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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]

Wireless IP networks will provide voice and data services using IP protocols over the wireless channel. But current IP is unsuitable to provide delay or loss bounds and insufficient to support diverse quality of service, both required by real-time applications. In order to support real-time applications in wireless IP networks, in this paper a measurement-based admission control (MBAC) with priority criteria and service classes is considered. First we have shown the suitability of MBAC in wireless IP networks by comparing its performance with a parameter-based scheme. Next, we have investigated the performance of strictly policy-based MBAC and policy plus traffic characteristic-based MBAC schemes in terms of (1) increasing the user mobility, (2) changing traffic parameters and (3) the presence of greedy users. The efficiency and fairness of each scheme are measured in terms of lower class new and handoff traffic performance. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper studies the performance of radio assignment algorithms for portable access in cellular systems. Several channel access procedures are proposed and simulated using block oriented network simulator (BONeS) simulation of a model 36-port system. Simulation results exhibit that load-sharing system with LIBTA algorithm is better than directed retry system with the same algorithm by around 0.9 erlangs while better than quasi-fixed channel assignment (QFCA) system by around 2 erlangs if the grade of service (GOS) is constrained to less than 10 per cent. Plus, a hybrid time-slot selection procedure is proposed to enhance the system performance. It is observed that systems with hybrid time-slot selection perform better than those with LIBTA algorithm in GOS under heavy load. It is also observed that load sharing system with hybrid time-slot selection algorithm is better than directed retry system with the same algorithm by around 0.7 erlangs and better than QFCA system by around 2 erlangs. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to extend fair queueing algorithms to wireless networks, we propose a channel error and handoff compensation scheme. A compensation is performed by a compensation server and a priority swapping mechanism. The proposed compensation scheme provides a short-term fairness guarantee for an error-free session, long-term fairness guarantee for an erroneous session, fast handoff and traffic-specific quality of service control. Copyright 2002 John Wiley & Sons, Ltd. [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: 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: 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: 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: Uncoded space‐time labelling diversity (USTLD) is a recent scheme that improved the error performance compared to conventional multiple‐input, multiple‐output systems. Thus far, USTLD has suffered from limited achievable data rates, as the original model uses only two transmit antennas. This motivates for the work in this paper, where the USTLD model is extended to allow for any desired number of transmit antennas. An analytical bound for the average bit error probability of this high‐rate USTLD (HR‐USTLD) system is derived. This expression is verified using the results of Monte Carlo simulations, which show a tight fit in the high signal‐to‐noise ratio region. The increased data rates associated with larger transmit antenna arrays in HR‐USTLD systems come at the cost of increased detection complexity. Therefore, this paper studies the application of low‐complexity detection algorithms based on the popular QR decomposition technique and proposes a new algorithm specifically designed for HR‐USTLD systems. Analysis of this algorithm in terms of accuracy and computational complexity is also provided and benchmarked against maximum‐likelihood detection (MLD). It is shown that the proposed algorithm achieves near‐MLD accuracy, while reducing complexity by 79.75% and 92.53% for the respective 4 × 4 16QAM and 4 × 5 16PSK HR‐USTLD systems investigated. [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: The handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN. [ABSTRACT FROM AUTHOR]

