Static Edge Research Articles

Constructing Socially-Inspired Networks Using the Static Edge Voting Model

Constructing Socially-Inspired Networks Using the Static Edge Voting Model

Exploring synchronizability of complex dynamical networks from edges perspective

With the rapid development of network information technology, synchronization problem of complex dynamical networks has garnered extensive attention. Current research predominantly concentrates on analyzing synchronizability and synchronization control in the complex dynamical networks with static edge weights or single weight attribute connections. However, there is limited research on networks characterized by variable weights and multiple weighted connections. This study addresses this gap by analyzing the synchronizability of such complex networks. In this paper, we investigate the synchronizability of two types of networks: weight-variable complex dynamical networks and multi-weighted complex dynamical networks. Considering the evolution of network weights, the synchronizability of complex dynamical network with weight-variable is investigated, the master stability equation, synchronizability criteria of the network are derived, and experiments are used to substantiate the validity of our findings. Our study then shifts to multi-weighted complex networks. Take the Rössler oscillators as node dynamics, we explore the impact of different inner coupling matrices on synchronization types under both unknown and known topologies. Moreover, we extend our study to scenarios with different coupling strengths. The analysis reveals that the inner coupling matrix influences the network’s synchronization region type. Interestingly, the clarity or ambiguity of network topologies does not markedly affect the synchronization region type. Furthermore, our analysis indicates a correlation between the synchronization region boundaries of multi-weighted complex networks and those of their single-weighted counterparts capable of synchronization.

Latency-Energy Tradeoff in Connected Autonomous Vehicles: A Deep Reinforcement Learning Scheme

Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing closer to user equipment (UEs) at the edge of the access network by delivering various services to the UEs with limited processing power and battery. However, in fifth-generation and beyond 5G (B5G) networks, where UEs’ service requests and locations change dynamically, the deployment of static edge server deployments may lead to an increase in latency and total energy consumption. This paper presents a latency-energy-aware, efficient task offloading scheme for connected autonomous vehicular networks. Firstly, vehicles are assembled into clusters, in which vehicle can transmit tasks to the other vehicle, while on the other hand, the VEC server is used for processing the data. We developed a joint resource allocation and offloading decision optimization problem to minimize network latency and total energy usage. Due to the non-convex character of the optimization issue, we employed the Markov decision process (MDP) to convert it to a reinforcement learning (RL) problem. Then, we used a soft-actor critic-based scheme to achieve the optimal policy for resource allocation and task offloading to reduce the total latency and energy consumption for connected autonomous vehicles. Simulation analysis reveals that the proposed scheme attains 46.6% and 17.2% lesser delay, and 28.8% and 20.0% consumes less energy than the Hybrid DRL with Genetic Algorithm (HDRL-GA) and DRL based collaborative Data Scheduling (DRL-CDSS) state-of-art schemes.

Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones

The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.

Assessment and modelling of corrugated board dynamic properties under impact loads. Application to edge crush disposition

Corrugated packaging models and theories are well-established under static load conditions, however, very few studies can be found in the literature dealing with the behaviour of corrugated boards under impact conditions, which is critical in e-commerce distribution. Packaging capacity of energy absorption and the acceleration peak provoked during the drop or shock event are the key factors to assure the impact does not cause damage to the products. This study describes cushioning properties of corrugated boards in edge crush disposition and proposes a predictive model, experimental methods, and tools to assess its dynamic properties. The proposed model is based on static edge crush test theory adapted to impact conditions through relations with static properties of paper. This novel model will allow the estimation of energy absorption for packaging solutions that rely on this deformation mode.

Mapping the geometry dependent relative stress in stretched polymer membranes by polarization and interferometric techniques

This paper reports experimental results on contactless geometry dependent stress characterisation and mapping in elastic polymer membranes using polarization and interferometric techniques. Three different geometries of polydimethylsiloxane (PDMS) namely, rectangular, wedge and bowtie shaped membranes are studied. The membranes were initially stretched and then relaxed from one edge during the characterisation. In the rectangular membrane the stress was observed to be concentrated at the static edge of the membrane. Whereas, in the wedge and bowtie shaped membranes, the stress is found to be concentrated at the locations wherein the membrane has a narrower width. The proposed facile methods for characterisation are helpful in designing stress induced tuneable optics and to analyze stress in polymer membranes during their industrial production.

