Multiple Edges Research Articles

Federated Recognition Architecture Based on Voting and Feedback Mechanisms for Accuracy Object Classification in Distributed Edge Intelligence Environment

Deep learning has recently become a crucial tool to solve many complex problems and has the potential to revolutionize industries. With the widespread adoption of the Internet of Things, there are now many devices with limited computational resources that are capable of running deep learning models. It has opened up new opportunities to implement deep learning in the edge environment so that decisions can be made locally without sending data to a cloud server for processing. However, because of limited resources, the model’s performance and communication overhead are challenges when deploying learning models to edge devices. The focus of this paper is to investigate and propose a federated recognition architecture for object classification in a distributed edge intelligence environment. Specifically, we build an edge server that includes a voting module and a feedback module to improve the overall accuracy of object classification. The voting module aggregates predictions of multiple edge devices whereas the feedback module sends the voting results to edge devices to adjust the local deep learning model. We build edge devices based on the EdgeX platform which makes it easy to manage data and optimize communication overheads. Because the edge server and edge nodes only exchange prediction results, our proposed architecture ensures security with sensitive data as well as deep learning model architecture. By testing on the image dataset, we evaluate the proposed architecture’s performance and show that it outperforms individual local models in terms of accuracy. Furthermore, our experiments demonstrate that with the feedback mechanism, the deep learning model is constantly updated with new data to maintain accuracy and avoid being outdated. Besides, we prove the real-time processing speed by collecting the delay time of the proposed model. The results show that our proposed architecture has the potential to be deployed in practical applications such as smart cities, and surveillance systems.

An energy-saving joint resource allocation approach for mobile edge computing based on NOMA

Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption.

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

Transient analysis of a cracked functionally graded magneto‐electro‐elastic rectangular finite plane under anti‐plane mechanical and in‐plane electric and magnetic impacts

AbstractThe transient anti‐plane problem of a functionally graded magneto‐electro‐elastic (FGMEE) rectangular plane weakened by several embedded and edge cracks under various boundary conditions is solved. First, the solutions to the dynamic magneto‐electro‐elastic dislocation in the finite rectangular domain are derived by employing Laplace and finite Fourier cosine transforms. The solutions can be utilized to construct the singular integral equations in Laplace transform domain for FGMEE rectangular plane with multiple embedded and edge cracks. The integral equations arising from dislocation solutions have Cauchy‐type singularities with unknown dislocation density functions. These unknown functions may be obtained by reducing the integral equations into a system of linear algebraic equations, hereby, the dynamic stress intensity factors (DSIFs) for several arbitrarily embedded and edge cracks are obtained. The overshoots of dynamic stress intensity factors are shown to be intensely affected by the rectangular plane dimensions, length and position of the cracks, loading parameter, and negative or positive gradient index.

A robust high-resolution potential-field edge detection filter with its application in the Indo-China Peninsula

Potential-field edge detection techniques are effective tools for identification of large-scale tectonic boundaries and geologic target bodies. Although a series of edge detectors have been developed, most of them suffer from a trade-off between edge resolution and noise immunity. To this end, we integrate multiple edge detectors such as normalized Harris filter (NHF) and ILTHG filter to propose a novel edge detector called ILGNH. The ILGNH edge detector takes advantage of both the enhanced noise immunity of NHF and the high resolution of ILTHG. Model experiments without/with noises both show that it is an alternative robust high-resolution edge detector. Afterwards, we apply this edge detection method to the Indo-China Peninsula and its adjacent areas to identify multi-phase tectonic boundaries. Combining the edge detection, residual crustal gravity anomalies (RCGA), and active faults, we identify four major NS transverse suture zones associated with microplates. These suture/boundary zones, together with deep faults, enclose 14 microplates with different interior feature of RCGA, while most of the boundaries are located in the gradient zones or the dislocation/distortion zones of the RCGA. Notably, inside the West Burma microplate, the ILGNH edge detection shows a distinct near NS-oriented arc-shaped deep boundary zone, which indicates that the underlying slab beneath the Indo-Burma Ranges was blocked during its eastward advance and shifted downward to form a high-angle deep subduction. Further, multi-scale edge detection at different depths confirms that the microplate boundary under the western Indo-China Peninsula vertically extends deeper than the eastern part. In addition, the seismicity below 50 km is concentrated to the west of the identified arc-shaped deep boundary zone and near the Naga-Kalaymyo-Arakan-Andaman Suture Zone.

