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Task Offloading and Resource Allocation for IoT in Next-Generation Networking

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A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 June 2026) | Viewed by 15268

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Editors

Dr. Nguyen Khoa

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Guest Editor

School of Information Technology, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: communication networks; cloud/edge computing; parked vehicle edge computing (PVEC); Internet of Vehicles (IoV); software-defined networking (SDN); network function virtualization (NFV); containerization technologies; evolutionary algorithms; AI/ML applications
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Dr. Steve Drew

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Guest Editor

Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: distributed learning systems; federated learning; AIOps; edge computing; cloud-native initiatives for blockchain services

Dr. Qihao Li

E-Mail Website
Guest Editor

College of Communication Engineering, Jilin University, Changchun 130000, China
Interests: industrial internet; digital twin; optimal control and optimization; wireless network security; localization
Special Issues, Collections and Topics in MDPI journals

Dr. Jinhua Guo

E-Mail Website
Guest Editor

College of Engineering & Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Interests: automotive engineering; computing and networks; cybersecurity; machine learning; optimization; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) continues to rapidly expand, enabling a hyper-connected world with billions of devices communicating and collaborating in real-time. Ranging from industrial automation to smart cities, healthcare, and agriculture, the IoT is the backbone of numerous digital transformation initiatives. However, IoT devices are often constrained by limited resources such as processing power, memory, and battery life. Efficient task offloading, the delegation of computational tasks to more capable resources in edge, fog, or cloud computing environments, has become essential for enhancing the performance of IoT systems while efficiently preserving energy and optimizing network resources.

Together with the proliferation of the IoT, next-generation networking technologies, especially 6G, promise to reshape how task offloading and resource allocation are performed. Building on the advancements of 5G, 6G networks will introduce unprecedented capabilities, such as ultra-reliable low-latency communication (URLLC), terahertz (THz) frequency bands, integrated space–air–ground–sea networks, and enhanced support for intelligent and autonomous systems. These innovations will unlock new potential for the IoT, especially in environments requiring real-time decision-making, massive machine-type communication (mMTC), and high throughput.

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) play crucial roles in supporting these advancements, offering flexible, programmable network architectures that allow dynamic control of resources and traffic. Moreover, emerging applications in the Internet of Vehicles (IoV) and autonomous systems will increasingly rely on task offloading and resource allocation to ensure seamless connectivity, safety, and efficiency in complex, high-mobility environments.

This Special Issue seeks contributions that explore the interplay between task offloading, resource allocation, and next-generation networking technologies, including 6G, SDN, NFV, and the IoV. We aim to gather cutting-edge research and practical solutions that address the challenges and opportunities posed by these evolving networks in enhancing the capabilities and efficiency of IoT systems.

The Special Issue will focus on several key areas that reflect the advancements in 6G networks and their impact on task offloading and resource allocation. It will feature, but is not limited to, the following areas:

Task offloading in 6G networks: enabling real-time and high throughput IoT applications.
Energy-efficient resource allocation for IoT in next-generation networks.
Ultra-low latency and high device connectivity in 6G for task offloading.
Dynamic resource management in distributed IoT systems.
Leveraging edge AI for task offloading in 6G and beyond.
SDN-enabled IoT architectures for optimized task offloading.
NFV-driven flexible resource allocation for scalable IoT services.
Network slicing in 6G: tailored resource allocation for IoT applications.
Task offloading in IoV: enhancing V2X and autonomous driving with 6G.
Collaborative edge computing and task offloading in IoV.
Green networking: sustainable resource allocation in next-generation IoT.
Security and privacy considerations in task offloading for 6G IoT systems.

