An Enhanced Hybrid Edge-Cloud algorithm For Reducing IoT Service Delay

This paper proposed a pickup and delivery (P&D) task provisioning and vehicle routing framework for carrier robots in a factory with edge servers assisting as the intermediary between robots and working stations, where the task requests come. The carrier robots must pick up and deliver each assigned load from and to designated locations with minimal traveled distance and without violating designated constraints. Edge servers are utilized to facilitate communication between the main server and delivery robots and assist the main server in deciding the best robot for each incoming task request and the updated route to facilitate execution of tasks by each carrier robot. The problem of pickup and delivery for each robot is modeled based on Dial-a-Ride Problem, and a discrete bio-inspired algorithm is proposed to solve this problem. The tasks are distributed to edge servers and carrier robots by taking their service loads into account. The simulation results show that the proposed framework can provide an effective solution towards optimizing the pickup and delivery process in a smart factory.

The typical centralized cloud computing system cannot meet the Quality of Service (QoS) prerequisites for some applications, notably those that require real-time, reliability, and security, since to the increment of connected gadgets to the Internet of Things (IoTs). Cloud computing has advanced with the emergence of edge computing. Its primary strength is its ability to move delicate information from the cloud to the edge while improving organization security, empowering high-recurrence correspondence, and empowering continuous information move. The edge computing network faces a number of serious security challenges as a result of the storage of confidential and sensitive data on edge servers. Since the information produced by IoT gadgets is parted into a few pieces and put away in different edge servers that are scattered across different areas, it is trying to guarantee information trustworthiness because of information misfortune and mistaken information capacity in edge servers. With the ascent of edge figuring, there are different security and information trustworthiness gives that blockchain innovation can address. We give an exhaustive assessment of edge processing in blend with blockchain innovation in this article. We start by presenting the models for blockchain and edge figuring. The objectives and plan of edge processing with combination of blockchain are then covered. The third concentrate on the blend of edge figuring and blockchain is advertised. The exploration issues are at last investigated.

Edge Computing is a distributed computing architecture where the computing process takes place near the user’s physical location or at the source where the data originates. By placing the edge servers (ES) closer to the user’s location, the services received are faster and more reliable while benefiting from edge computing. Various Internet of Vehicle (IoV) applications in smart cities including assisted and autonomous driving, real-time accidents monitoring require huge data processing and low-latency communication. Since the user’s devices are resource constrained, an effective approach to address this constraint is to offload their tasks to nearby ES. So, an adaptive placements (to respond continuously changing environment) of these edge computing devices play a major role in the performance of various IoV applications. So, an efficient placement of ES is considered a critical issue in vehicular edge computing (VEC). To address the efficient and cost aware dynamic ES placement problem (CADEP), we developed two greedy algorithms. First, is cost aware and vehicle density based deployment of ES (static) that ensures that each vehicle’s demand is covered by at least by one ES (coverage constraint), called Greedy_static. Second, is based on vehicle density and is dynamic as per changing environment, called Greedy_dynamic which updates ES locations periodically based on change in the environment. To minimize the relocation cost, we formulated an optimization problem and used Hungarian matching to find optimal cost. For various vehicle densities, we found that our algorithms outperform uniform strategies in terms of cost-effectiveness and ES utilization. Further, for dynamic relocation of ES, we have shown that the cost required to relocate ES randomly is more as compared to our proposed algorithm Greedy_dynamic.

The long-term impact on global production of the shift from traditional to intelligent manufacturing is intriguing. The concept of “Industry 4.0” has been put up as a means of expanding production to achieve cost-effectively shorter product life cycles and extreme mass customization. The goal of a smart factory is to improve production efficiency, quality, control, visibility, and management by combining physical and cyber technology. For Industry 4.0 to work, manufacturers will need a smart factory equipped with cutting-edge technologies such as the Internet of Things (IoT), cloud computing, and machine intelligence (ML). As a result, we outline and illustrate how ML can be used in a cloud-assisted factory to increase its performance (CaSF). An entirely new four-tier CaSF architecture with vertical connections has been demonstrated in this study for the first time. In addition, the CaSF’s core ML technologies are categorized and defined by the architecture hierarchy’s logical relationships. It is now time to discuss some of the most pressing concerns and potential solutions related to ML technologies in CaSF systems. Industry 4.0 rollout has been aided to some degree by the incorporation of machine learning (ML) in smart factories.

A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing

Edge computing is widely recognized as a crucial technology for the upcoming generation of communication networks and has garnered significant interest from both industry and academia. Compared to other offloading models like cloud computing, it provides faster data processing capabilities, enhanced security measures, and lower costs by leveraging the proximity of the edge servers to the end devices. This helps mitigate the privacy concerns associated with data transfer in edge computing, by reducing the distance between the data source and the server. Raw data in typical edge computing scenarios still need to be sent to the edge server, leading to data leakage and privacy breaches. Federated Learning (FL) is a distributed model training paradigm that preserves end devices’ data privacy. Therefore, it is crucial to incorporate FL into edge computing to protect data privacy. However, the high training overhead of FL makes it impractical for edge computing. In this study, we propose to facilitate the integration of FL and edge computing by optimizing FL hyper-parameters, which can significantly reduce FL’s training overhead and make it more affordable for edge computing.