An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation. This paper proposes a Blockchain-based secure aggregation strategy for FL. Blockchain is implemented as a channel of communication between the central server and edge devices. It provides a mechanism of masking device local data for secure aggregation to prevent compromise and reconstruction of the training data by a malicious server. It enhances the scalability of the system, eliminates the threat of a single point of failure of the central server, reduces vulnerability in the system, ensures security, and transparent communication. Furthermore, our framework utilizes a fault-tolerant server to assist in handling dropouts and stragglers which can occur in federated environments. To reduce the training time, we synchronously implemented a callback or end-process mechanism once sufficient post-trained models have been returned for aggregation (threshold accuracy achieved). This mechanism resynchronizes clients with a stale and outdated model, minimizes the wastage of resources, and increases the rate of convergence of the global model.

A Chandra Study of the NGC 7618/UGC 12491 Major Group Merger at Apogee: Multiple Cold Fronts, Boxy Wings, Filaments, and Arc-shaped Slingshot Tails

Analyses of major group mergers are key to understanding the evolution of large-scale structure in the Universe and the microphysical properties of the hot gas in these systems. We present imaging and spectral analyses of deep Chandra observations of hot gas structures formed in the major merger of the NGC 7618 and UGC 12491 galaxy groups and compare the observed hot gas morphology, temperatures, and abundances with recent simulations. The morphology of the observed multiple cold front edges and boxy wings are consistent with those expected to be formed by Kelvin–Helmholtz instabilities and gas sloshing in inviscid gas. The arc-shaped slingshot tail morphologies seen in each galaxy suggest that the dominant galaxies are near their orbital apogee after having experienced at least one core passage at a large impact parameter.

Location Privacy-Aware and Energy-Efficient Offloading for Distributed Edge Computing

Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.

Cloud native Lightweight Slice Orchestration (CLiSO) framework

Cloud-native network functions are becoming promising for 5G and beyond networks. They provide the much needed agility and flexibility that was missing from virtual machines. Though, this paradigm shift of using cloud-native application design principles, containerization, microservices, high resilience, and on-demand scaling has created challenges for legacy orchestration systems. They were designed for handling virtual machine-based network functions. Indeed, network slice orchestration requires interaction with multiple technological domain orchestrators, access, transport, core network, and edge computing. The specifications and existing orchestrators are made on top of the legacy virtual machine based network function orchestration. Hence, this limitation constrains their approach to managing a cloud-native network function. To overcome their challenges, we propose a novel Cloud-native Lightweight Slice Orchestration (CLiSO) framework extending our previously proposed Lightweight edge Slice Orchestration (LeSO) framework. In addition, we present a technology-agnostic and deployment-oriented network slice template. To allow zero-touch management of network slices, our framework provides a concept of Domain Specific Handlers. The framework has been thoroughly evaluated via orchestrating OpenAirInterface container network functions on public and private cloud platforms.

A matching-minor monotone parameter for bipartite graphs

Given a bipartite graph G with bipartition (U,W), we denote by Q(G) the set of all real U×W matrices B=[bu,w] with bu,w=0 if u and w are non-adjacent, bu,w≠0 if u and w are connected by a single edge, and bu,w∈R if u and w are connected by multiple edges. We denote by N(G) the set of all U×W matrices X=[xu,w] with xu,w=0 if u and w are adjacent. We say that a matrix B∈Q(G) has the Asymmetric Strong Arnold Property (ASAP) if for all X∈N(G), if XTB=0 and BXT=0, then X=0.If G is a bipartite graph for which there exists a matrix B∈Q(G) that has the Asymmetric Strong Arnold Property, we define the stable minimum bipartite rank mbrS(G) as the smallest rank of any matrix B∈Q(G) having the ASAP. We show that if H is a matching minor of G, then mbrS(G)≤mbrS(H)+1/2(|V(G)|−|V(H)|). If G has a bipartition with parts of the same size, we define the stable maximum bipartite nullity MbS(G) as the largest nullity of any matrix B∈Q(G) having the ASAP. Then MbS(H)≤MbS(G). We give a characterization in terms of forbidden matching minors of the classes of graphs G with MbS(G)=0 and with MbS(G)≤1.

