Edge-based Processing Research Articles

An efficient task allocation framework for scheduled data in edge based Internet of Things using hybrid optimization algorithm approach

Allocation of tasks in IoT is an integral and critical approach to finding a perfect match between scheduled tasks of a particular application and Edge-based processing devices for instant response and efficient utilization of resources to make them renewable. We need a protocol to help optimize the problem of allocating processing devices to the tasks, as task allocation is considered an NP-hard problem to prevent problems with energy consumption and response time problems. For this, a hybrid bio-inspired Swarm-based approach will improve the solution to optimize the matching of a task to a particular device. This paper proposed a Meta-heuristic algorithm to optimize Energy and Time-delay for allocating tasks to the edge-based Processing device in IoT. The proposed algorithm called the Hybrid Artificial Bee Colony whales Optimization algorithm (HAWO) is formulated by integrating Artificial Bee Colony with the Whales Optimization algorithm to overcome the search process of an Artificial Bee Colony, which converges too soon due to the local search of Employee Bee phase and Onlooker Bee phase causing the problem of looping. From the simulation results conducted in Matlab, it is observed that the integrated HAWO method shows promising results in terms of Energy and Time Delay when compared with Artificial Bee Colony and Whales Optimization algorithms separately. Also, proposed method when compared with the benchmark work shows significant improvements of 50%, 25% and 60% in terms of Energy, Time Delay and Best cost, respectively.

Wearable Fabric μ Brain Enabling On-Garment Edge-Based Sensor Data Processing

Advances in artificial intelligence (AI) is an enabler for innovative wearable on-garment edge-driven AI. Such AI technologies are leveraging bioinspired methods to develop spiking neural networks (SNNs) that mimic the workings of the human brain to produce higher performance edge-based processing capabilities. Such advancements are driving the development and emergence of neuromorphic computing with SNN architectural designs. Taking inspiration from these, this article proposes and demonstrates a wearable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Brain multilayer SNN smart garment, with an event-driven artificial SNN embedded into the garments fabric. The fabric-based multilayer SNN is interchangeable and scalable through its fabric hidden layer (FHL) and fabric patch design and can be interconnected with textile-based analog monitoring sensors. The prototype was tested to check the functionality of the individual and collective nodes of the SNN as well as evaluating the wearable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Brain for fault tolerance scenarios (ripping and fraying) proving the prototype remained intact following the disconnection of fabric node Patches 5 and 8. Two application demonstrators were identified and tested. The first application focused on the detection of touch sensation on a forearm using the wearable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Brain; here, real-world use cases were checked against the expected output. Fluctuations were encountered when voltage output readings were above and below the set threshold level of 2.3 V. The second application interfaced the wearable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Brain to live neurons to demonstrate how textile pressure sensors on the surface can lead to stimulation of neurons, when validated we observed an 11-s timeframe where the brain slice was stimulated.

SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

Natural disasters have a significant impact on human welfare. In recent years, disasters are more violent and frequent due to climate change, so their impact may be higher if no preemptive measures are taken. In this context, real-time data processing and analysis have shown great potential to support decision-making, rescue, and recovery after a disaster. However, disaster scenarios are challenging due to their highly dynamic nature. In particular, we focus on data traffic and available processing resources. In this work, we propose SLedge—an edge-based processing model that enables mobile devices to support stream processing systems’ tasks under post-disaster scenarios. SLedge relies on a two-level control loop that automatically schedules SPS’s tasks over mobile devices to increase the system’s resilience, reduce latency, and provide accurate outputs. Our results show that SLedge can outperform a cloud-based infrastructure in terms of latency while keeping a low overhead. SLedge processes data up to five times faster than a cloud-based architecture while improving load balancing among processing resources, dealing better with traffic spikes, and reducing data loss and battery drain.

Edge extraction method for medical images based on improved local binary pattern combined with edge-aware filtering

Edge-based processing and analysis of medical images are indispensable in modern diagnosis and the application value of edge extraction technology is rising with this tide. For medical images containing redundant noise, blurred details, and low contrast, a robust edge extraction method based on edge-aware filtering and improved local binary pattern (EF-ALBP) is proposed in this paper. EF-ALBP contains two parts: the edge-aware filtering (EF) is proposed to suppress noise and enhance contrast while preserve edges, and ALBP is used to extract the crucial edge features of the previous step results accurately by introducing an accumulation function into local binary pattern. Quantitative analyses and visual evaluation for experimental results on X-ray, CT, and MRI images from medical image datasets demonstrate that the proposed method is competitive in robustness and outperforms those of the popular methods.