Dr. Nguyen Khoa
Dr. Steve Drew
Dr. Qihao Li
Dr. Jinhua Guo
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

task offloading
resource allocation
Internet of Things (IoT)
6G networks
edge computing
software-defined networking (SDN)
network function virtualization (NFV)
ultra-low latency
Internet of Vehicles (IoV)
edge AI

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Related Special Issue

Task Offloading and Resource Allocation for IoT in Next-Generation Networking—2nd Edition in Future Internet

Published Papers (10 papers)

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Editorial

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3 pages, 132 KB

Open AccessEditorial

Editor for the Special Issue on: Task Offloading and Resource Allocation for IoT in Next-Generation Networking

by Khoa Nguyen, Steve Drew, Qihao Li and Jinhua Guo

Future Internet 2026, 18(10), 512; https://doi.org/10.3390/fi18100512 - 27 Sep 2026

Viewed by 63

Abstract

The proliferation of the Internet of Things (IoT) has created a hyper-connected ecosystem where devices communicate, compute, and collaborate under stringent CPU capacity, memory, energy, and latency constraints [...] Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

Research

Jump to: Editorial, Review

26 pages, 547 KB

Open AccessArticle

A Two-Stage Multi-Objective Cooperative Optimization Strategy for Computation Offloading in Space–Air–Ground Integrated Networks

by He Ren and Yinghua Tong

Future Internet 2026, 18(1), 43; https://doi.org/10.3390/fi18010043 - 9 Jan 2026

Cited by 1 | Viewed by 854

Abstract

With the advancement of 6G networks, terrestrial centralized network architectures are evolving toward integrated space–air–ground network frameworks, imposing higher requirements on the efficiency of computation offloading and multi-objective collaborative optimization. However, existing single-decision strategies in integrated space–air–ground networks find it difficult to achieve coordinated optimization of delay and load balancing under energy tolerance constraints during task offloading. To address this challenge, this paper integrates communication transmission and computation models to design a two-stage computation offloading model and formulates a multi-objective optimization problem under energy tolerance constraints, with the primary objectives of minimizing overall system delay and improving network load balance. To efficiently solve this constrained optimization problem, a two-stage computation offloading solution based on a Hierarchical Cooperative African Vulture Optimization Algorithm (HC-AVOA) is proposed. In the first stage, the task offloading ratio from ground devices to unmanned aerial vehicles (UAVs) is optimized; in the second stage, the task offloading ratio from UAVs to satellites is optimized. Through a hierarchical cooperative decision-making mechanism, dynamic and efficient task allocation is achieved. Simulation results show that the proposed method consistently maintains energy consumption within tolerance and outperforms PSO, WaOA, ABC, and ESOA, reduces the average delay and improves load imbalance, demonstrating its superiority in multi-objective optimization. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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22 pages, 2232 KB

Open AccessArticle

A Dynamic Offloading Strategy Based on Optimal Stopping Theory in Vehicle-to-Vehicle Communication Scenarios

by An Li, Jiaxuan Ling, Yeqiang Zheng, Mingliang Chen and Gaocai Wang

Future Internet 2026, 18(1), 18; https://doi.org/10.3390/fi18010018 - 28 Dec 2025

Cited by 1 | Viewed by 869

Abstract

Faced with the access of a large number of devices, and for mobile vehicles with high speeds, some situations may be far from the communication range of the current edge node, resulting in a significant increase in communication latency and energy consumption. To ensure the effectiveness of task execution for mobile vehicles under high-speed conditions, this paper regards intelligent vehicles as edge nodes and establishes a dynamic offloading model in Vehicle-to-Vehicle (V2V) scenarios. A dynamic task offloading strategy based on optimal stopping theory is proposed to minimize the overall latency generated during the offloading process while ensuring the effectiveness of task execution. By analyzing the potential migration paths of tasks in V2V scenarios, we construct a dynamic migration model and design a migration benefit function, transforming the problem into an asset-selling problem in optimal stopping theory (OST). At the same time, it is proven that there exists an optimal stopping rule for the problem. Finally, the optimal migration threshold is determined by solving the optimal stopping rule through dynamic programming, guiding the task vehicle to choose the best target service vehicle. Comparisons between the proposed TMS-OST strategy and three other peer offloading strategies show that TMS-OST can significantly reduce the total offloading latency, select service vehicles with shorter distances using fewer detection attempts, guarantee service quality while lowering detection costs, and achieve high average offloading efficiency and average offloading distance efficiency. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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41 pages, 3181 KB

