Edge-based Approach Research Articles

Distributed Byzantine-Resilient Observer for High-Order Integrator Multiagent Systems on Directed Graphs: An Edge-Based Approach

Distributed Byzantine-Resilient Observer for High-Order Integrator Multiagent Systems on Directed Graphs: An Edge-Based Approach

Vehicular Edge-Based Approach for Optimizing Urban Data Privacy

Vehicular Edge-Based Approach for Optimizing Urban Data Privacy

Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations

Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to remote exploitation through their vulnerable software/cyber components. More importantly, compromised EVCS can be leveraged to perform coordinated oscillatory load attacks against the interconnected power grid, leading to power grid instability, increased operational costs, and power line tripping. In this paper, an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid is investigated. The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. The proposed detection approach was evaluated by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, the detection mechanism of oscillatory load attacks is decentralized and created an effective alternative for operator-centric mechanisms to mitigate multi-operator and Man-in-the-Middle (MitM) oscillatory load attacks against the power grid. Additionally, the created test bed is leveraged to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead. Finally, a security metric is createdto evaluate the resiliency of the ecosystem based on its design and architecture.

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

Graph clustering has received considerable attention recently, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources. This study proposes an approach to partitioning and overlapping clustering of undirected unweighted graphs, allowing users to specify the required density of resultant clusters. This required density is achieved by means of ‘Relative Density’. The proposed algorithm adopts an edge-based approach, commencing with the determination of the edge degree for each edge. The main clustering process is then initiated by an edge with an average degree. A cluster is expanded by considering adjacent edges that can be included while monitoring the relative density of the cluster. Eight empirical networks with diverse characteristics are used to validate the proposed algorithm for both partitioning and overlapping clustering. Their results are assessed using an appropriate metric known as the mean relative density deviation coefficient (MRDDC). This is the first work that attempts to carry out partitioning and overlapping graph clustering, which allows user-specified density.

Examining the relationship between contested edges and intergroup crime dynamics

The measurement of spatial patterns across urban areas is paramount in answering questions related to the dynamics of social phenomenon. Scholars have measured the layout of cities, land use, infrastructure, and other features of the built environment that may correlate with a number of social processes. Recently, several studies have posited the idea of a contested boundary as a way to measure where and to what extent intergroup conflict may occur. Contested boundaries are conceptualized as interstitial zones that separate communities of contrasting ethno-racial composition – areas where there is no clear majority group that makes up the population. Previous studies have successfully demonstrated the utility of this approach yet have largely based their work on the analysis of areas, potentially masking the local heterogeneity of incident locations in relation to surrounding neighborhoods. Using the City of Cincinnati as a case study, we reconceptualize contested areas as contested edges and determine whether the edges separating racially distinct neighborhoods are associated with a higher rate of interracial crime. Our results lend support for the use of an edge-based approach to evaluate urban social patterns and highlight the importance of accounting for local spatial heterogeneity when assessing ethno-racial group interaction.

An edge-based approach for virtual network embedding based on the graph edit distance.

Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

Modeling Control Delays for Edge-Enabled UAVs in Cellular Networks

Real-time control solutions for unmanned aerial vehicles (UAVs) have attracted great interest in recent years. Most existing control methods use Wi-Fi technology. While Wi-Fi is inexpensive and easy to use, it has only a limited transmission range. Thus, 4G/5G cellular networks have been proposed as an alternative enabling technology. This study focuses on the problem of improving the appropriateness of the control commands sent by the ground control station (GCS) to the UAV over the control and nonpayload communication (CNPC) link of the UAV through the cellular network. To satisfy the low-latency requirement of the CNPC link, multiaccess edge computing (MEC) technology is leveraged to collocate the GCS and base station. The effectiveness of the proposed edge-based approach is demonstrated by conducting experiments on two LTE platforms with different MEC deployment methods. An edge-enabled UAV control solution is proposed in which each end-to-end control delay in the UAV-GCS system is estimated based on the preceding delay such that the location of the UAV at the moment it receives the control command from the GCS can be predicted in advance and taken into consideration by the GCS when formulating an appropriate control decision. To this end, an analytical modeling method is proposed for estimating the expected error range of each control delay based on a bimodal distribution approximation of the empirical control delays observed at the UAV. Finally, an event-driven simulator is developed to confirm the accuracy of the analytical predictions of the control delay based on the expected error between consecutive delays.