Summary: Localization algorithms are essential for tracking, fault tolerance, and adding location context to Internet of Things (IoT) networks. Among the various range‐free techniques, DV‐Hop stands out for its lower computational complexity. Despite efforts to minimize manufacturing costs for sensors, they often find themselves deployed in remote and hard‐to‐access locations, making battery replacement a challenging prospect. These sensors can be utilized in a hostile environment, such as a field of battle, fires, floods, and earthquakes. In these types of scenarios, the sensors can become dead and stop communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. The faults of the sensor can lower the quality of the algorithm if they are not replaced. In such situations, we need to distribute supplementary target nodes to replace faulty nodes. This paper presented an improved DV‐Hop based method for localization of additional target nodes using grey wolf optimization (GWO). The proposed method will only localize the position of target nodes and minimizes the localization time, which makes the system more energy efficient. Simulation results conclude that the proposed GWO‐based DV‐Hop localization performed well as compared with traditional DV‐hop, genetic DV‐Hop, and Adaptive genetic DV‐hop. [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: This paper introduces a new detection mechanism for defending the cyberspace with a new logic that aiding the concept of deep learning. The process involves two phases, namely, feature extraction and classification. The initial phase is the feature extraction, in which the features are extracted from the given input data by the renowned principal component analysis (PCA). Subsequently, the extracted features are subjected to the classification phase, where the deep belief network (DBN) model is used. The DBN model classifies the presence of attacks like denial of service (DoS), probe, R2L, and U2R. In order to make the performance more excellent, this paper diverts the strategy to a new concept termed "Optimization Concept." Here, the hidden neuron of DBN is optimally selected by a new algorithm termed novel mutation rate‐based lion algorithm (NMR‐LA), which is the modified model of lion algorithm (LA). The performance of proposed algorithm NMR‐LA is compared over the conventional models in terms of both positive and negative measures like accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 score and Mathews correlation coefficient (MCC), false‐positive rate (FPR), false‐negative rate (FNR), and false‐discovery rate (FDR) and proves the betterments of proposed work. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a mode transformation algorithm based on traffic prediction in virtual multiple optical line terminal (OLT) passive optical network (PON) is proposed. By proposing exponential smoothing algorithm based on weight update (WU‐ESA), user traffic is predicted well. WU‐ESA is a combination of two algorithms: exponential smoothing algorithm (ESA) and genetic algorithm (GA). The weight in ESA is optimized by GA based on real‐number encoding. By setting two periods, GA part and ESA part can be separated effectively. By presenting elastic packing algorithm (EPA), the load balance problem in virtual multi‐OLT PON is solved. EPA is implemented based on WU‐ESA. By the simulation and analysis, the effectiveness of the proposed algorithms is demonstrated. Compared with traditional mode transformation algorithm, EPA shows good performances in delay, throughput, and packet loss. Compared with traditional mode transformation algorithm, the EPA makes the packet loss decrease by at least 5% when the system load is greater than 0.9. Meanwhile, the delay of the two subsystems can be kept at a relatively balanced level by the implement of EPA. For throughput, the use of EPA improves the throughput by 20% when the system load is high. [ABSTRACT FROM AUTHOR]

Summary This paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs. [ABSTRACT FROM AUTHOR]

Summary Unmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately O(N)$$ \mathcal{O}\left(\sqrt{N}\right) $$, compared to O(N)$$ \mathcal{O}(N) $$ for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss. [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: 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 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: 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: 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: 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: 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: 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: 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: 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]

Summary: The novel and optimal phasor measurement unit (PMU) observability has been attained based on the flower pollination algorithm (FPA). The prime objective is to select the optimal bus for placing the PMU to maximize the number of bus observance in the system. Many optimization techniques have been developed with an aim to optimize the PMU's required quantity with attaining full observability of the network. This paper proposes a new technique of modified FPA, which reduces the computation time and achieves greater accuracy. This approach also helps in achieving the maximum observability of power system based on choosing the optimum buses to place the PMUs. The proposed algorithm would be best suited and offers highly accurate results when analyzing the system for maximum observability with the minimum number of allotted PMUs. The FPA algorithm is implemented on standard IEEE 14 bus, IEEE 30 bus, and IEEE 118 bus systems. The proposed FPA algorithm outperforms other evolutionary algorithms. It yields better overall performance than other algorithm methods, in terms of the minimum number of PMUs with maximum observability, including contingency analysis. [ABSTRACT FROM AUTHOR]

Summary: The major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Summary: In practical terms, direct data domain least squares (D3LS) beamforming algorithm performance is severely decreased by the array steering vector uncertainty. In this paper, we investigate the performance of D3LS approach for beamforming. Uniform linear antenna array radar of dipole antenna is considered in the presence of mutual coupling between the elements of the array. The performance of D3LS beamforming algorithm is severely affected by uncertainty caused by mutual coupling. In order to test the robustness of the D3LS approach against uncertainty, a quadratic inequality constraint is considered to optimize weight vector of D3LS beamforming algorithm. The convex optimization method is used to calculate optimized weight vector. The proposed convex optimization problem is solved by barrier method. It is a subset of Interior‐point numerical analysis methods. Several different scenarios are used to evaluate the proposed algorithm in MATLAB. [ABSTRACT FROM AUTHOR]