A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing.

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary's exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy "Give up a Pawn to Save a Chariot". Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use.

Expanding edges of quantum Hall systems in a cosmology language: Hawking radiation from de Sitter horizon in edge modes

Expanding edge experiments are promising to open new physics windows of quantum Hall systems. In a static edge, the edge excitation, which is described by free fields decoupled with the bulk dynamics, is gapless, and the dynamics preserve conformal symmetry. When the edge expands, such properties need not be preserved. We formulate a quantum field theory in 1+1 dimensional curved spacetimes to analyze the edge dynamics. We propose methods to address the following questions using edge waveforms from the expanding region: Does the conformal symmetry survive? Is the nonlinear interaction of the edge excitations induced by edge expansion? Do the edge excitations interact with the bulk excitations? We additionally show that the expanding edges can be regarded as expanding universe simulators of two-dimensional dilaton-gravity models, including the Jackiw-Teitelboim gravity model. As an application, we point out that our theoretical setup might simulate emission of analog Hawking radiation with the Gibbons-Hawking temperature from the future de Sitter horizon formed in the expanding edge region.

The Roaming Edge and its Applications

Connected sensors, e.g., cameras, LiDARs, air sensors, installed in a mobile platform (e.g., a terrestrial or aerial vehicle, or special in-person devices) can provide broad views of wide-area environments quickly and efficiently. If many vehicles incorporate such sensing systems, they together can be composed into a unique crowd-sourced platform and can gather fine-grained, diverse, and noisy information at city-scales. However, these sensors can generate large amounts of data and such data is hard to aggregate in a central server. We explore the design of a "roaming edge" - the notion that generalpurpose computing be installed in these mobile platforms, which connect over wireless paths to the static infrastructure and to the static edge nodes, to support a broad range of applications. In particular, a roaming edge node allows different sensors and data sources in-range of a mobile platform to connect to it, and supports data processing for necessary local analytics, considering both efficiency and privacy. The roaming edge, of course, does not operate in isolation and we describe a three-tier architecture that integrates it with a static edge and cloudhosted services. This paper also outlines several applications that can leverage opportunities provided by the roaming edge, and focus, briefly, on one - a real-time video query application with public safety implications.

Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile Computation Offloading in Industrial Networks

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

DynNetSLAM: Dynamic Visual SLAM Network Offloading

Existing Visual Simultaneous Localization And Mapping (vSLAM) approaches that offload the complex self-localization computations from mobile robots over a wireless network to edge computing are limited to static offloading, i.e., the offloaded computation tasks are offloaded permanently. However, wireless networks are inherently dynamic and may excessively delay the transmissions between a mobile device and the edge during periods of poor wireless network quality, e.g., from fading or temporary obstructions. We propose and evaluate Dynamic Visual SLAM Network Offloading (DynNetSLAM) to dynamically adapt the vSLAM computation offloading according to the measured wireless network latency. As groundwork towards developing DynNetSLAM, we first enhance the existing state-of-the-art vSLAM approaches through judicious parameter settings and parallel map updates to enable the tracking of common fast vSLAM data sets. We introduce an offloading latency threshold along with a safe zone and a hysteresis around the threshold to control the dynamic offloading. Our extensive evaluations with public vSLAM data sets indicate that DynNetSLAM with the hysteresis substantially reduces the probability of track loss events compared to the state-of-the-art ORB-SLAM2 approach for processing statically on the mobile device and the enhanced static Edge SLAM. Also, DynNetSLAM nearly attains the low absolute position error and only slightly increases the CPU utilization compared to the enhanced static Edge SLAM.