Edge-based network analysis reveals frequency-specific network dynamics in aberrant anxiogenic processing in rats.

Uncovering interactions between edges of brain networks can reveal the organizational principle of the networks and also their dysregulations underlying aberrant behaviours such as in neuropsychiatric diseases. In this study, we looked into the applicability of edge-based network analysis in uncovering possible network mechanisms of aberrant anxiogenic processing. Utilizing a rat model of prodromal Parkinson's disease we examined how a dorsomedial striatum-tied associative network (DSAN) may mediate context-based anxiogenic behaviour. Following dopamine depletion in the dorsomedial striatum, an exaggerated bottom-up signalling (posterior parietal-hippocampal-retrosplenial to anterior prefrontal-cingulate-amygdala regions) and gradient specific to the theta frequency in this network was observed. This change was accompanied by increased anxiety behaviour of the animals. By employing an edge-based approach in correlating informational flow (phase transfer entropy) with functional connectivity of all edges of this network, we further explore how the abnormal bottom-up signalling might be explained by alterations to the informational flow-connectivity motifs in the network. Our results demonstrate usage of edge-based network analysis in revealing concurrent informational processing and functional organization dynamics across multiple pathways in a brain network. This approach in unveiling network abnormalities and its impact on behavioural outcomes would be useful in probing the network basis of neuropsychiatric conditions.

A Sustainable Multi-Modal Multi-Layer Emotion-Aware Service at the Edge

Limited by the computational capabilities and battery energy of terminal devices and network bandwidth, emotion recognition tasks fail to achieve good interactive experience for users. The intolerable latency for users also seriously restricts the popularization of emotion recognition applications in the edge environments such as fatigue detection in auto-driving. The development of edge computing provides a more sustainable solution for this problem. Based on edge computing, this article proposes a multi-modal multi-layer emotion-aware service (MULTI-EASE) architecture that considers user’s facial expression and voice as a multi-modal data source of emotion recognition, and employs the intelligent terminal, edge server and cloud as multi-layer execution environment. By analyzing the average delay of each task and the average energy consumption at the mobile device, we formulate a delay-constrained energy minimization problem and perform a task scheduling policy between multiple layers to reduce the end-to-end delay and energy consumption by using an edge-based approach, further to improve the users’ emotion interactive experience and achieve energy saving in edge computing. Finally, a prototype system is also implemented to validate the architecture of MULTI-EASE, the experimental results show that MULTI-EASE is a sustainable and efficient platform for emotion analysis applications, and also provide a valuable reference for dynamic task scheduling under MULTI-EASE architecture.

Enhancement of light field disparity maps by reducing the silhouette effect and plane noise

During the last decade, there has been an increasing number of applications dealing with multidimensional visual information, either for 3D object representation or feature extraction purposes. In this context, recent advances in light field technology, have been driving research efforts in disparity estimation methods. Among the existing ones, those based on the structure tensor have emerged as very promising to estimate disparity maps from Epipolar Plane Images. However, this approach is known to have two intrinsic limitations: (i) silhouette enlargement and (ii) irregularity of surface normal maps as computed from the estimated disparity. To address these problems, this work proposes a new method for improving disparity maps obtained from the structure-tensor approach by enhancing the silhouette and reducing the noise of planar surfaces in light fields. An edge-based approach is initially used for silhouette improvement through refinement of the estimated disparity values around object edges. Then, a plane detection algorithm, based on a seed growth strategy, is used to estimate planar regions, which in turn are used to guide correction of erroneous disparity values detected in object boundaries. The proposed algorithm shows an average improvement of 98.3% in terms of median angle error for plane surfaces, when compared to regular structure-tensor-based methods, outperforming state-of-the-art methods. The proposed framework also presents very competitive results, in terms of mean square error between disparity maps and their ground truth, when compared with their counterparts.

