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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: In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc networks (VANETs) have attracted significant attention due to their safety, traffic management, and infotainment applications. However, vehicular communication suffers from network congestion caused by high vehicle density, which raises numerous challenges for efficiently disseminating emergency messages in vehicular safety applications. This paper aims to solve the problem of capacity optimization of the control channel (CCH) during the delivery of emergency messages. Hence, we propose a novel channel capacity optimization scheme for safety application (CCOSSA). The performance of the CCOSSA scheme is evaluated by performing comprehensive traffic and network simulations regarding various parameters such as the probability of reception, channel busy time (CBT) ratio, and average delay. CCOSSA scheme is compared with the state‐of‐the‐art schemes under varying vehicle density and distance from the source in which the proposed scheme shows an improvement in the performance. [ABSTRACT FROM AUTHOR]

Summary: As Massive multiple‐input multiple‐output (Ma‐MIMO) is one of the critical 5G technologies, security aspects of this technology in terms of powerful distributive jamming attacks for improving the total system performance are so important. In this paper, we evaluate the sum spectral efficiency (SE) of Ma‐MIMO systems in the multicell scenario concerning jamming effects. First, the closed‐form expression for sum SE using two detectors maximum ratio combining (MRC) and zero‐forcing (ZF) in the presence of a jammer was obtained. Next, the effects of jamming power, the number of base station antennas, the signal‐to‐noise ratio (SNR), and coherence block lengths on the system performance were obtained. Finally, to improve the signal‐to‐noise and interference ratio (SINR) of cell edge users, the problem of optimizing the fairness trade‐off between the central legitimate users and the cell edge legitimate users is modeled by using log barrier functions. We propose an alternating algorithm to solve the non‐convex problem. Numerical results showed that by considering the same interference power and the power of the legitimate users, the sum SE improved with proposed schemes and also by increasing the number of antennas at BS. Also, the ZF outperforms MRC with increasing the number of antennas. Finally, it is shown that by using the optimal legitimate users instead of overall legitimate users, the system performance in terms of fairness sum SE trade‐off improved. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, there is an emerging need for applications based on the Internet of Things (IoT). The sensor nodes present in the IoT network produce data constantly, which directly influences the durability of the network. Therefore, two major challenges while designing IoT systems are network lifetime and energy consumption. Although the ability of IoT applications is huge, there are several limitations such as energy optimization, heterogeneity of devices storage, load balancing, privacy, and security that have to be addressed. These constraints have to be optimized for improving the efficiency of the networks. Hence, the main intention of this paper is to develop the intelligent‐based cluster head selection model for accomplishing green communication in IoT. The two famous algorithms like spotted hyena optimization (SHO) and sun flower optimization (SFO) are integrated to form sun flower‐spotted hyena optimization (SF‐SHO) by utilizing the hybrid meta‐heuristic concept for the optimal cluster head selection. The most significant parameters in IoT networks like delay, distance, energy, temperature, and load are considered for deriving a multi‐objective function to offer optimal clustering. The cluster head of the model is optimally tuned based on the hybrid SF‐SHO, to solve the multi‐objective problem, thus showing the enhanced green communication performance. The proposed model is analyzed and evaluated over different approaches in terms of energy‐specific factors, and the attained results confirm the efficiency of the developed method. [ABSTRACT FROM AUTHOR]

Summary: Congestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter‐vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic‐centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter‐vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi‐direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even‐driven messages. The obtained simulation results through OMNET++ in both single and multi‐direction scenarios show the efficiency of FDCC in terms of the end‐to‐end delay, network throughput, and packet delivery ratio for event‐driven messages as well as essential CAMs. [ABSTRACT FROM AUTHOR]

Summary: In multi‐user multiple input multiple output (MU‐MIMO) uplink, the bit error rate (BER) of linear detection schemes, namely, zero forcing (ZF) and minimum mean square error (MMSE) under correlated Rayleigh fading channel deteriorates with increase in spatial correlation and load factor due to the increase in the condition number of the channel matrix. Moreover, the performance gap between ZF and MMSE increases with increase in the antenna number for large load factors. To address this issue, in this paper, truncated singular value decomposition (TSVD), which is a popular method for solving ill‐posed problem, is used in the ZF detection process through proper selection of hard threshold. However, the pseudo‐inverse computed using TSVD is an approximate inverse, which results in interference at the output of the detection process. Therefore, a mathematical expression for the post‐processing signal‐to‐interference‐plus‐noise ratio (SINR) of the substream is derived and subsequently BER is evaluated. Further, the analytical BER is analyzed with respect to the parameters namely, average signal‐to‐noise ratio (SNR) (EsN0), correlation coefficient (ρU,ρB), load factor (β), and number of singular values considered to compute the hard threshold (n) and also validated by Monte Carlo trails. From the simulation results, it is observed that for higher order MU‐MIMO systems and at lower values of EsN0, the performance of TSVD‐based scheme is comparable with that of MMSE by decreasing the truncation parameter (k). Moreover, as the complexity of TSVD‐based ZF using randomized schemes is O(NU2NB+NU2log(k)+NUk2), therefore, the complexity of detection significantly decreases with decrease in k. [ABSTRACT FROM AUTHOR]

Summary: Although software‐defined networking (SDN) provides a flexible way to provision and control networks, it also makes network debugging and troubleshooting more complex. In SDN, the network is fully managed by software programs that increase flexibility and sophistication but are prone to bugs. Pinpointing those bugs is challenging because they can occur at multiple locations, such as the forwarding plane, the controller OS, and the network services running on top of the controller. Compared to functional bugs, performance bugs are particularly irritating due to their non‐failure semantics. They can lead to performance loss (reduced throughput, increased latency, and wasted resources) while maintaining the network connectivity. The literature reports several tools and techniques to diagnose SDN bugs, but, unfortunately, they are mostly ineffective against performance bugs. In this paper, we propose a novel monitoring and diagnostic framework capable of diagnosing performance bugs in the SDN data plane. The proposed tool works within Open vSwitch (OVS), a popular software switch, albeit it can easily be adapted to any OpenFlow switch. Tracing techniques are used to collect low‐level performance data from monitored switches. Our tool derives adapted performance metrics from kernel and userspace traces, and then displays them in time‐synchronized graphical views. These views provide valuable insights into OVS operation. They also enable practitioners to discover performance‐related issues and analyze their root causes. A few use cases are presented to demonstrate the efficiency of our tool in optimizing OVS performance and diagnosing its performance bugs. [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: The digitization of objects has given birth to the concept of the Internet of Things (IoT) which is revolutionizing traditional objects by replacing them with intelligent objects such as smart vehicles, smartphones, smart home...etc. In this context, and with the emergence of vehicle networks, is born the need to increase the vehicular resources in order to benefit from several applications facilitating driving and ensuring the drivers safety, even going to think of the automated driving. The use of cloud computing has become the key solution to the lack of resources required to run compute‐intensive applications and the lack of storage space to back up all data related to roads and applications. In addition, we are witnessing the birth of the fog computing paradigm, which brings the functionalities of cloud computing at the edge of the network, thereby solving the latency problem for some time‐sensitive applications and also saving the bandwidth of the network because vehicle requests will not need to cross the entire network to be processed at the cloud level. In this paper, we discuss the different principles of cloud/fog computing and compare the two paradigms. We present a classification of data dissemination schemes in vehicular ad hoc networks (VANETs), vehicular cloud, and vehicular fog computing with their different architectures proposed. We finally present several cloud/fog computing applications. [ABSTRACT FROM AUTHOR]