Open AccessArticle

Transmission-Path Selection with Joint Computation and Communication Resource Allocation in 6G MEC Networks with RIS and D2D Support

by Yao-Liang Chung

Future Internet 2025, 17(12), 565; https://doi.org/10.3390/fi17120565 - 6 Dec 2025

Cited by 2 | Viewed by 1192

Abstract

This paper proposes a transmission-path selection algorithm with joint computation and communication resource allocation for sixth-generation (6G) mobile edge computing (MEC) networks enhanced by helper-assisted device-to-device (D2D) communication and reconfigurable intelligent surfaces (RIS). The novelties of this work lie in the joint design of three key components: a helper-assisted D2D uplink scheme, a packet-partitioning cooperative MEC offloading mechanism, and RIS-assisted downlink transmission and deployment design. These components collectively enable diverse transmission paths under strict latency constraints, helping mitigate overload and reduce delay. To demonstrate its performance advantages, the proposed algorithm is compared with a baseline algorithm without helper-assisted D2D or RIS support, under two representative scheduling policies—modified maximum rate and modified proportional fair. Simulation results in single-base station (BS) and dual-BS environments show that the proposed algorithm consistently achieves a higher effective packet-delivery success percentage, defined as the fraction of packets whose total delay (uplink, MEC computation, and downlink) satisfies service-specific latency thresholds, and a lower average total delay, defined as the mean total delay of all successfully delivered packets, regardless of whether individual delays exceed their thresholds. Both metrics are evaluated separately for ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications services. These results indicate that the proposed algorithm provides solid performance and robustness in supporting diverse 6G services under stringent latency requirements across different scheduling policies and deployment scenarios. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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28 pages, 13653 KB

Open AccessArticle

Computation Offloading in Space–Air–Ground Integrated Networks for Diverse Task Requirements with Integrated Reliability Mechanisms

by Yitian Chen and Yinghua Tong

Future Internet 2025, 17(12), 542; https://doi.org/10.3390/fi17120542 - 27 Nov 2025

Cited by 2 | Viewed by 1198

Abstract

The sixth-generation (6G) system has been attracting increasing attention from both industry and academia, with the space–air–ground integrated network (SAGIN) identified as one of its key applications. This study investigates a SAGIN framework tailored for deployment in remote areas. To address the differing needs of users with emergency and routine tasks, an offloading strategy is proposed that enables direct offloading for emergency tasks and optimized UAV-assisted offloading for routine tasks. Additionally, considering the limited satellite coverage duration, a reliability mechanism for task offloading is designed. The study formulates a task offloading optimization problem aimed at maximizing the completion rate of routine tasks—while reducing their energy consumption and latency—under the premise of guaranteeing the completion of emergency task offloading. The problem is modeled as a Markov Decision Process (MDP). To solve it, a D-MAPPO reinforcement learning algorithm is proposed, which integrates the Dirichlet distribution with the Multi-Agent Proximal Policy Optimization (MAPPO) framework. Simulation results show that, compared with the MAPPO and PPO algorithms, the delay is reduced by 38% and 31%, respectively, while the energy consumption is reduced by 7% and 48%, respectively. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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19 pages, 1514 KB

Open AccessArticle

A UAV Trajectory Optimization and Task Offloading Strategy Based on Hybrid Metaheuristic Algorithm in Mobile Edge Computing

by Yeqiang Zheng, An Li, Yihu Wen and Gaocai Wang

Future Internet 2025, 17(7), 300; https://doi.org/10.3390/fi17070300 - 3 Jul 2025