The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented; specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.

Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

As the technologies are growing, new challenges are emerging. The Internet is also one of these. IoT and cloud computing are considered a highly suitable combination in the field of information technology. This combination has generated the highest range of M2M and H2M interaction networks containing billions of gadgets operated remotely. Cloud computing provides computing and data storage utilities including service databases, software, networking and data analytics over the Internet to give faster deployment, economies of scale and flexible and optimal resource management. In edge computing, edge servers are placed close to the smart devices that reduce the communication overhead and security concerns. In this communication, a lightweight cryptographic approach has been proposed for searching and sharing the requested data by authorised end-users on edge server in cloud-assisted Internet of things. Results have shown a decrease in time duration for searching the data while not compromising with the security detail.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aerial computing</i> , via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

Edge computing, which locates edge servers with limited computing and storage resources at the edge of networks, is expected as a novel architecture for low-latency applications. In edge computing, users offload a task to edge servers due to poor computing resources and batteries of mobile devices so that the edge servers execute the offloaded task and return the result of it to users. Users can thus enjoy various applications without depending on the limitations of mobile devices. However, when the edge server’s load is too heavy, a large number of tasks will be offloaded to distant cloud servers. The long distance between users and cloud servers significantly degrades the quality of mobile applications. To prevent this problem, we propose flow splitting and aggregation schemes to improve offload delay to cloud servers in edge computing. This scheme splits TCP connections between users and cloud servers at the edge server, and then aggregates TCP connections between the edge server and cloud servers. We show the effectiveness of our scheme through simulation evaluations.

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

IoT (Internet of Things) is a core technology of hyper connected society that connects and interacts with all things, spaces and environments in the real world. Recently, IoT has evolved into edge computing technology that enhances data processing and analysis by decentralizing the computing power that was performed in the central cloud to an end device. Since edge computing is a structure suitable for a manufacturing environment requiring real-time processing of data and fast response, many studies have considered edge computing as an optimized platform technology for constructing a smart factory. Therefore, we design and implement a distributed smart factory platform for application to the factory environment based on the distributed architecture of edge computing. The proposed platform uses the data communication mechanism based on OPC UA standard to support M2M interaction between distributed edge devices. The implementation results are applied to the smart factory test bed, which is constructed through a preliminary study for applicability verification.

Vehicular applications in smart cities, such as assisted and autonomous driving, require sophisticated data processing, low latency, and high throughput data transmission. Edge Computing is a leading approach designed to meet those application requirements. By deploying Edge servers at the network's edge, close to the vehicles, such applications can be successfully delivered while adhering to low-latency and high-throughput requirements. However, optimal placement of Edge servers is challenging since it necessitates a trade-off between quality of service and deployment cost. Latency can be reduced by placing as many Edge servers as feasible close to the vehicles, however, this results in significant deployment costs. This work addresses the problem of optimal Edge server placement. It solves this problem using integer linear programming, considering the relation between delay and cost, as well as the capacity of Edge servers in realistic road traffic scenarios. The proposed generic methodology is designed to reduce the cost of deploying Edge servers by combining the achievement of the desired latency threshold with workload balancing between Edge servers. We evaluate the efficiency of the proposed solution mathematically and through simulations based on open data from real vehicles traffic on roadways of Bordeaux, France. The obtained results demonstrate that our solution outperforms existing Edge server placement approaches, especially on workload balancing.

Emerging mobile applications, such as cognitive assistance based on deep neural network (DNN), require low latency as well as high computation power. To meet these requirements, edge computing (also called fog computing) has been proposed, which offloads computations to edge servers located near mobile clients. This paradigm shift from cloud to edge requires new computing infrastructure where edge servers are pervasively distributed over a region.This paper presents PerDNN, a system that executes DNNs of mobile clients collaboratively with pervasive edge servers. PerDNN dynamically partitions DNN computation between a client and an edge server to minimize execution latency. It predicts the next edge server the client will visit, calculates a speculative partitioning plan, and transfers the server-side DNN layers to the predicted server in advance, which reduces the initialization overhead needed to start offloading, thus avoiding cold starts. We do not incur excessive network traffic between edge servers, though, by migrating only a tiny fraction of the server-side DNN layers with negligible performance loss. We also use GPU statistics of edge servers for DNN partitioning to deal with the resource contention caused by multi-client offloading. In the simulation with human trace datasets and execution profile of real hardware, PerDNN reduced the occurrence of cold starts by up to 90%, achieving 58% higher throughput when clients change their offloading servers, compared to a baseline without proactive DNN transmission.