Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance

<span>Vehicular ad hoc network (VANET) is an emerging technology which can be very helpful for providing safety and security as well as for intelligent transportation services. But due to wireless communication of vehicles and high mobility it has certain security issues which cost the safety and security of people on the road. One of the major security concerns is the Sybil attack in which the attacker creates dummy identities to gain high influence in the network that causes delay in some services and fake voting in the network to misguide others. The early detection of this attack can prevent people from being misguided by the attacker and save them from getting into any kind of trap. In this research paper, Sybil attack is detected by first applying the Poisson distribution algorithm to predict the traffic on the road and in the second approach, analysis of the network performance for packet delivery ratio (PDR) is performed in malign and benign environment. The simulation result shows that PDR decreases in presence of fake vehicles in the network. Our approach is simple and effective as it does not require high computational overhead and also does not violate the privacy issues of people in the network.</span>

Performance of symmetric and asymmetric links in wireless networks

<p>Wireless networks are designed to provide the enabling infrastructure for emerging technological advancements. The main characteristics of wireless networks are: Mobility, power constraints, high packet loss, and lower bandwidth. Nodes’ mobility is a crucial consideration for wireless networks, as nodes are moving all the time, and this may result in loss of connectivity in the network. The goal of this work is to explore the effect of replacing the generally held assumption of symmetric radii for wireless networks with asymmetric radii. This replacement may have a direct impact on the connectivity, throughput, and collision avoidance mechanism of mobile networks. The proposed replacement may also impact other mobile protocol’s functionality. In this work, we are mainly concerned with building and maintaining fully connected wireless network with the asymmetric assumption. For this extent, we propose to study the effect of the asymmetric links assumption on the network performance using extensive simulation experiments. Extensive simulation experiments were performed to measure the impact of these parameters. Finally, a resource allocation scheme for wireless networks is proposed for the dual rate scenario. The performance of the proposed framework is evaluated using simulation.</p>

Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

With the fast-developing deep learning models in the field of autonomous driving, the research on the uncertainty estimation of deep learning models has also prevailed. Herein, a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation. Semantic segmentation is one of the most important perception problems in understanding visual scene, which is critical for autonomous driving. This study to optimize Bayesian SegNet for uncertainty evaluation. This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet. mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset. The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet, shortens the sampling time, and improves the network performance.

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.

A Robust Dirichlet Reputation and Trust Evaluation of Nodes in Mobile Ad Hoc Networks

The distributed nature of mobile ad hoc networks (MANETs) presents security challenges and vulnerabilities which sometimes lead to several forms of attacks. To improve the security in MANETs, reputation and trust management systems (RTMS) have been developed to mitigate some attacks and threats arising from abnormal behaviours of nodes in networks. Generally, most reputation and trust systems in MANETs focus mainly on penalising uncooperative network nodes. It is a known fact that nodes in MANETs have limited energy resources and as such, the continuous collaboration of cooperative nodes will lead to energy exhaustion. This paper develops and evaluates a robust Dirichlet reputation and trust management system which measures and models the reputation and trust of nodes in the network, and it incorporates candour into the mode of operations of the RTMS without undermining network security. The proposed RTMS employs Dirichlet probability distribution in modelling the individual reputation of nodes and the trust of each node is computed based on the node’s actual network performance and the accuracy of the second-hand reputations it gives about other nodes. The paper also presents a novel candour two-dimensional trustworthiness evaluation technique that categorises the behaviours of nodes based on their evaluated total reputation and trust values. The evaluation and analyses of some of the simulated behaviours of nodes in the deployed MANETs show that the candour two-dimensional trustworthiness evaluation technique is an effective technique that encourages and caters to nodes that continuously contribute to the network despite the reduction in their energy levels.

Measuring and Assessing Performance of Mobile Broadband Networks and Future 5G Trends

Mobile broadband (MBB) is one of the critical goals in fifth-generation (5G) networks due to rising data demand. MBB provides very high-speed internet access with seamless connections. Existing MBB, including third-generation (3G) and fourth-generation (4G) networks, also requires monitoring to ensure good network performance. Thus, performing analysis of existing MBB assists mobile network operators (MNOs) in further improving their MBB networks’ capabilities to meet user satisfaction. In this paper, we analyzed and evaluated the multidimensional performance of existing MBB in Oman. Drive test measurements were carried out in four urban and suburban cities: Muscat, Ibra, Sur and Bahla. This study aimed to analyze and understand the MBB performance, but it did not benchmark the performance of MNOs. The data measurements were collected through drive tests from two MNOs supporting 3G and 4G technologies: Omantel and Ooredoo. Several performance metrics were measured during the drive tests, such as signal quality, throughput (downlink and unlink), ping and handover. The measurement results demonstrate that 4G technologies were the dominant networks in most of the tested cities during the drive test. The average downlink and uplink data rates were 18 Mbps and 13 Mbps, respectively, whereas the average ping and pong loss were 53 ms and 0.9, respectively, for all MNOs.

Predictive Neuronal Adaptation as a Basis for Consciousness

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more “educated” response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

SplitTrie: A Fast Update Packet Classification Algorithm with Trie Splitting

Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.

Analyzing optical TDMA to mitigate interference in downlink LiFi optical attocell networks

Co-channel interference in the downlink of LiFi attocell networks significantly decreases the network performance in terms of rate. Analysis of multiple access schemes is essential to mitigate interference and improve rate. The light-emitting diodes (LEDs) being centrally monitored, the time division multiple access (TDMA) scheme over the LEDs will be suitable to analyze. This work considers the interference characterization in Ref. (Surampudi A, Ganti RK. Interference characterization in downlink Li-Fi optical attocell networks. J Lightwave Technol 2018;36:3211–28) over M-PAM modulated signals to derive an exact expression for the goodput G of the time scheduled attocell network, which is arranged as a deterministic square lattice in two dimensions. Given this TDMA over the LEDs, numerical simulations show that the LEDs can be optimally time scheduled to maximize the goodput, which implies that the TDMA mitigates interference in an attocell network compared to the case when the LEDs are unscheduled.

Rethinking Pooling Operation for Liver and Liver-Tumor Segmentations

Deep convolutional neural networks (DCNNs) have been widely used in medical image segmentation due to their excellent feature learning ability. In these DCNNs, the pooling operation is usually used for image down-sampling, which can gradually reduce the image resolution and thus expands the receptive field of convolution kernel. Although the pooling operation has the above advantages, it inevitably causes information loss during the down-sampling of the pooling process. This paper proposes an effective weighted pooling operation to address the problem of information loss. First, we set up a pooling window with learnable parameters, and then update these parameters during the training process. Secondly, we use weighted pooling to improve the full-scale skip connection and enhance the multi-scale feature fusion. We evaluated weighted pooling on two public benchmark datasets, the LiTS2017 and the CHAOS. The experimental results show that the proposed weighted pooling operation effectively improve network performance and improve the accuracy of liver and liver-tumor segmentation.