Task offloading to edge cloud balancing utility and cost for energy harvesting Internet of Things

Task offloading from many Internet of Things (IoT) devices to a mobile edge computing (MEC) system, consisting of multiple collaborative edge servers (CES), results in reduced task execution delay as well as energy consumption. However, the exploitation of computation resources of CESs incurs additional costs. Existing works in the literature either focused on minimizing execution latency or energy consumption. In this paper, we have developed a task offloading policy that aims at making a trade-off between device utility and execution cost. The utility is defined as a function of task execution latency and energy consumption of energy-harvesting IoT devices. The task offloading problem is formulated as a subset selection problem that makes the desired trade-off. The offloading problem is proven to be NP-hard and thus we develop a meta-heuristic approach, namely SCOPE, inspired by Social Cognitive Optimization (SCO) to obtain the desired polynomial time execution. The results show its potency compared to the state-of-the-art methods in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.

Scheduling independent tasks on multiple cloud-assisted edge servers with energy constraint

In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first paper in the literature which optimizes the makespan of task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers.

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks.

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

Device Access, Subchannel Division, and Transmission Power Allocation for NOMA-Enabled IoT Systems

In the era of the Internet of Things (IoT), it is a promising way to improve system energy utility and better meet users’ requirements for quality of service (QoS) via integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) technologies. In light of this idea, we investigate device access, sub-channel division, and transmission power allocation for NOMA-enabled IoT systems. To maximize the energy utility of IoT systems while satisfying the minimum demands of IoT devices (IoTDs) on achievable uplink data rate, a joint optimization problem is formulated with the consideration of device access, sub-channel division, and transmission power allocation. Due to the non-convexity of this problem, we propose an alternating optimization algorithm aiming to find the optimal solution. The proposed algorithm first decomposes the joint optimization problem into three sub-problems through the block coordinate descent (BCD), and then obtains the near-optimal solution by solving the decomposed sub-problems alternately. Extensive simulations validate our analysis for the convergence of the proposed algorithm. The numerical results demonstrate that the proposed algorithm significantly outperforms the benchmark algorithms in terms of improving system energy utility.

Spatiotemporal graph neural networks for predicting mid-to-long-term PM2.5 concentrations

Predicting the concentration of PM2.5 particles is of critical importance in public health management because their small size enables them to penetrate deep into the lungs and even enter the bloodstream. However, achieving accurate predictions is challenging due to the complexity of the transport and dispersion processes involved, which are influenced by multiple factors, including atmospheric pollutants, meteorological conditions, and geographic features. To address this challenge, we developed a novel framework for PM2.5 prediction that utilizes multiple edges for feature extraction and employs a multi-gated graph neural network for feature calculations. The model utilizes multiple edges, created by combining an atmospheric diffusion coefficient with a PM2.5 similarity metric, to elucidate the complex characteristics of PM2.5 interactions between different monitoring stations. Node features are computed by aggregating connected nodes' features, weighted by these multi-faceted edges. The experimental results demonstrate that the proposed method outperforms conventional time-series prediction models in the mid-to-long-term prediction of PM2.5 concentrations (96 h in advance), with improvements in Root Mean Square Error (RMSE) by 2.613%, Critical Success Index (CSI) by 3.143%, and R-squared (R2) by 5.263%. Using the proposed model, the learning of global features can be achieved and issues related to local dependency can be mitigated.

The algorithms for the Eulerian cycle and Eulerian trail problems for a multiple graph

In this paper, we study undirected multiple graphs of any natural multiplicity $k>1$. There are edges of three types: ordinary edges, multiple edges and multi-edges. Each edge of the last two types is a union of $k$ linked edges, which connect 2 or $(k+1)$ vertices, correspondingly. The linked edges should be used simultaneously. If a vertex is incident to a multiple edge, it can be also incident to other multiple edges and it can be the common end of $k$ linked edges of some multi-edge. If a vertex is the common end of some multi-edge, it cannot be the common end of another multi-edge. We set the problem of finding the eulerian walk (the cycle or the trail) in a multiple graph, which generalizes the classical problem for an ordinary graph. We formulate the necessary conditions for existence of an eulerian walk in a multiple graph and show that these conditions are not sufficient. Besides that, we show that the necessary conditions of existence of an eulerian cycle and eulerian trail are not mutually exclusive for an arbitrary multiple graph, that is why it is possible to construct a multiple graph where two types of eulerian walks exist simultaneously. Any multiple graph can be juxtaposed to the ordinary graph with quasi-vertices, which represents the structure of the initial graph in a simpler form. In particular, each eulerian walk in the multiple graph corresponds to the eulerian walk in the graph with quasi-vertices. The algorithm for getting such a graph is formulated. Also, the auxiliary problem of finding the covering trails with given endpoints in an ordinary graph is studied. Two algorithms are obtained for this problem. We elaborate the algorithm for finding the eulerian walk in a multiple graph, which has the exponential complexity. We suggest the polynomial algorithm for the special case of a multiple graph and show that the necessary conditions are sufficient for existence of an eulerian walk in this special case.