Phase-Field Modeling of Chemoelastic Binodal/Spinodal Relations and Solute Segregation to Defects in Binary Alloys.

Microscopic phase-field chemomechanics (MPFCM) is employed in the current work to model solute segregation, dislocation-solute interaction, spinodal decomposition, and precipitate formation, at straight dislocations and configurations of these in a model binary solid alloy. In particular, (i) a single static edge dipole, (ii) arrays of static dipoles forming low-angle tilt (edge) and twist (screw) grain boundaries, as well as at (iii) a moving (gliding) edge dipole, are considered. In the first part of the work, MPFCM is formulated for such an alloy. Central here is the MPFCM model for the alloy free energy, which includes chemical, dislocation, and lattice (elastic), contributions. The solute concentration-dependence of the latter due to solute lattice misfit results in a strong elastic influence on the binodal (i.e., coexistence) and spinodal behavior of the alloy. In addition, MPFCM-based modeling of energy storage couples the thermodynamic forces driving (Cottrell and Suzuki) solute segregation, precipitate formation and dislocation glide. As implied by the simulation results for edge dislocation dipoles and their configurations, there is a competition between (i) Cottrell segregation to dislocations resulting in a uniform solute distribution along the line, and (ii) destabilization of this distribution due to low-dimensional spinodal decomposition when the segregated solute content at the line exceeds the spinodal value locally, i.e., at and along the dislocation line. Due to the completely different stress field of the screw dislocation configuration in the twist boundary, the segregated solute distribution is immediately unstable and decomposes into precipitates from the start.

Joint Optimization of Path Planning and Resource Allocation in Mobile Edge Computing

With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity $L$ L -step lookahead branch scheme (referred to as $L$ L -step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed $L$ L -step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: Q-learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.

Nature-inspired design of a turbine blade harnessing wave energy

A bidirectional turbine used in an oscillating water column device extracts wave energy from oscillating airflow. To improve its power output, a concept of static extended trailing edge found in the wings of owl and merganser was adopted. The static extended trailing edge with 0–10% of chord length ( C) was analyzed for different flow coefficients via Reynolds-averaged Navier–Stokes equation-based computational fluid dynamics (CFD) analysis. ANSYS-CFX 15.0 was used to simulate the flow. Grid convergence index was calculated to obtain optimum mesh, and numerical validation was done with experimental results. The static extended trailing edge with 5% C enhanced relative mean torque by 23.4% and, reduced relative mean efficiency by 5.4%, before stall condition. The modification increased pressure difference between the suction side and the pressure side and enhanced torque. The increased pressure drop reduced the efficiency. A further longer static extended trailing edge showed poorer stall characteristics.

Essential Investigation and Removal assets using Laser Induced Breakdown Spectroscopy in Silicone Rubber

With laserinduced breakdown spectroscopy (LIBS), the basic sytheses of the room temperaturevulcanized silicone rubber and the high temperature-vulcanized silicone rubber recently arranged examples were broke down in this paper. The LIBS spectra demonstrated that carbon (C), oxygen (O), aluminum (Al), silicon (Si), press (Fe), and zinc (Zn) were contained in the specimens; predictable with the energy dispersive X-ray spectroscopy (EDS) comes about. With scanning electron microscopy (SEM), the micromorphology and the profundity of the removal pits were watched. The pits ’sizes were just around 200?400 μm, and there was about no distinction between the hydrophobicity of the nonablated zone and the removed territory, and the static contact edge of the cavities was considerably bigger than that of the nonablated region, implying that the LIBS examination was practically nondestructive. The profundity of the removal pits indicated linearity association with the laser heartbeat number that would help a considerable measure in the in situ investigation. Expanding the laser energy of the shot, the force of spectra lines of unequivocal component and the electron number density would increment in the meantime. The review result demonstrated that the LIBS would be a promising component investigation method of silicone rubber particularly on line, and the piece of the composites could be plainly watched.

Edge effects in game-theoretic dynamics of spatially structured tumours.