An Edge Based Attack Detection Model (EBAD) for Increasing the Trustworthiness in IoT Enabled Smart City Environment

Several massive real-time services could be offered to the residents of smart cities by the incorporation of collaborative applications. All such applications require latency-aware network services for accomplishing various needs of the smart city environment. It requires technological enhancements to the existing mechanisms to serve better in smart environments. Such enhancements to the prevailing approaches also opened a wide range of chances to the intruders. Among such infringes, the identity-based attack is the most powerful attack, which may directly affect the credibility of legitimate network components. Such attackers aim to steal the identity of other legitimate entities. Thus, the prevailing trust-based approaches cannot withstand such attacks. The proposed Edge-based approach, EBAD has been designed for smart city environments, as a robust prevention mechanism for identity theft and misuse. EBAD is efficient enough to identify the Sybil attacker nodes and the early identification of such attacker nodes will nullify the probability of performing the Sybil attack over a Cooperative blackmailing attack (SA-CBA). EBAD uses an Edge-based accusation analysis approach to assess the malicious behavior of the network entities. The major part of the required computations has been placed at the edge node for reducing the computational overload of the end devices. Finally, the efficiency of EBAD has been examined under a malicious environment.

Ego-Motion Estimation With Stereo Cameras Using Efficient 3D–2D Edge Correspondences

This article presents a new edge-based ego-motion estimation method for stereo cameras using 3D–2D edge alignment. To filter out prominent edgelets from Canny edge extractor, a region growing algorithm is developed to remove cluttered edges out of raw edgelets. Edge matching between stereo image pairs is guided by sparse feature correspondence using optical flow. The disparity is then refined using an inverse compositional algorithm, which is far more computationally efficient than searching along the epipolar line. The relative camera motion is solved by 3D–2D edge alignment based on an iterative closest point (ICP) algorithm, minimizing the point-to-tangent distance. A gradient-orientation-based similarity measurement is proposed, and the optimal correspondence is searched between the 3-D edge point projected onto the current image and the extracted edge pixels in the current frame with 1-nearest neighbor. In addition, the inverse depth of edge points in the reference frame is optimized. Experiments show that the proposed method can provide centimeter-level relative pose error (RPE), which has a better performance than the point-based approach efficient perspective-n-point (EPnP) and the edge-based approach direct edge-alignment (DE-A).

LBRO: Load Balancing for Resource Optimization in Edge Computing

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

Skin Lesion Border Detection Based on Best Statistical Model Using Optimal Colour Channel

<p>This paper proposes an effective way to segment melanoma skin lesion in colour dermoscopic images, using an edge-based approach. The proposed method, different methods were combined to improve the segmentation performance. These methods are morphological operations, bilateral filter, spline, polynomial model and canny edge detector. Different methods were tested to select the best method that was produced the best outcome. These testing methods, bilateral filter provided the highest PSNR amongst other filters such as median filter, Gaussian and average filter. Two statistical models were implemented polynomial model and linear regression and selected the best performance as polynomial model. Four edge detectors were applied to detect the edge of skin lesion and select the best segmentation accuracy. �Manual border selection was used as the benchmark to evaluation the accuracy of the automatic border. The proposed method was able to achieve a good average accuracy of 96.69% based on canny edge detector. Our dataset consists of (70) dermoscopic images that includes melanoma and nevus.</p>

An Edge-Based Model of SEIR Epidemics on Static Random Networks.