On the Aeroelasticity of a Cantilever Wing Equipped with the Spanwise Morphing Trailing Edge Concept

This paper studies the aeroelastic behavior of a rectangular, cantilever wing equipped with the spanwise morphing trailing edge (SMTE) concept. The SMTE consists of multiple trailing edge flaps that allow controlling the spanwise camber distribution of a wing. The flaps are attached at the wing’s trailing edge using torsional springs. The Rayleigh–Ritz method is used to develop the equations of motion of the wing-flap system. The use of shape functions allows for representing the wing as an equivalent 2D airfoil with generalized coordinates that are defined at the wingtip. Strip theory, based on Theodorsen’s unsteady aerodynamic model, is used to compute the aerodynamic loads acting on the wing. A representative Padé approximation for Theodorsen’s function is utilized to model the aerodynamic behaviors in a state-space form allowing time-domain simulation and analysis. The model is validated using a rectangular cantilever wing and the data are available in the literature. A comprehensive parametric comparison study is conducted to assess the impact of flap stiffness on the aeroelastic boundary. In addition, the potential of the SMTE to provide load alleviation and flutter suppression is assessed for a wide range of flight conditions, using a discrete (1-cosine) gust. Finally, the implementation and validation of a controller for a wing with SMTE for gust load alleviation are studied and controller parameters are tuned for a specific gust model.

On Partition Dimension of Generalized Convex Polytopes

Let G be a graph having no loop or multiple edges, k − order vertex partition for G is represented by γ = γ 1 , γ 2 , … , γ k . The vector r ϕ γ = d ϕ , γ 1 , d ϕ , γ 2 , d ϕ , γ 3 ⋯ , d ϕ , γ k is the representation of vertex ϕ with respect to γ . If the representation of all the vertices with respect to γ is different, then γ is said to be resolving partition for the graph G . The minimum number k is resolving partition for G and is termed as partition dimension for G , represented by p d G . There are numerous applications of partition dimension in different fields such as optimization, computer, mastermind games, and networking and also in modeling of numerical structure. The problem of finding constant value of partition dimension for a graph or network is very hard, so one can find bounds for the partition dimension. In this work, we consider convex polytopes in their generalized forms that are E n , S n , and G n , and we compute upper bounds for the partition dimension of the desired polytopes.

Theory of nonlinear corner states in photonic fractal lattices

AbstractWe study linear and nonlinear higher-order topological insulators (HOTIs) based on waveguide arrays arranged into Sierpiński gasket and Sierpiński carpet structures, both of which have non-integer effective Hausdorff dimensionality. Such fractal structures possess different discrete rotational symmetries, but both lack transverse periodicity. Their characteristic feature is the existence of multiple internal edges and corners in their optical potential landscape, and the formal absence of an insulating bulk. Nevertheless, we show that a systematic geometric shift of the waveguides in the first generation of such fractal arrays, which affects the coupling strengths between sites of this building block as well as in subsequent structure generations, enables the formation of corner states of topological origin at the outer corners of the array. We find that, in contrast to HOTIs based on periodic arrays, Sierpiński gasket arrays always support topological corner states, irrespective of the direction of the shift of the waveguides, while in Sierpiński carpet structures, corner states emerge only for one direction of the waveguide shift. We also find families of corner solitons bifurcating from linear corner states of fractal structures that remain stable practically in the entire gap in which they form. These corner states can be efficiently excited by injecting Gaussian beams into the outer corner sites of the fractal arrays. Our results pave the way toward the investigation of nonlinear effects in topological insulators with non-integer dimensionality and enrich the variety of higher-order topological states.

Square Distance in Graphs

In this paper, we consider a simple connected graph having no loops and multiple edges. The order and size of are denoted by and respectively in graphs is a wide branch of graph theory having many scientific and real-life applications. There are various types of distances studied in the literature.The distance is the length of the shortest path between the vertices and in [2]. The detour distance is the length of the longest path between the vertices and in [3]. The concept of centrality widely applied in social network analysis. Degree centrality is the simplest centrality measure to compute centre. Betweenness centrality measures used for analyzed cancer network.