Cancer dynamics are an evolutionary game between cellular phenotypes. A typical assumption in this modelling paradigm is that the probability of a given phenotypic strategy interacting with another depends exclusively on the abundance of those strategies without regard for local neighbourhood structure. We address this limitation by using the Ohtsuki-Nowak transform to introduce spatial structure to the go versus grow game. We show that spatial structure can promote the invasive (go) strategy. By considering the change in neighbourhood size at a static boundary--such as a blood vessel, organ capsule or basement membrane--we show an edge effect that allows a tumour without invasive phenotypes in the bulk to have a polyclonal boundary with invasive cells. We present an example of this promotion of invasive (epithelial-mesenchymal transition-positive) cells in a metastatic colony of prostate adenocarcinoma in bone marrow. Our results caution that pathologic analyses that do not distinguish between cells in the bulk and cells at a static edge of a tumour can underestimate the number of invasive cells. Although we concentrate on applications in mathematical oncology, we expect our approach to extend to other evolutionary game models where interaction neighbourhoods change at fixed system boundaries.

Performance of Micro Surfacing on Expressway

Micro surfacing is developed from a slurry seal technique. It is a mixture of polymer modified bitumen emulsion with 9.5 mm chipping, quarry dust, cement, additive and water. The emulsion used in micro surfacing is CQS-1h and added with natural latex as its polymer modifier. This study was carried out to evaluate the performance of micro surfacing applied on the expressway. In 2008, a number of preventive maintenance projects in North South Expressway used micro surfacing especially in the northern region. This study involved particular sections of expressways located at km 58.60 to km 59.60 south bound and km 84.60 to km 85.60 southbound from Bukit Kayu Hitam. The Portable Pendulum Tester was used to evaluate skid resistance performance while the texture depth of micro surfacing was measured using Sand Patch Test. The surface irregularities data was collected using a 3 m rolling straight edge while the rutting measurement was based on 3 m static straight edge. The measurements were carried for every 3 months starting from April 2009 to 1 year of the operation period. The result shows that the skid resistance, texture depth, surface irregularities and rut depth for micro surfacing comply with the specification requirements after one year of operation. The pendulum test value obtained is greater than 46 with a texture depth of 0.50 mm. The average number of surface irregularities is below 4.0 mm while the rut depth average value is less than 5.0 mm. This study shows that the micro surfacing was performing well on the expressway. It has proven effective in restoring friction characteristic, filling ruts and re-establishing the transverse profile of road surface.

Over-barrier side-band electron emission from graphene with a time-oscillating potential

We propose a model which describes side-band electron emission from vertically aligned monolayer graphene with an internal time-oscillating barrier and a static edge barrier. Our results show that electron emission is governed by the over-barrier emission process, which is dominated by the time-oscillating barrier. The emitted current line density J [nA/nm] is only dependent on the amplitude V1 and on the frequency ω of the time-oscillating barrier, and is characterized by 0<γ=V1/ℏω<1. From the model, it is found that J is maximized at about ω=1014rad/s to 1015rad/s, and that J increases with γ.

Flexural transient response of elastically supported elliptical plates under in-plane loads using Mathieu functions

The elaborated method of eigenfunction expansion in elliptic coordinates is employed to obtain an exact time-domain series solution, involving products of angular and radial Mathieu functions, for the forced flexural vibrations of a thin elastic plate of elliptical planform. The plate is supported by a constant moduli two-parameter foundation, while elastically restrained against translation and rotation at its edge, and subjected to the combined action of uniform in-plane static edge forces and general arbitrary time-dependent transverse loads with arbitrary initial conditions. Numerical calculations are carried out for the displacement response of clamped or simply supported elliptical plates of selected aspect ratios in various practical loading configurations (i.e., an impulsive point load, a point force in circular motion, a uniformly distributed harmonic load, and a blast load), with or without an elastic foundation, while taking the effects of initial tension or compression below the buckling load into consideration. Limiting cases are considered and good agreements with available results as well as with the computations made by using a commercial finite element package are obtained.