Summary: Geographic routing in mobile ad hoc network (MANET) has gained much attention even though it fails to find a route when a node meets a void zone (hole). Therefore, an efficient approach is required to avoid the void issues and to effectively transfer the data packets if a path is available between the source and destination nodes. For such, a hybrid evolutionary algorithm is introduced with geographical routing protocol (GRP) for avoiding the void, which selects the shortest route for routing from the source to the destination by combining firefly (FF) algorithm and galactic swarm optimization (HFFGSO). This proposed algorithm exploits the strengths of both the FF algorithm and galactic swarm mechanisms. In this paper, the cross‐layer design approach is implemented, which contains data link and network layer. The proposed hybrid FFGSO‐GRP algorithm improves the routing process within these layers. The implementation process for this proposed technique is carried out in NS2 simulator. From the simulation results, it is observed that our hybrid protocol when compared with the existing approaches proves its significance in terms of packet delivery ratio (PDR), delay, throughput, energy efficiency, power consumption, routing overhead and hop count. [ABSTRACT FROM AUTHOR]

Summary: Pilot contamination has come up as one of the major limitations of massive multiple‐input multiple‐output. In the recent past, the pilot contamination resulted by the pilot reuse in inter‐cell system has been successfully handled. In this paper, three efficient algorithms have been proposed to reduce the pilot contamination in intra‐cell system. These schemes emphasize on assigning the available pilots to the users based upon their channel qualities by considering the interfering degree of the various pilot sequences. The first proposed algorithm is based on water filling approach, which attains the system throughput very close to that of brute‐force exhaustive search algorithm (ESA) and performs the best among the three proposed algorithms in terms of system throughput. The second algorithm is designed for achieving better user fairness (by assigning less interfering pilots to the poor channel conditioned users). The third algorithm improves the system throughput as compared to the second algorithm without losing much in terms of user fairness. Furthermore, the computational complexity incurred by all the proposed algorithms is much less than that of ESA. [ABSTRACT FROM AUTHOR]

Summary: To improve the quality of spatial information in a location‐based services (LBS), crowdsourced LBS (cLBS) applications that receive additional information such as the visit time of static spatial objects from users have appeared. In this paper, we propose a new type of nearest neighbor (NN) query called the k‐nearest reliable neighbor (kNRN) query, which searches for objects that are likely to exist. Suppose that in cLBSs, the user wants to find a restaurant that is likely to exist and is close to the user. In such a case, a kNRN query is highly recommended. In this paper, we formally define a data model in cLBSs and define reliable objects and a kNRN problem. As a brute‐force approach to this problem in a massive dataset that has large computational and I/O costs, we propose a 3DR‐tree‐based baseline algorithm, 2DR‐tree‐based incremental algorithm, and an a3DR‐tree‐based branch‐and‐bound algorithm for kNRN queries. A performance study is conducted on both synthetic and real datasets. Our experimental results show the efficiency of our proposed methods. [ABSTRACT FROM AUTHOR]

Summary: Cognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the implementation of a modified version of Bayesian relevance vector machine (RVM)‐based compressive sensing method on cognitive radio network with wavelet transform for spectrum hole detection. Bayesian compressive sensing is used in this work to deal with the complexity and uncertainty of the process. The dependency of the Bayesian compressive sensing on the knowledge of noise levels in the measurement has been relaxed through the proposed Bayesian RVM‐based compressive sensing algorithm. This technique recovers the wideband signals even with fewer measurements maintaining considerably good accuracy and speed. Wavelet transform is used in this paper to enable the detection of primary user (PU) even in the low regulated transmission from unlicensed user. The advantage of this approach lies in the fact that it enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation study is performed to evaluate the efficacy of the proposed technique over the cognitive radio environment. The performance of the proposed technique is compared with the conventional Bayesian approach on the basis of recovery error, recovery time and covariance to verify its superiority. [ABSTRACT FROM AUTHOR]

Summary: The optimization of indoor femtocell base stations (FBS) placement is a highly complex problem in which multiple performance figures can be considered. In this paper, we propose a biobjective optimization approach based on non‐dominated sorting genetic algorithm II (NSGA‐II) to solve the indoor FBS deployment problem in residential scenarios where a single cell is deployed within the coverage of a single dominant interfering external macro base station (MBS). The optimization problem is formulated with the objective of jointly minimizing outage probability and maximizing normalized downlink average throughput. Several conditions are simultaneously considered under the framework of Monte Carlo simulations, such as the effect of co‐channel interference in uplink and downlink, buildings with arbitrary layouts, realistic transmission schemes and antenna radiation patterns, random user distributions, and a well‐established empirical path loss model. The proposed algorithm, which determines the location and orientation of the FBS's antenna that provides a suitable compromise between the two objective functions, is evaluated in this paper. First, two different experiments are proposed to investigate the algorithm convergence; then, statistical analyses are formulated to characterize the relation between the values achieved for both objective functions; finally, the computational load is evaluated. Results indicate the convergence of the proposed method towards the real Pareto front and a significant reduction of the computational time with respect to a reference method based on exhaustive search, while pointing at new criteria for the selection of the objective functions. [ABSTRACT FROM AUTHOR]