A privacy preserving scheme for vehicle-to-everything communications using 5G mobile edge computing

Recently much research has been carried out towards the road condition monitoring system for improving the road safety and moving towards modern intelligent transportation system (ITS). 5G-based vehicle-to-everything (V2X) communication offers wider support and enhances the capabilities of road monitoring systems. The road monitoring systems utilizes the vehicles to report road conditions, traffic information or accidents to the roadside units (RSUs), which further relays such information to the cloud server for computing and processing. Then the cloud computing network redirects the necessary actions based on the these received reports. The major problem with this type of architecture is that it requires long computational time and causes high network latency. This also leads to poor utilization of bandwidth. As a result, in this paper a new network protocol has been proposed based on edge-based computing. In this paper, we present a new privacy preserving protocol to boost the security in vehicular network, by utilizing the concept of edge-based computing. The major concern in edge-based architecture is that all the data processing is performed at edge nodes which have various security holes. To secure the edge-based processing, we propose the idea of improved certificate-less aggregation sign-cryption scheme (iCLASC) with incorporating the information transmission protocol to formulate the road monitoring data. Confidentiality, integrity, joint authentication, privacy and anonymity are the key components of the security protocol. Lastly, we make the comparisons of the proposed work with the existing works in terms of computational cost, communication overhead and security feature analysis. Our scheme outperform all the previous methods and shows satisfactory security and communication performance requires for 5G-V2X Communication.

Edge-Based Live Video Analytics for Drones

Real-time video analytics on small autonomous drones poses several difficult challenges at the intersection of wireless bandwidth, processing capacity, energy consumption, result accuracy, and timeliness of results. In response to these challenges, this paper describes four strategies to build adaptive computer vision pipelines for domains such as search-and-rescue, surveillance, and wildlife conservation. Our experimental results show that a judicious combination of drone-based processing and edge-based processing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or latency.

Shape‐adaptive 3‐D mesh simplification based on local optimality measurement

AbstractMesh simplification is the process of reducing the number of triangles in a mesh representation of object surface. For a given level of detail or error tolerance, the conventional mesh simplification algorithms maximize the edge length globally, without explicitly considering local object shape. In this paper, we present a shape‐adaptive mesh simplification algorithm that locally maximizes edge length, depending on local shape. The proposed algorithm achieves shape‐adaptive simplification by iteratively maximizing edges between vertices, based on comparison with the ‘optimal’ edge lengths derived from local directional curvatures for a given error tolerance. Edge‐based processing facilitates the local shape adaptation and preserves sharp features. Experimental results demonstrate the efficacy of the proposed algorithm, by showing good visual quality and extremely small approximation error. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Motion edges and regions guide image segmentation by colour.

Image segmentation is an important early stage in visual processing in which the visual system groups together parts of the image that belong together, prior to or in conjunction with object recognition. Two principal processes may be involved in image segmentation: an edge-based process that uses feature contrasts to mark boundaries of coherent regions, and a region-based process that groups similar features over a larger scale. Earlier, we have shown that motion and colour interact strongly in image segmentation by the human visual system. Here we explore the nature of this interaction in terms of edge- and region-based processes. We measure performance on a region-based colour segmentation task in the presence of distinct types of motion information, in the form of edges and regions which in themselves do not reveal the location of the colour target. The results show that both motion edges and regions may guide the integrative process required for this colour segmentation task. Motion edges appear to act by delimiting areas over which to integrate colour information, whereas motion similarities define primitive surfaces within which colour grouping and segmentation processes are deployed.

Converging Evidence for Two Separate Processes in Perceptual Grouping

Using a visual search paradigm, Gilchrist, Humphreys, Riddoch, and Neumann ( Journal of Experimental Psychology: Human Perception and Performance in press) demonstrated that grouping was mediated by a contrast-polarity-dependent surface-based process, and a contrast-independent edge-based process. A vertical pair of circles ‘popped out’ amongst horizontal pairs of circles as distractor only if the items within each pair had the same contrast polarity. Alternatively, if the circles were replaced with squares that had collinear edges, the vertical pair ‘popped out’ regardless of the contrast polarity of the squares. Here we report new data in which the role of these two grouping processes in the control of saccadic eye-movements was investigated. If subjects are presented with two items concurrently in an eye-movement task they will often make a saccade to the midpoint between the two items—this has been called the ‘global effect’. This effect is assumed to reflect some underlying integration of the visual information during the programming of saccade landing position. Distractor items that either shared the same polarity or had collinear edges with the target were found to affect the landing position of saccades. Distractors that had neither collinear edges nor common polarity produced a smaller global effect but the landing position was still affected. This suggests that the integration processes that underlie the global effect are sensitive to both common edge and surface properties. However, some integration may occur as a result of the mere presence of activity in the visual field.