Studies have been done using networks to represent the spread of infectious diseases in populations. For diseases with exposed individuals corresponding to a latent period, an SEIR model is formulated using an edge-based approach described by a probability generating function. The basic reproduction number is computed using the next generation matrix method and the final size of the epidemic is derived analytically. The SEIR model in this study is used to investigate the stochasticity of the SEIR dynamics. The stochastic simulations are performed applying continuous-time Gillespie's algorithm given Poisson and power law with exponential cut-off degree distributions. The resulting predictions of the SEIR model given the initial conditions match well with the stochastic simulations, validating the accuracy of the SEIR model. We varied the contribution of the disease parameters and the average degree of the network in order to investigate their effects on the spread of disease. We verified that the infection and the recovery rates show significant effects on the dynamics of the disease transmission. While the exposed rate delays the spread of the disease, increasing it towards infinity would lead to almost the same dynamics as that of an SIR case. A network with high average degree results to an early and higher peak of the epidemic compared to a network with low average degree. The results in this paper can be used as an alternative way of explaining the spread of disease and it provides implications on the control strategies applied to mitigate the disease transmission.

Effective Edge-Based Approach for Promoting the Spreading of Information

Real-world systems, ranging from social to infrastructural, can be abstracted into complex networks. Promoting the spreading of some typical information (for instance, the commercial message, vaccination guidance, innovation, and political movement) on these networked systems can bring benefits to all aspects of society. In this study, we propose an effective edge-based approach for promoting the spreading of information on complex networks. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the information spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way to promote the spreading of information by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading dynamics.

Modeling second-order boundary perception: A machine learning approach.

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

Challenging the Boundaries of Unsupervised Learning for Semantic Similarity

The semantic analysis field has a crucial role to play in the research related to text analytics. Calculating the semantic similarity between sentences is a long-standing problem in the area of natural language processing, and it differs significantly as the domain of operation differs. In this paper, we present a methodology that can be applied across multiple domains by incorporating corpora-based statistics into a standardized semantic similarity algorithm. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. When tested on both benchmark standards and mean human similarity dataset, the methodology achieves a high correlation value for both word ( $r=0.8753$ ) and sentence similarity ( $r=0.8793$ ) concerning Rubenstein and Goodenough standard and the SICK dataset ( $r=0.8324 $ 1 ) outperforming other unsupervised models. 1 Eliminating the outliers which constitutes to 3.75% of 4927 statement pairs

Drivable path detection based on image fusion for unmanned ground vehicles

Autonomous vehicles are used for a range of tasks, such as automated highway driving, transporting work, etc. These vehicles are used both in structured and unstructured environments. This work presents an effective method for path detection using statistical texture features extracted from fused LIDAR sensor and visual camera images. An edge-based feature detection approach is adopted for image registration. The Grey Level Co-occurrence Matrix (GLCM)-based texture features are extracted from the fused image. Classification performance of K-NN and Support Vector Machine (SVM) classifiers are analysed in this work. For experimentation, the data available in Ford Campus Vision data set are used. The results of this new approach are very promising for path detection problem of unmanned ground vehicles.

Social contagions on interconnected networks of heterogeneous populations.

Recently, the dynamics of social contagions ranging from the adoption of a new product to the diffusion of a rumor have attracted more and more attention from researchers. However, the combined effects of individual's heterogenous adoption behavior and the interconnected structure on the social contagions processes have yet to be understood deeply. In this paper, we study theoretically and numerically the social contagions with heterogeneous adoption threshold in interconnected networks. We first develop a generalized edge-based compartmental approach to predict the evolution of social contagion dynamics on interconnected networks. Both the theoretical predictions and numerical results show that the growth of the final recovered fraction with the intralayer propagation rate displays double transitions. When increasing the initial adopted proportion or the adopted threshold, the first transition remains continuous within different dynamic parameters, but the second transition gradually vanishes. When decreasing the interlayer propagation rate, the change in the double transitions mentioned above is also observed. The heterogeneity of degree distribution does not affect the type of first transition, but increasing the heterogeneity of degree distribution results in the type change of the second transition from discontinuous to continuous. The consistency between the theoretical predictions and numerical results confirms the validity of our proposed analytical approach.