Data Offloading Research Articles

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

Joint Data Offloading and Energy-Efficient Secure MEC Resource Allocation Method for IoT Device Data in RAN Communication

Joint Data Offloading and Energy-Efficient Secure MEC Resource Allocation Method for IoT Device Data in RAN Communication

Deep reinforcement learning in edge networks: Challenges and future directions

Driven by the perception of IoT applications and advanced communication technologies, including beyond 5G and 6G, recent years have seen a paradigm shift from traditional cloud computing towards the local edge of the networks. Modern edge-centric networks have become autonomous and decentralized to expand IoT applications and corresponding data fusion. When edge networks are uncertain, network entities execute tasks locally to increase network performance. Over the past decade, Reinforcement Learning (RL) algorithms have been integrated into edge networks to generate optimal decisions and intelligent edge networks. However, complex edge networks with ample state and action space create several challenges in making optimal decisions with the RL technique. To address such shortcomings, Deep Reinforcement Learning (DRL) is combined with edge networks to build an intelligent edge framework. Concerning the benefits of edge intelligence, this paper summarizes the importance of traditional and advanced DRL methodologies in edge networks. Besides, we discuss different types of DRL-enabled libraries and state-of-the-art edge models for processing real-time IoT applications. Then, we review other emerging issues in edge networks regarding data offloading, caching, dynamic network access, edge information fusion, and data privacy. Moreover, we incorporate various DRL-enabled IoT applications in edge networks such as healthcare applications, industrial applications, traffic management, etc. Finally, we shed light on future trends of intelligent edge computing regarding system performance, security, and network management.

Optimizing energy efficiency in mobile edge computing: Leveraging latency-aware offloading, clustering, and UAV placement strategies

In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (H-NOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAV-hosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.

VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks

Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloading tasks. In this work, we develop an optimization framework, named VESBELT, for offloading tasks from Vessel users to nearby Edge Servers or the cloud server, aiming to reduce Energy consumption and service Latency Trade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long-short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide real-time solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.

Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading.

Internet of Things-Based Robust Green Smart Grid

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

Reinforcement learning based autonomous multi-rotor landing on moving platforms

Multi-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of the vehicle. Classical approaches rely on accurate, complex and difficult-to-derive models of the vehicle and the environment. Reinforcement learning (RL) provides an attractive alternative due to its ability to learn a suitable control policy exclusively from data during a training procedure. However, current methods require several hours to train, have limited success rates and depend on hyperparameters that need to be tuned by trial-and-error. We address all these issues in this work. First, we decompose the landing procedure into a sequence of simpler, but similar learning tasks. This is enabled by applying two instances of the same RL based controller trained for 1D motion for controlling the multi-rotor’s movement in both the longitudinal and the lateral directions. Second, we introduce a powerful state space discretization technique that is based on i) kinematic modeling of the moving platform to derive information about the state space topology and ii) structuring the training as a sequential curriculum using transfer learning. Third, we leverage the kinematics model of the moving platform to also derive interpretable hyperparameters for the training process that ensure sufficient maneuverability of the multi-rotor vehicle. The training is performed using the tabular RL method Double Q-Learning. Through extensive simulations we show that the presented method significantly increases the rate of successful landings, while requiring less training time compared to other deep RL approaches. Furthermore, for two comparison scenarios it achieves comparable performance than a cascaded PI controller. Finally, we deploy and demonstrate our algorithm on real hardware. For all evaluation scenarios we provide statistics on the agent’s performance. Source code is openly available at https://github.com/robot-perception-group/rl_multi_rotor_landing.

Co-Optimization of Cell Selection and Data Offloading in Sparse Mobile Crowdsensing

Co-Optimization of Cell Selection and Data Offloading in Sparse Mobile Crowdsensing

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

SummaryIn this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS.

A new secure offloading approach for internet of vehicles in fog-cloud federation

The Internet of Vehicles (IoV) plays a crucial role in advancing intelligent transportation systems. However, due to limited processing power, IoV faces challenges in independently handling large volumes of data, necessitating the use of offloading as a solution. Offloading data in wireless environments raises security concerns, highlighting the need for robust data protection mechanisms. This study introduces a secure offloading (SO) scheme within the Fog-Cloud Federation for IoV. The proposed NSO-VFC scheme undergoes both informal and formal analysis using the Avispa tool, demonstrating resilience against active and passive attacks. Performance evaluations indicate that the security measures of NSO-VFC meet acceptable standards compared to similar approaches. Nonetheless, the heightened focus on security incurs higher computational and communication costs than alternative strategies. Simulation experiments using the NS3 tool involve varying numbers of IoVs (50, 70, and 100), revealing that increased IoV density correlates with enhanced packet delivery rates and throughput within the NSO-VFC scheme.