Cited by 10 | Viewed by 1613

Abstract

In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable a UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper has significantly higher success than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of UAV. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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29 pages, 9734 KB

Open AccessArticle

Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8

by Momotaz Begum, Abm Kamrul Islam Riad, Abdullah Al Mamun, Thofazzol Hossen, Salah Uddin, Md Nurul Absur and Hossain Shahriar

Future Internet 2025, 17(6), 254; https://doi.org/10.3390/fi17060254 - 9 Jun 2025

Cited by 6 | Viewed by 3324

Abstract

Uneven roads pose significant challenges to vehicle stability, passenger comfort, and safety, especially in snowy and mountainous regions. These problems are often complex and challenging to resolve with traditional detection and stabilization methods. This paper presents a dual-method approach to improving vehicle stability by identifying road irregularities and dynamically adjusting the balance. The proposed solution combines YOLOv8 for real-time road anomaly detection with a GY-521 sensor to track the speed of servo motors, facilitating immediate stabilization. YOLOv8 achieves a peak precision of 0.99 at a confidence threshold of 1.0 rate in surface recognition, surpassing conventional sensor-based detection. The vehicle design is divided into two sections: an upper passenger seating area and a lower section that contains the engine and wheels. The GY-521 sensor is strategically placed to monitor road conditions, while the servomotor stabilizes the upper section, ensuring passenger comfort and reducing the risk of accidents. This setup maintains stability even on uneven terrain. Furthermore, the proposed solution significantly reduces collision risk, vehicle wear, and maintenance costs while improving operational efficiency. Its compatibility with various vehicles and capabilities makes it an excellent candidate for enhancing road safety and driving experience in challenging environments. In addition, this work marks a crucial step towards a safer, more sustainable, and more comfortable transportation system. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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25 pages, 5629 KB

Open AccessArticle

Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine

by Rene Francisco Santana-Cruz, Martin Moreno, Daniel Aguilar-Torres, Román Arturo Valverde-Domínguez and Rubén Vázquez-Medina

Future Internet 2025, 17(6), 250; https://doi.org/10.3390/fi17060250 - 31 May 2025

Cited by 1 | Viewed by 1919

Abstract

Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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27 pages, 1229 KB

Open AccessArticle

Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading

by Giuseppe Baruffa and Luca Rugini

Future Internet 2025, 17(1), 39; https://doi.org/10.3390/fi17010039 - 16 Jan 2025

Cited by 4 | Viewed by 1845

Abstract

This paper deals with the optimization of the operational efficiency of a fleet of mobile robots, assigned with delivery-like missions in complex outdoor scenarios. The robots, due to limited onboard computation resources, need to offload some complex computing tasks to an edge/cloud server, requiring artificial intelligence and high computation loads. The mobile robots also need reliable and efficient radio communication with the network hosting edge/cloud servers. The resource assignment aims at minimizing the total latency and delay caused by the use of radio links and computation nodes. This minimization is a nonlinear integer programming problem, with high complexity. In this paper, we present reduced-complexity algorithms that allow to jointly optimize the available radio and computation resources. The original problem is reformulated and simplified, so that it can be solved by also selfish and greedy algorithms. For comparison purposes, a genetic algorithm (GA) is used as the baseline for the proposed optimization techniques. Simulation results in several scenarios show that the proposed sequential minimization (SM) algorithm achieves an almost optimal solution with significantly reduced complexity with respect to GA. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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Review

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47 pages, 9271 KB

Open AccessReview

AI-Driven Mobility Management in 5G and 6G Wireless Networks: A Survey

by Hafiz M. Asif, Abdulraqeb Alhammadi, Naser Tarhuni and Mohammed M. Bait-Suwailam

Future Internet 2026, 18(8), 425; https://doi.org/10.3390/fi18080425 - 11 Aug 2026

Viewed by 501

Abstract

Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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