) to decrease the multiple‐access interference (MAI), when the number of users is increasing. In this paper, besides giving an analysis of network, cluster, group, and users, we first present the system model. Then, an efficient precoder and detector are designed based on interference analysis. In particular, the proposed scheme can decompose a massive MIMO‐NOMA system into multiple separated single‐input single‐output NOMA channels. Also, analytical results are developed to evaluate the performance of the system. To address the dramatic increment of power level, with the aid of the proposed algorithm, the optimal solution is achieved. Mathematical analyses for both MIMO‐NOMA and STBC are presented to design efficient precoding and detection. Finally, simulation results, based on STBC, are provided to verify the theoretical analysis to demonstrate significant capacity improvements of the massive MIMO‐NOMA. [ABSTRACT FROM AUTHOR]

Summary: Internet of Underwater Things (IoUT) comprises various resource‐constrained sensor nodes; therefore, the routing followed in acoustic medium underwater should be energy efficient to preserve their energies. Due to large area covered in the scenarios of underwater, the multi‐hop communication leads to energy hole problem. Therefore, Energy hole mitigation through Optimized Cluster Head (CH) selection and Strategic Routing (EOCSR) in IoUT is proposed in this paper. The proposed work not only optimizes CH selection using Tunicate Swarm Algorithm but also incorporates strategic routing to address energy hole problem. The simulation results show that EOCSR improves stability and lifetime of network by 16.8% and 17.7%, as compared with recently proposed Moth Flame Optimization‐based routing method. [ABSTRACT FROM AUTHOR]

Summary: Heterogeneous networks (HetNets) have been a trending topic of interest for researchers in 5G technology. A HetNet structure comprises a macro cell network assisted by small cell networks such as pico cells and femto cells. This additional hardware ensures distribution of the user equipment (UE) load of the main base station (MBS) at the cost of a surge in the overall system power consumption. Optimized power consumption coupled with enhanced cell throughput by dynamic small cell ON/OFF strategy improves the energy efficiency (EE) in dense HetNets. This paper proposes two algorithms to switch the small cell ON/OFF based on cell throughput contribution rate (CTCR). CTCR is the ratio of actual cell throughput to the maximum cell throughput with full utilization of the allotted bandwidth. In the first method, the threshold to decide small cell ON/OFF has been carefully defined considering two important factors—the MBS‐SBS distance and the ratio of small cell density to UE density such that less loaded small cells that are closer to the MBS are the candidates chosen for sleep mode. In the second method, a correction factor in the computation of CTCR is introduced. It is a logarithmic function of the relative distance of the small base station (SBS) to the radius of macro cell coverage. This steps up the threshold for SBS that are close to MBS. They are more suitable sleep state candidates as their UEs can be served directly by the MBS and enable large user throughput. Simulation results show that the EE of the proposed amended CTCR method is 8% more than proposed CTCR method and 30.66% better relative to conventional load‐based sleep control method. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms. [ABSTRACT FROM AUTHOR]

Summary: Range‐free localization is a widely concerned technique that supports many essential network applications like coverage and clustering. Most existing localization technologies assume that nodes are uniformly distributed in a regular network with no obstacles and that propagation paths between pairwise nodes are close to straight lines. However, in real‐world scenarios, network topology is usually irregular, resulting in estimated distances that differ significantly from the corresponding physical distances and poor anchor deployment. These will lead to erroneous distance measurement and inaccurate localization results. This paper proposes a new algorithm, called the weighted range‐free localization algorithm (WRL for short), to improve the localization performance in irregular networks. Specifically, we first present a feasible scheme that can fulfill the information exchange and relative distance estimation for each node simultaneously. We then employ an optimal weight function to suppress the effect of cumulative errors and introduce the weighted bounding‐box algorithm to mitigate outlier location estimation induced by probably collinear anchor nodes. Simulation results show that, compared to state‐of‐the‐art methods, WRL improves localization accuracy while incurring lower computation and communication overheads in various irregular network topologies. [ABSTRACT FROM AUTHOR]

Summary: For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models. [ABSTRACT FROM AUTHOR]

Summary: In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as an NP‐complete problem. Moreover, the proposed algorithm exploits a new heuristic‐based earliest finish time technique for task mapping to virtual machines (VMs) section to decrease the failure rate. Experimental results on random DAGs indicate that RATSA improves some current algorithms in terms of reliability and has acceptable performance in makespan. The results reveal that the RATSA decreases the overall failure rate by 43% compared to some current task scheduling algorithms. [ABSTRACT FROM AUTHOR]