A Blockchain-Based Scheme for Secure Data Offloading in Healthcare With Deep Reinforcement Learning

With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

Design and Performance Analysis of Prioritized On-the-Spot Mobile Data Offloading Scheme

Recent years have witnessed a surge in the mobile data traffic which is only expected to grow exponentially in the coming years. To cope up with this huge data demand, mobile operators have restored to a novel scheme called on-the-spot mobile data offloading wherein mobile data is offloaded onto Wi-Fi network whenever the user enters a Wi-Fi coverage area. Further reports indicate that a sizable portion of the mobile data traffic is multimedia in nature that demands good quality of service. In this context, we design and analyze a scheme to prioritize different data traffic encountered in on-the-spot mobile data offloading. Using a Markov chain model for on-the-spot mobile data offloading scheme, we derive the packet transmission delay of different traffic categories that carry different priorities. We validate out analytical results against simulation and demonstrate the effectiveness of the proposed prioritization scheme.

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review

Objectives: This article reviews and provides an exhaustive examination of task offloading and trajectory control techniques in unmanned aerial vehicles (UAVs) integrated with Internet of Things (IoT), highlighting their significance and impact on the UAV ecosystem. The paper begins by introducing the fundamental concepts of UAVs, IoT, and their integration, emphasizing the potential benefits and challenges of this union. Subsequently, it delves into an extensive exploration of task offloading, a critical aspect that optimizes UAV operations by distributing tasks between the UAV and edge/cloud computing resources. Various task offloading strategies, including computation offloading, data offloading, and control offloading, is discussed in detail, elucidating their role in optimizing resource utilization, energy efficiency, and real-time decision-making in UAVs.Methods: The review comprehensively covers trajectory control techniques, which are essential for ensuring UAVs can navigate through dynamic environments safely and efficiently. This study outlines the use of IoT technologies, such as GPS, sensors, and communication networks, to enable precise trajectory planning, obstacle avoidance, and adaptive path adjustments. It also discusses the integration of machine learning and AI algorithms for autonomous UAV navigation, taking into account environmental factors, mission objectives, and real-time data from IoT sources. The paper further discusses the challenges and potential security concerns associated with IoT integration in UAVs, as well as the emerging trends and future prospects of this dynamic field. It emphasizes the need for standardized protocols and robust cybersecurity measures to ensure the reliability and safety of UAV-IoT systems.Findings: This exhaustive review offers a comprehensive understanding of the synergistic relationship between UAVs and IoT, shedding light on the task offloading and trajectory control techniques that empower these autonomous aerial vehicles. By leveraging IoT technologies, UAVs are poised to continue transforming industries and driving innovation in ways previously unimaginable, making this interdisciplinary field an area of great promise and significance.

Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

UNION: Fault-tolerant Cooperative Computing in Opportunistic Mobile Edge Cloud

Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of a single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fa u lt-Tolera n t Cooperat i ve C o mputi n g) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is 𝒪(|ℐ| 2 +|𝒩|) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies.

Container-based low-delay microservice data offloading and processing for distribution substation area gateway in smart grid

A large number of source-load-storage devices are connected to the power system in the new power system, which leads to the surge of power service and puts forward new requirements for the operation efficiency of the distribution substation area. Container technology provides computing capacity for microservice of different devices by integrating computing resources of distribution substation area gateway. However, the differentiation of microservice priorities and continuous processing requirements bring new challenges to the optimization of microservice data offloading and processing. In this paper, we propose a container-based low-delay microservice data offloading and processing method for distribution substation area gateway. Firstly, we construct a container-based low-delay microservice data offloading and processing architecture for distribution substation area gateway. Then, an empirical matching-based joint gateway and container selection algorithm for low-delay microservice data offloading and processing is proposed. By introducing empirical performance and microservice priority, realizing the gateway and container selection of microservice data, reducing the microservice data offloading and processing delay. Simulation results show that the proposed algorithm is superior to the existing algorithm in total delay of microservice data offloading and processing.

An Air-Ground Coordinated Sensing, Relay and Offloading for Emergency Disposal in ITS System

Nowadays, the Unmanned Aerial Vehicle (UAV) has emerged as a powerful platform for diversified application development. In traffic areas specifically for the much-anticipated Intelligent Transportation System (ITS), UAVs can coordinate with the ground internet of things infrastructure, such as the Road Side Unit (RSU), to perform 3D data collection and processing, improving traffic safety and transportation efficiency. In ITS, decision-making is crucial and relies heavily on proper and timely processing and transmission of the massive amounts of data generated by ubiquitous sensors, especially in emergency disposal scenarios. In this paper, the latency incurred from RSU-aided data offloading and UAV-aided data relay plays a decisive role, characterized by both the average and risk performance metric, Conditional Value at Risk (CVaR), for quantifying the risk incurred from the latency violation over a certain threshold in probability. Specifically, the peak Age of Information (AoI) with its distribution is adopted to underlying the CVaR analysis in relay latency. Besides, the matching algorithm to coordinate the mutual transmission between UAVs and RSUs is proposed, achieving low computation complexity and minimum risk for both sides. The system performance is validated through analysis, simulation, and field experiments.