A significant part of the data in mobile networks is transferred as bulk data with transmission control protocol (TCP) as apps or video downloads. When the video takes too long to start, users are more prone to abandon watching, which eventually leads to decreased revenue for the content provider. While it is widely known that TCP has performance issues in mobile networks, end-to-end measurement studies, especially based on real data, should be studied further. In this paper, we measure the efficiency of TCP in long term evolution (LTE) networks and provide an analysis on the reasons of unoptimal performance based on 235 000 measurements from deployed mobile networks. For this purpose, we propose an algorithm for LTE networks that detects the periods during a TCP connection where the path is saturated, ie, the network is the limiting factor. From this data, we find that TCP is a source for unoptimal performance, and assumed reasons from other papers are partly confirmed and partly refuted. Most importantly, we find that the amount of queueing on the path has significant impact on the achieved protocol performance. Lastly, we learn from the LTE experience and put the findings into a 5G context. [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]

We present a new radio frequency identification tag anti-collision algorithm in this paper. The main idea of this algorithm is that we should find the collided bit at first, and every tag contains a register RC, which has length the same as the tag's ID length to store the conflict bit, then all the subsequent processing are based on the conflict bit. In this new algorithm, tag uses a binary pulse string to transfer data to the reader, compared with the traditional query tree-based algorithms, which is based on the tag's ID; this new algorithm is more efficient and requires fewer read cycles for tag's identification. [ABSTRACT FROM AUTHOR]

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS-based approaches have been proposed with different sparse penalties, for example, zero-attracting LMS and Lp-norm LMS. However, these proposed methods lead to suboptimal solutions in low signal-to-noise ratio (SNR) region, and the suboptimal solutions are caused by LMS-based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. [ABSTRACT FROM AUTHOR]

Summary: Machine‐to‐machine (M2M) communications is one of the major enabling technologies for the realization of the Internet of Things (IoT). Most machine‐type communication devices (MTCDs) are battery powered, and the battery lifetime of these devices significantly affects the overall performance of the network and the quality of service (QoS) of the M2M applications. This paper proposes a lifetime‐aware resource allocation algorithm as a convex optimization problem for M2M communications in the uplink of a single carrier frequency division multiple access (SC‐FDMA)‐based heterogeneous network. A K‐means clustering is introduced to reduce energy consumption in the network and mitigate interference from MTCDs in neighbouring clusters. The maximum number of clusters is determined using the elbow method. The lifetime maximization problem is formulated as a joint power and resource block maximization problem, which is then solved using Lagrangian dual method. Finally, numerical simulations in MATLAB are performed to evaluate the performance of the proposed algorithm, and the results are compared to existing heuristic algorithm and inbuilt MATLAB optimal algorithm. The simulation results show that the proposed algorithm outperforms the heuristic algorithm and closely model the optimal algorithm with an acceptable level of complexity. The proposed algorithm offers significant improvements in the energy efficiency and network lifetime, as well as a faster convergence and lower computational complexity. [ABSTRACT FROM AUTHOR]

Summary: In healthcare applications, gait plays a major role in identification of the normal or abnormal person in different situations. Human gait refers to the walking style of the person, and it may also refer as locomotion using human limbs. The abnormal gait has irregular patterns of stance and swing phases. Without any clinical impairment, this paper proposes a novel approach to classify the person as normal or the person as suffering from neurological disorders from the videos using their gait videos. In addition, neurological gait disorders such as Parkinson gait, hemiplegic gait, and neuropathic gait has been identified using the gait features. Many systems are designed to detect and identify gait disorders using head, hip, heel, and toe behavior analysis from the bidirectional gait videos. As motivated by previous mechanisms, this paper proposes a novel vision based algorithm to recognize the gait abnormalities using model free approaches and significant feature vector generation from complete silhouette images of one gait cycle of a person. Here, a lean angle and ramp angle are considered as distinguishing and prominent features, and the results of these features are properly classified into normal or abnormal gait through the design of an unsupervised classifier. [ABSTRACT FROM AUTHOR]

Summary: There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks. [ABSTRACT FROM AUTHOR]

Summary: Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme. [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]