Summary: The wireless network environment has become increasingly complex, diverse, and dynamic. In recent years, the introduction of new concepts such as green networks and smart networks has diversified the optimization objectives of the spectrum resource management. Under this background, this paper proposes the novel deep learning‐driven distributed communication systems for cluster online educational platform considering human–computer interaction. Before the modeling, the literature review and the state‐of‐the‐art methodologies are introduced. For the framework design, first, the novel distributed communication system is designed considering the integration of the Q‐learning under the deep neural network structure. Then, the cluster computing model is integrated for the system to enhance the robustness. Later, the human–computer interaction together with the movement tracking models is proposed to finalize the interactive online platform. The simulation results show that the proposed algorithm can extract the information accurately, and transmission efficiency is guaranteed. Compared with the latest platforms, the proposed outperforms from the aspects of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Summary: The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities. [ABSTRACT FROM AUTHOR]

Summary: A sensor network is a situation‐based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load‐balanced and fuzzy‐ACO (DLB‐fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree‐ and energy‐balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster‐based hierarchical routing. Communication is performed at two levels. In the first level, node‐to‐cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy‐integrated ACO method is applied for inter‐cluster route formation. The route optimization is here performed under fuzzy‐based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed load‐balanced protocol is analyzed against the LEACH, LEACH‐C, LEACH‐CC, M‐LEACH, Fuzzy‐PSO, Fuzzy‐ACO, Fuzzy‐Cuckoo, and FMCB‐ER protocols. The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we mainly focus on the influence of network partitioning on the network and develop a new Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) based on network information. By studying the influence of the different number of partitions and partition mode on the whole sensor networks, and simultaneously comparing different MCs scheduling algorithms from multiple perspectives, such as the effective energy usage efficiency and the survival rate of sensor nodes in the whole network, the superiority of Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) scheduling strategy and the rationality and necessity of layered mechanism are proven. Simulations are conducted to verify the advantages of our scheme, revealing that our scheme outperforms in terms of dead node number, energy usage efficiency, and so on. [ABSTRACT FROM AUTHOR]

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

Summary: Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime. [ABSTRACT FROM AUTHOR]

Summary: In a digital world changing at high speed, data centers still play a major role in new network architectures. Software‐defined network (SDN) technology is considered as a suitable solution to solve and overcome several limitations of standard data centers including flexibility, scalability, and ease of management. However, in an SDN with a distributed control plane, the assignment of switches could be a cause of generating significant latency that could degrade the Quality of Experience (QoE) of certain services and applications hosted on the data center. In this paper, we propose a matching game algorithm ensuring a dynamic assignment of switches. Its main characteristic is to achieve a fair assignment while balancing the load generated by the data plane among all controllers resulting in a balanced response time. The numerical results show that our algorithm outperforms other existing state‐of‐the‐art algorithms in terms of minimizing controllers' response time. [ABSTRACT FROM AUTHOR]

Summary: Industrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods. [ABSTRACT FROM AUTHOR]

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

Summary: Unmanned aerial vehicle (UAV)‐enabled wireless sensor networks (WSNs) have evolved as the significant paradigms to communicate the information among the devices in today's scenario instantly. These technologies support for developing smart crop spraying, smart infrastructure, aerial photography and videography, emergency services, etc. in providing smart solutions. Therefore, there is a dire need to develop an energy‐efficient solution for data gathering through UAVs. In this paper, an optimum weighted probability function is proposed by considering residual node energy, node spacing, and average energy of networks. This function helps in optimizing the clustering process for three‐tier heterogeneity by minimizing the energy of the proposed network. The proposed methods achieve 29.45% and 52.48% increment in the network lifespan as compared to the existing algorithm. The reason for such a gigantic enhancement in the network lifespan is the selection of the highest capable node as a cluster head using the proposed function. [ABSTRACT FROM AUTHOR]

Summary: Accurate individual (per‐user) traffic prediction in cellular networks is considered a critical capability for the system performance improvement in terms of dynamic bandwidth allocation and network optimization. However, existing methods have limitations in capturing the characteristics of individual traffic, because the observed traffic data are usually incomplete and traffic consumption patterns significantly differ among users. In this paper, to fully exploit the inherent temporal‐spatial correlations of individual traffic data, this work models the traffic data as a three‐dimensional traffic tensor. Different from the "three‐step" strategies (i.e., "data processing + decomposition + prediction") in the existing methods, we propose a novel tensor completion (TC)‐based "two‐step" strategy, that is, "data processing & decomposition + prediction" for individual traffic prediction, which can recover and decompose the traffic data simultaneously. Furthermore, an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to solve the resulting model. To the best of our knowledge, this is the first work to apply the TC‐based "two‐step" strategy for individual traffic prediction in cellular networks. Experiments conducted on a real cellular traffic dataset empirically validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results. [ABSTRACT FROM AUTHOR]

