Edge Computing Network Research Articles

Optimized LoRaWAN Architectures: Enhancing Energy Efficiency and Long-Range Connectivity in IoT Networks for Sustainable, Low-Power Solutions and Future Integrations with Edge Computing and 5G

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

V2V Energy Trading Scheme Based on Collaborative Edge Computing and Lightning Network

V2V Energy Trading Scheme Based on Collaborative Edge Computing and Lightning Network

Heterogeneous Service-Oriented Resource Provisioning and UAV Deployment for Aerial Edge Computing Networks

Heterogeneous Service-Oriented Resource Provisioning and UAV Deployment for Aerial Edge Computing Networks

DRL-MURA: A Joint Optimization of High-Definition Map Updating and Wireless Resource Allocation in Vehicular Edge Computing Networks

DRL-MURA: A Joint Optimization of High-Definition Map Updating and Wireless Resource Allocation in Vehicular Edge Computing Networks

Price Competition in Multi-Server Edge Computing Networks Under SAA and SIQ Models

Price Competition in Multi-Server Edge Computing Networks Under SAA and SIQ Models

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

Joint Optimization of Caching, Computing and Trajectory Planning in Aerial Mobile Edge Computing Networks: A MADDPG Approach

Joint Optimization of Caching, Computing and Trajectory Planning in Aerial Mobile Edge Computing Networks: A MADDPG Approach

АРХІТЕКТУРА ТА МОДЕЛІ НАДІЙНОСТІ ГІБРИДНИХ СЕНСОРНИХ МЕРЕЖ СИСТЕМ ЕКОЛОГІЧНОГО ТА АВАРІЙНОГО МОНІТОРИНГУ

The authors study the aspects of developing and analyzing the hybrid sensor networks’ operability as subsystems of environmental and emergency monitoring systems for critical infrastructure. The proposed architecture of such a system is based on the technology of edge computing (EC) and combines stationary and mobile components, the first of which is implemented by a ground sensor network (GSN), and the second by a swarm of unmanned aerial vehicles that form a flying EC network. The data collection algorithms, scaling problems, and optimization of the operation of the GSN and monitoring systems in general are analyzed. The reliability models of the GSN in the conditions of failure of one and groups of sensors are developed and investigated. Analytical dependencies of reliability indicators on different sizes of sensor failure clusters and their intensity are obtained. Recommendations for the design and implementation of hybrid sensor networks are given. Keywords: hybrid sensor networks, edge computing, reliability models, multiple failures, environmental monitoring systems, emergency monitoring systems.

Enabling the Future: A Virtualized Approach to 5G and Edge Computing

This abstract examines the evolution of wireless communications technologies beyond 5G (B5G) and the emergence of 6G. It highlights their key role in powering the Internet of Things (IoT) and enabling edge computing. Built on shared resources, this system is exposed to various real-time application scenarios and uses simulated user equipment (UE) and operational Nextcloud instances. Performance metrics are analyzed and the system can automatically scale during high network traffic to ensure high availability. Key concepts include Radio Access Network (RAN), Edge Computing, User Equipment and Virtual Network Functions (VNF). This framework relies on shared resources and undergoes rigorous testing with real-time application scenarios, using simulated User Equipment (UE) and operational Nextcloud instances.

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

AbstractThis article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances.

Joint fiber and MEC deployment for sparsely populated areas

The deployment of multi-access edge computing (MEC) networks gives rise to the MEC placement problem, which deals with finding the right server locations to reduce the cost and guarantee network performance. Multiple papers have been presented to solve this problem, but they are usually oriented to urban areas where short distances and high-quality network infrastructure are assumed. When this problem must be solved for sparsely populated areas, like rural environments, the connectivity is not always granted and the deployment of such connectivity using fiber technologies should be included in the problem. In contrast to urban areas, where the density of users is high and therefore the main problem is capacity, in sparsely populated areas, the problem lies in how to cost-effectively plan the MEC sites and the interconnecting network while meeting the delay constraints of the services offered through that network. This paper proposes a technique to solve the MEC placement problem considering the joint deployment of the optical network required to interconnect the base stations and the MEC servers. It consists of a three-phase scheme, which combines a spanning tree topology, for fiber deployment, with the use of mixed integer linear programming (MILP) formulations to minimize MEC servers and MEC data centers (MEC-DCs). We have applied the technique in a case study for a province in Spain (Valladolid, 8110km2 ), obtaining a reduction of around 50% of the total cost when compared to a previous work. In addition, a clustering method is proposed to improve the scalability of the model for large scenarios. A simulation study is also presented to demonstrate the performance of the proposal assuming a 94,226km2 region (Castilla y León) with 1576 base stations.

Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

In this paper, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches.

Communication-Computation-Aware User Association in MEC HetNets: A Meta-Analysis

The stochastic geometry-based modeling and analysis of large-scale mobile edge computing (MEC) networks are vital for the effective configuration of MEC networks. In this paper, we develop a meta-analytical framework for MEC-enabled heterogeneous networks with the communication-computation-aware (CCA) user association mechanism. Compared with the communication-based user association mechanisms in most existing works, the CCA user association mechanism can capture the impacts of network computation capability on the association process between the user and MEC access point, at the expense of dealing with the more complex coupling of communication and computing. Given the need for interference characterization, we first derive the essential prerequisite quantities (i.e., per-tier association probability, link distance distribution, interferer process intensity, etc.) to represent the computation-dependent interference model. Further, the moment and the meta distribution of the task success offloading probability are derived, based on which we investigate the task execution latency performance, including the communication latency, local computing latency, and edge computing latency. By theoretical analysis and simulation results, it is demonstrated that the proposed analytical framework can provide accurate fine-grained network information for the MEC-enabled HetNets. Moreover, we elaborate on the impacts of the edge computation capability on the network performance and reveal important tradeoffs of the performance metrics.

Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-Enabled Mobile Edge Computing Network

Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-Enabled Mobile Edge Computing Network

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.

Priority-aware path planning and user scheduling for UAV-mounted MEC networks: A deep reinforcement learning approach

Owing to the flexibility and controllability, unmanned aerial vehicle (UAV) is frequently integrated into mobile edge computing (MEC) network to improve the system performance. This paper investigates a novel multi-user multi-hotspot MEC network supported by a UAV, where the UAV can help compute the tasks offloaded from end users (EUs) in multiple hotspots. In this network, we consider the task priority and task size are dynamic, due to the EUs’ demands. We then propose a task priority-based system utility model to evaluate the network performance, which considers the priorities of tasks based on the urgent or non-urgent level. We further formulate a utility maximization problem that jointly optimizes the UAV’s access path and the EUs’ offloading strategy, while satisfying the constraints related to the UAV’s battery capacity and UAV’s duration of flight. Since the formulated problem is a NP-hard problem, we present a deep reinforcement learning (DRL) based scheme as a solution. The DRL scheme utilizes principles from reinforcement learning to address the optimization problem effectively, resulting in a dynamic solution. Simulation results demonstrate that the proposed DRL scheme outperforms alternative benchmark schemes in terms of system utility.

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.

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.