Summary: A novel multiband pinwheel fractal antenna with monopole configuration is proposed. The pinwheel fractal antenna has been fabricated on a commercially available FR4 substrate with a thickness of 1.6 mm. This paper proposes a novel pinwheel antenna with a 50% reduction in the dimension of width with a reduced level of return loss. The proposed antenna has multiband characteristics covering the bands of GSM (900 MHz), DCS (1710 GHz), PCS (1900 MHz), UMTS (1920 MHz), and WLAN (2.4–5.9 GHz) applications. The projected antenna resonates at multiple frequencies with acceptable antenna gain and directivity. The proposed antenna design is optimized by particle swarm optimization (PSO) algorithm. The parametric studies and the design rule for the proposed fractal have been reported. The prototypes of the proposed multiband antenna have been demonstrated. The measured and simulated results ensure the multi‐resonance characteristics and authorize the projected design method and gratify the necessities of handheld wireless communication systems. The performance analysis proves that the proposed novel pinwheel antenna provides reduced return loss. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed. [ABSTRACT FROM AUTHOR]

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

Summary: Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms. [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: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

Summary: To design new revolutionary wireless communication systems, orthogonal frequency‐division multiplexing (OFDM)‐based massive multiuser (MU) multiple‐input multiple‐output (MIMO) has been shown to be the most promising technology to significantly enhance the spectral, energy, and hardware efficiencies. However, massive MU‐MIMO‐OFDM transmitters exhibit signal with high peak‐to‐average power ratio (PAPR). Accordingly, the nonlinearity of the radio frequency (RF) power amplifier (PA), which causes the most severe hardware impairment, is expected to be a low‐cost and energy‐efficient component to enable cost‐ and energy‐efficient massive MU‐MIMO‐OFDM BS deployments, generating harmful in‐band distortion and out‐of‐band (OOB) emissions. In this paper, we develop a PAPR‐aware downlink transmission scheme in an OFDM‐based large‐scale MU‐MIMO. Linear precoding of data and peak‐canceling signals (PCSs) are employed to reduce the PAPRs of the transmitted signals by exploiting the excess degrees of freedom (DoFs) provided by equipping the base station (BS) by a large number of transmit antennas. Specifically, we design PCSs to be added to the frequency‐domain precoded data signals, with the goal of reducing the PAPRs of their time‐domain counterpart signals. Most importantly, the added PCSs have to lie in the null spaces of their associated MIMO channel matrices such that they do not cause any MU interference (MUI) and OOB radiation. In this regard, an efficient algorithm is developed, which is based on different data and PCSs precoders, and the corresponding achievable PAPR reduction and bit error rate (BER) performance are analyzed. Moreover, to optimize a tradeoff between performance and complexity, linear precoders based on matrix polynomials (M‐POLYs) and gradient‐iterative approaches are studied for both data and PCSs precoding. Simulation results reveal that these latter provide similar performance as the regularized zero‐forcing (RZF) and orthogonal projection null space (OPNS)‐based data and PCSs precoders, while they need much lower computational complexity. The substantial PAPR reduction provided by the proposed algorithm offers interesting insights for the design of energy‐efficient massive MU‐MIMO‐OFDM systems. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the performance of a co‐operative device‐to‐device (D2D) network is investigated. All the D2D users sense the cellular user (CU) spectrum and use it in hybrid mode: underlay and overlay. The expressions of throughput and outage are developed under the considered network scenario. An algorithm is proposed to optimize the sensing time to minimize the outage. The performance is investigated in terms of throughput and outage for several network parameters such as a number of D2D users, sensing time, signal‐to‐noise ratio (SNR), allowable interference threshold, and CU transmit power. Sensing time is optimized to minimize the outage. The interference threshold and target rate have a significant impact on network performance. The network has to suffer outage for lower values of allowable interference threshold and higher values of the target rate. The impact of channel fading on the network performance is also studied. A simulation model is developed in MATLAB on the basis of the analytical development of the considered network model. [ABSTRACT FROM AUTHOR]